# Combining recurrent and residual learning for deforestation monitoring using
multitemporal SAR images

Carla Nascimento Neves<EMAIL_ADDRESS>Raul Queiroz Feitosa<EMAIL_ADDRESS>rio.br Mabel X. Ortega Adarme<EMAIL_ADDRESS>Gilson Antonio Giraldi
<EMAIL_ADDRESS>National Laboratory for Scientific Computing, Petropolis, RJ,
Brazil  Pontifical Catholic University of Rio de Janeiro, 22451-900 Rio de
Janeiro, Brazil

###### Abstract

With its vast expanse, exceeding that of Western Europe by twice, the Amazon
rainforest stands as the Earth’s largest forest, holding immense importance in
global climate regulation. Yet, deforestation detection from remote sensing
data in this region poses a critical challenge, often hindered by the
persistent cloud cover that obscures optical satellite data for much of the
year. Addressing this need, this paper proposes three deep-learning models
tailored for deforestation monitoring, utilizing SAR (Synthetic Aperture
Radar) multitemporal data moved by its independence on atmospheric conditions.
Specifically, the study proposes three novel recurrent fully convolutional
network architectures—namely, RRCNN-1, RRCNN-2, and RRCNN-3—crafted to enhance
the accuracy of deforestation detection. Additionally, this research explores
replacing a bitemporal with multitemporal SAR sequences, motivated by the
hypothesis that deforestation signs quickly fade in SAR images over time. A
comprehensive assessment of the proposed approaches was conducted using a
Sentinel-1 multitemporal sequence from a sample site in the Brazilian
rainforest. The experimental analysis confirmed that analyzing a sequence of
SAR images over an observation period can reveal deforestation spots
undetectable in a pair of images. Notably, experimental results underscored
the superiority of the multitemporal approach, yielding approximately a 5%
enhancement in F1-Score across all tested network architectures. Particularly
the RRCNN-1 achieved the highest accuracy and also boasted half the processing
time of its closest counterpart.

###### keywords:

Remote sensing , Deforestation detection , SAR images

##  1 Introduction

Remote sensing refers to acquiring information about an object from a remote
location. This term is frequently employed to describe the imaging of the
Earth’s surface from an elevated perspective, such as via satellite [Parelius,
2023]. Multitemporal remote sensing data can offer wide information for land
change monitoring [Shi et al., 2020, Ban & Yousif, 2016].

Change detection captures spatial differences in the state of an object by
observing it at different times [Singh, 1989]. In the Remote Sensing context,
its purpose is to monitor environmental changes by jointly processing a set of
images of the same geographical area acquired at different dates, which is
essential for the management of natural resources, the conservation of
ecosystems and biodiversity as well as decision support for sustainable
development [Asokan & Anitha, 2019].

Change detection using remote sensing imagery assumes a crucial function in
numerous fields of applications, including disaster monitoring [Zheng et al.,
2021b], biodiversity study [Newbold et al., 2015], desertification [Dawelbait
& Morari, 2012], urbanization process [Han et al., 2017] and deforestation
detection [De Bem et al., 2020], which is the focus of this research.

Among these applications, preserving the rainforests is critical in
maintaining the health and stability of our planet’s ecosystems. In
particular, the Amazon rainforest, the Earth’s largest forest, has suffered
increasing deforestation rates in recent years, with Brazil being the country
where the most significant statistics are concentrated [Giljum et al., 2022,
Amin et al., 2019].

The Brazilian government tracks deforestation in the Amazon region through
systematic satellite monitoring. For example, the Amazon Deforestation
Monitoring Project
(PRODES111http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes)
provides annual reports about deforestation in the Brazilian Legal Amazon
(BLA) since 1988 [Valeriano et al., 2004]. One notable limitation of PRODES
and several other deforestation monitoring systems is their dependency on
optical data. Optical data are frequently hindered by cloud cover throughout
most of the year in tropical regions [Doblas et al., 2020]

Various techniques have been addressed to create change maps for deforestation
detection, including simple differencing [Stauffer & McKinney, 1978], change
vector analysis [Perbet et al., 2019] and traditional machine learning
techniques like Support Vector Machine [Reis et al., 2020], Principal
Component Analysis [Sule & Wood, 2020], Random Forest [Hethcoat et al., 2020],
Maximum Likelihood [Diniz et al., 2022] and distance-based classifiers
[Nicolau et al., 2021].

Apart from these methods and their variations, numerous publications have
incorporated deep learning in remote sensing change detection and demonstrated
their superiority over conventional change detection methods Bai et al.
[2022].

Among the recent researches with deep learning solutions for change detection,
Autoencoders, U-Net and its variants [Li et al., 2022, Zheng et al., 2021a],
Recurrent Networks [Fang et al., 2023, Shi et al., 2022], Generative
Adversarial Networks [Zhao et al., 2020], and Transformer-based networks [Chen
et al., 2021, Wang et al., 2022] are included.

A current research line Panuju et al. [2020] focuses on combining different
techniques to improve change detection accuracy in remote sensing. Moreover,
Parelius [2023] points out that the growing abundance of satellite imagery
offers an opportunity to shift focus from the traditional bitemporal change
detection methods used in previous studies to models that exploit longer time-
series images. This approach allows for the incorporation of a richer set of
data for change detection, a perspective that has been relatively
underexplored until now, as emphasized in Parelius [2023].

The present study follows this trend and seeks to develop solutions for change
detection with remote sensing data, specifically for deforestation monitoring,
by employing multitemporal SAR data and combinations of different deep
learning techniques. This paper proposes three recurrent fully convolutional
deep networks for pixel-wise deforestation detection from SAR images.
Furthermore, this study assesses the application of image sequences instead of
bitemporal SAR image pairs for deforestation detection. This approach is
inspired by the hypothesis that deforestation signs in tropical forests tend
to rapidly diminish in SAR imagery due to the natural process of forest
regeneration. Consequently, deforestation detection becomes increasingly
challenging when bi-temporal image acquisition intervals are too widely apart.
By harnessing a sequence of multiple images captured between such intervals,
we substantially enhance the prospects of accurate and timely deforestation
detection.

The main contributions of this work are:

  * 1.

Development of novel deep learning based solutions for automatic deforestation
mapping and comparison with state-of-art methods.

  * 2.

Addressing underexplored aspects in change detection literature, including the
use of longer multitemporal data sequences.

  * 3.

Experimental analysis of the proposed methods using SAR data provided by
Sentinel-1 from a sample site of the Amazon forest.

This paper is organized as follows. Section 2 discusses the recent research
and also the gaps in change detection with deep learning. Section 3 provides
the theoretical background on the deep learning techniques that inspired the
architectures developed in the present work and Section 4 introduces the
proposed models. Section 5 presents the employed data, the experimental setup,
and the architectures used for comparison. Section 6 shows and discusses the
experimental results. Finally, Section 7 presents the main conclusions drawn
from this study.

##  2 Related Works

With the continuous advancement of Deep Learning methods within computer
vision, their application has been extended to the problem of change detection
in Remote Sensing due to their ability to capture complex and hierarchical
features present in the data [Parelius, 2023]. Hence, this section will
discuss recent works with deep learning solutions for change detection
problems.

The U-Net encoder-decoder architecture and its variants are commonly employed
for change detection tasks among the reviewed models. For instance, Zheng et
al. [2021a] introduced the Cross-Layer Convolutional Neural Network (CLNet),
which is a modified U-Net with Cross-Layer Blocks (CLB) incorporated to
integrate multi-scale features and multi-level contextual information by
temporarily splitting the input into two parallel asymmetrical branches using
different convolution strides and concatenating the feature maps from the two
branches. Experiments were conducted on two building change detection
datasets: the Learning Vision and Remote Sensing Laboratory building change
detection (LEVIR-CD) and WHU-CD, reaching superior performance compared to
several state-of-the-art methods.

Wang et al. [2022] proposed the U-Net-like Visual Transformer for Change
Detection (UVACD) for bitemporal image change detection. This network uses a
CNN backbone for extracting semantic information followed by a visual
transformer for feature enhancement that constructs change intensity tokens to
complete the temporal information interaction and suppress irrelevant
information weights to help obtain more distinguishable change features. The
experiments were conducted on the WHU-CD and LEVIR-CD datasets. The authors
reported a Precision of 94.58%, a Recall of 91.17%, an F1 score of 92.84%, and
an IoU of 86.64% on the WHU-CD. UVACD outperformed some previous state of the
art change detection methods in the experimental results.

U-Nets variants proposed in the literature take the form of a double-stream
architecture. One example is the Densely Attentive Refinement Network (DARNet)
introduced in Li et al. [2022] to improve change detection on bitemporal very-
high-resolution (VHR) remote sensing images. DARNet has a dense skip
connections module between the encoder–decoder architecture, which combines
features from various levels. A hybrid attention module is inserted at the
skip connections level to combine temporal, spatial, and channel attention.
Also, a recurrent refinement module is used to refine the predicted change in
the decoding process. The experimental results on the season-varying change
detection (SVCD) dataset, the Sun Yat-sen University change detection (SYSU-
CD) dataset, and the LEVIR-CD dataset outperformed state-of-the-art models.

Some deep learning-based models proposed for change detection include
Recurrent Neural Networks (RNN) due to their ability to handle related data
sequences. The sequence usually consists of images from two different time
points [Parelius, 2023].

Fang et al. [2023] proposed a fine-grained Multi-Functional Radar (MFR) model
followed by a multi-head attention-based bi-directional Long Short Term Memory
(LSTM) network to capture relationships between successive pulses. This
process uses the temporal features to predict the probability of each pulse
being a change point. The simulation results achieved better performance than
the compared convolutional and recurrent networks.

Shi et al. [2022] proposed a Multi-path Convolutional LSTM (MP-ConvLSTM) by
combining LSTM and a CNN for change detection with bi-temporal hyperspectral
images. A Siamese CNN was adopted to reduce the dimensionality of the images
and extract preliminary features. A Convolutional LSTM was used to learn
multi-level temporal dependencies among them. The MP-ConvLSTM was evaluated
using four publicly available hyperspectral datasets acquired by the Earth
Observing-1 (EO-1) Hyperion and obtained superior results than several state-
of-the-art change detection algorithms, also exhibiting better trade-off
between complexity and accuracy in general.

Papadomanolaki et al. [2019] presented a framework for urban change detection
that combines a fully convolutional network (FCN) similar to U-Net for feature
representation and a recurrent network for temporal modeling. The U-Net-based
encoder–decoder architecture has a convolutional LSTM block added at all
levels of the encoder. The authors evaluated the performance of this network
using an ensemble cross-validation strategy on bi-temporal data from Onera
Satellite Change Detection (OSCD) Sentinel-2 dataset (SAR data). The
U-Net+LSTM model outperformed the regular U-Net.

Strategies using residual learning [He et al., 2016] to facilitate gradient
convergence are also applied with FCN approaches for change detection since
such a combination helps obtain a more comprehensive range of information
[Khelifi & Mignotte, 2020, Shafique et al., 2022].

Basavaraju et al. [2022] introduced UCDNet, the Urban Change Detection
Network. This model is based on an encoder-decoder architecture, using a
version of spatial pyramid pooling (SPP) blocks for extracting multiscale
features and residual connections for introducing additional maps of feature
differences between the streams at each level of the encoder to improve change
localization, aiming to acquire better predictions while preserving the shape
of changed areas. UCDNet uses a proposed loss function, a combination of
weighted class categorical cross-entropy (WCCE) and modified Kappa loss. The
authors evaluated the network on bi-temporal multispectral Sentinel-2
satellite images from Onera Satellite Change Detection (OSCD), obtaining
better results than the models used for comparison..

The Multiscale Residual Siamese Network fusing Integrated Residual Attention
(IRA-MRSNet), proposed by Ling et al. [2022], introduced multi-resolution
blocks that combine convolutions with kernels of different sizes to extract
deep semantic information and features at multiple scales. In addition, this
network utilizes an attention unit connecting the encoder and the decoder. In
experiments conducted on Seasonal Change Detection Dataset (CDD) the network
outperformed the counterpart methods.

Peng et al. [2019] presented an improved U-Net++ design where change maps
could be learned from scratch using available annotated datasets. The authors
adopted the U-Net++ model with dense skip connections as the backbone for
learning multiscale feature maps from several semantic layers. Residual blocks
are employed in the convolution unit, aiming for better convergence. Deep
supervision is implemented by using multiple side-output fusion (MSOF) to
combine change maps from different semantic levels, generating a final change
map. They used the weighted binary cross-entropy loss and the dice coefficient
loss to mitigate the impact of class imbalance. The performance of the
proposed CD method was verified on a VHR satellite image dataset and acheived
a superior performance than the related methods.

Recent studies proposed change detection techniques by combining residual and
recurrent learnings. For instance, Khankeshizadeh et al. [2022] presented
FCD‐R2U‐Net, a forest change detection that includes a module for producing an
enhanced forest fused difference image (EFFDI), to achieve a more efficient
distinction of changes, followed by (Recurrent Residual U-Net) R2U-Net,
applied to segment the EFFDI into the changed and unchanged areas.
Experimental results were conducted on four bi-temporal images acquired by the
Sentinel 2 and Landsat 8 satellite sensors. The qualitative and quantitative
results demonstrated the effectiveness of the proposed EFFDI in reflecting the
true forest changes from the background. Regarding the qualitative results,
forest changes and their geometrical details were better preserved by
FCD‐R2U‐Net, compared with U-Net, ResU-Net, and U-Net++. The proposed network
also obtained superior results in the quantitative analysis.

Moustafa et al. [2021] proposed a change detection architecture named
Attention Residual Recurrent U-Net (Att R2U-Net), inspired by R2U-Net and
attention U-Net. This study supports the notion that deep neural networks can
learn complex features and improve change detection performance when combined
with hyperspectral data. Three hyperspectral change detection datasets with
class imbalance and small regions of interest were employed to evaluate the
performance of the proposed method for binary and multiclass change cases. The
results were compared with U-Net, ResU-Net, R2U-Net, and Attention U-Net. Att
R2U-Net outperformed the counterpart methods in almost all metrics and cases.

As observed in the previously mentioned related works, in addition to
developing new algorithms, combinations of available techniques are being
considered to improve the accuracy of change detection in remote sensing. This
is a research focus pointed out by Panuju et al. [2020].

##  3 Deep Learning Approaches Background

This section presents the deep learning approaches explored for constructing
the change detection frameworks proposed in this study. The architectures are
rooted in recurrent residual learning.

###  3.1 Residual Networks

To enhance the training process of deep convolutional neural networks (CNNs),
Residual Networks (ResNets) were conceived based on the observation that as
neural networks grow deeper, they typically encounter elevated training
errors, particularly when the network’s depth becomes substantially large.

He et al. [2016] introduced the so called residual blocks (see1 equipped with
skip connections or shortcuts. In a residual block, the input to a layer is
combined with the output of that layer, allowing the network to pass through
information without significant alteration directly. As gradients
backpropagate during training, they flow nearly unaltered through these skip
connections, improving the convergence at the earlier layers.

Figure 1: Residual block (RB). Source: Adapted from He et al. [2016].

He et al. [2016] defines de building block as

$\textrm{{y}}=\mathcal{F}(\textrm{{x}},\\{\mathcal{W}_{i}\\})+\textrm{{x}}\textrm{
,}$ (1)

where x and y are the input and output of the layers considered and the
function $\mathcal{F}(\textrm{{x}},\\{\mathcal{W}_{i}\\})$ denotes the
residual mapping with learnable weights assembled in $\mathcal{W}_{i}$. The
example in Figure 1 has two layers, so $\mathcal{F}=W_{2}$
$\sigma(W_{1}\textrm{{x}})$, with $\sigma$ representing the rectified linear
unit (ReLU) activation function [Nair & Hinton, 2010]. Biases are omitted for
simplifying notations. It also seen in Figure 1 that the operation
$\mathcal{F}+\textrm{{x}}$ is carried out by a shortcut connection and
element-wise addition. These shortcut connections introduce no additional
parameters and negligible computational complexity.

Inspired by the U-Net architecture and deep residual learning, Zhang et al.
[2018] proposed the Deep Residual U-Net (ResU-Net). ResU-Net has three parts:
The encoder consists of Residual Blocks (RB) and down-sampling operations that
compress the input into compact representations. The bottleneck connects the
encoder with the last part, the decoder, which comprises bilinear upsampling
operations and recovers the representations to a pixel-wise categorization. In
addition, skip connections link the first and last part so that information
from the encoding layers is preserved and transmitted to the decoding layers.

According to Zhang et al. [2018], the deep residual units make the deep
network easy to train, and the skip connections within a residual unit and
between the corresponding levels of the network will facilitate information
propagation without degradation, making it possible to design a deep neural
network with fewer parameters.

As the present work approaches change detection, the decoder output feeds a
softmax operator that delivers the posterior class probabilities for change or
no change at each pixel location. The following figures show U-Net (Figure
2(a)) and ResU-Net (Figure 2(c)) variants used for deforestation detection in
[Ortega et al., 2021]. The residual blocks (Figure 2(b)) facilitate the flow
of information through the network layers, allowing the capture of relevant
features of the satellite image [He et al., 2016], which is crucial for change
detection.

(a) U-Net Architecture

(b) Residual Block (RB)

(c) ResU-Net Architecture

Figure 2: U-Net and ResU-Net Architectures being used for a change detection
scheme. Legend: C (Convolution), MP (Max-pooling), RB (Residual Block), US
(Up-sampling).

###  3.2 Recurrent Networks

Recurrent Neural Networks (RNNs) are designed to process sequential data,
updating their internal state at each time step $t$ while storing relevant
information from prior steps [Rumelhart et al., 1986]. This enables RNNs to
share weights across time steps, capturing temporal patterns Goodfellow et al.
[2016], Graves [2013]. However, the ability to retain information over long
sequences is limited in traditional RNNs due to vanishing or exploding
gradients during back-propagation [Calin, 2020]. The Long Short-term Memory
(LSTM) [Hochreiter & Schmidhuber, 1997] networks were developed to address
such limitations of conventional RNNs. They incorporate gating mechanisms that
selectively allow information to flow nearly unaltered through them, allowing
the capture of long-range dependencies in sequential data.

The present work employs the Convolutional LSTM Network (ConvLSTM). A ConvLSTM
unit (See Figure 3) consists of inputs $X_{t}$, a cell state $C_{t}$, a hidden
state $H_{t}$ along with an input gate $i_{t}$, an output gate $o_{t}$ and a
forget gate $f_{t}$ to control information flow. Due to the introduction of
the convolutional structure, all the states, inputs, and intermediary outputs
are three-dimensional tensors where the first two dimensions are spatial (rows
and columns), and the last dimension learns feature representations [Shi et
al., 2022]. The input $X_{t}$ and past states $C_{t-1}$, $H_{t-1}$ are
employed to determine the future states $C_{t}$ and $H_{t}$.

Figure 3: Inner structure of ConvLSTM Source: Adapted fromShi et al. [2022]

The central equations are described in the sequence, with ’$*$’ denoting the
convolutional operator, ’$\circ$’ the Hadamard product, $W$ the coefficient
matrix, $\sigma$ the sigmoid function and $b$ the bias vector.

$\displaystyle i_{t}$ $\displaystyle=\sigma(W_{xi}\ast X_{t}+W_{hi}\ast
H_{t-1}+W_{ci}\circ C_{t-1}+b_{i})$ (2) $\displaystyle f_{t}$
$\displaystyle=\sigma(W_{xf}\ast X_{t}+W_{hf}\ast H_{t-1}+W_{cf}\circ
C_{t-1}+b_{f})$ (3) $\displaystyle C_{t}$ $\displaystyle=f_{t}\circ
C_{t-1}+i_{t}\circ\tanh(W_{xc}\ast X_{t}+W_{hc}\ast H_{t-1}+b_{c})$ (4)
$\displaystyle o_{t}$ $\displaystyle=\sigma(W_{xo}\ast X_{t}+W_{ho}\ast
H_{t-1}+W_{co}\circ C_{t}+b_{o})$ (5) $\displaystyle H_{t}$
$\displaystyle=o_{t}\circ\tanh(C_{t})$ (6)

According to Shi et al. [2022], the focus when using ConvLSTM for a change
detection task is on capturing short-term temporal dependencies that
accentuate bands capable of detecting changes while attenuating bands with
less informative content. ConvLSTM recognizes and analyzes the multitemporal
changes in image sequences by capturing temporal dependencies and
incorporating temporal features into the change detection process.
Consequently, the hidden states $H_{t}$ of the ConvLSTM output can be
extracted as representative features of changes.

Considering this ability, convLSTM has been used in applications involving
sequential images, such as detection of changes in hyperspectral images [Shi
et al., 2022], detection of urban changes [Papadomanolaki et al., 2019] and
deforestation detection [Masolele et al., 2021].

###  3.3 Recurrent Residual Networks

According to Yue et al. [2018], residual learning and shortcut connections can
effectively mitigate the exploding and vanishing gradient issues in long-term
backpropagation. Bringing together the residual learning described in Section
3.1 and the recurrent learning presented in Section 3.2, Recurrent Residual
Neural Networks have been proposed in the literature. Some examples include
the Hybrid Residual LSTM (HRL) used for sequence classification [Wang & Tian,
2016], R2U++ [Mubashar et al., 2022] and the Deep Recurrent U-Net (DRU-Net)
[Kou et al., 2019] proposed for medical image segmentation.

A particular recurrent residual network called R2U-Net [Alom et al., 2019]
takes the U-Net architecture and the residual blocks of ResU-Net (Figure 2) as
a starting point and includes the recurrent learning. This model combines
recurrent convolutional operations with the residual blocks in Recurrent
Residual Convolutional Units (RRCU - Figure 4(a)) to replace the regular
convolutional layers in the U-Net. Each RRCU has two Recurrent Convolutional
Layers (RCL), and the input of the residual block is added to the output of
the second RCL unit.

The unfolded RCL (Figure 4(b)) for $t$ time steps is a feed-forward sub-
network of depth $t+1$. The figure exemplifies an RCL with $t=2$, referring to
the recurrent convolutional operation that includes one single convolution
layer followed by two sub-sequential recurrent convolutional layers.

(a) RRCU

(b) Unfolded Recurrent Convolutional Layer (RCL) with $t=2$

Figure 4: Recurrent Residual Convolutional Unit (RRCU)

The following mathematical explanation of an RCL unit is adapted from Liang &
Hu [2015]. For a unit located at $(i,j)$ on the $k$-th feature map in an RCL,
the net input, $z_{ijk}(t)$ at a step $t$, is formulated as:

$z_{ijk}(t)=(\textrm{{w}}^{f}_{k})^{T}\textrm{{x}}^{f(i,j)}(t)+(\textrm{{w}}^{r}_{k})^{T}\textrm{{x}}^{r(i,j)}(t-1)+b_{k}\textrm{,}$
(7)

where x${}^{f(i,j)}(t)$ and x${}^{r(i,j)}(t-1)$ represents the feedforward and
recurrent input, respectively. They correspond to the vectorized patches
centered at $(i,j)$ of the feature maps in the current and previous layer. The
terms w${}^{f}_{k}$ and w${}^{r}_{k}$ represent the vectorized feed-forward
weights and recurrent weights, respectively, and $b_{k}$ is the bias. The
first term in Eq. 7 is used in standard CNN and the recurrent connections
induce the second term. The activity or state of this unit is a function of
$z_{ijk}(t)$, where $\sigma$ is the ReLU:

$\sigma(z_{ijk}(t))=\textrm{max}(z_{ijk}(t),0)$ (8)

The RRCU proposed by Alom et al. [2019] uses the RCL in a Residual Block, as
shown in Figure 4(a). Considering $x_{l}$ as the input in the $l^{th}$ layer
of an RRCU, the output of this unit, $x_{l+1}$, can be calculated as:

$x_{l+1}=x_{l}+F(x_{l},w_{l})\textrm{,}$ (9)

where $F(x_{l},w_{l})$ is the output of the last RCL, expressed as

$F(x_{l},w_{l})=\sigma(z^{l}_{ijk}(t))=\textrm{max}(z^{l}_{ijk}(t),0)\textrm{.}$
(10)

Figure 5 shows a R2U-Net architecture with three encoding and decoding levels
and a bottleneck between these stages, employing RRCUs.

Figure 5: R2U-Net Architecture for change detection. Legend: MP (Max-pooling),
TC (Transpose Convolution), RRCU (Recurrent Residual Convolutional Unit)

Recurrent residual architectures derived from R2U-Net, including FCD‐R2U‐Net
[Khankeshizadeh et al., 2022] and Att R2U-Net [Moustafa et al., 2021] have
been used for change detection tasks. Alom et al. [2019] and Kou et al. [2019]
state that the inclusion of RCLs in residual units further enhances the
ability to handle deeper architectures. Also, the process of collecting and
combining information from different time-steps in a recurrent neural network
allows the model to capture dependencies and patterns over longer sequences of
data. This feature accumulation process leads to more comprehensive feature
representations and helps the model extract very low-level features from the
data, which are crucial for segmentation tasks across various modalities.

##  4 Proposed Architectures

This section presents the deep learning solutions for deforestation monitoring
proposed in this work, drawing on the principles of recurrent residual
learning discussed in Section 3.3. In this way, in addition to the ability to
learn in multiple layers and the mitigation of the problem of vanishing
gradients provided by the residual blocks, there will be benefits from the
temporal dependency modeling provided by the recurrent layers. The first
proposal, illustrated in Figure 5, consists of an adaptation of R2U-Net, using
the RRCUs in the encoder and in the bottleneck and the replacement of these
units in the decoder by transposed convolutions (TC), as shown in Figure 6.
The result is a network with fewer parameters.

Figure 6: Modified R2U-Net Architecture: RRCNN-1

The second proposal relies on a new recurrent residual block, the Residual
Convolutional LSTM block (RCLSTM), that consists of convolutional LSTM blocks
with a ReLU activation instead of the RCL used in R2U-Net’s RRCU. Figure 7
highlights the differences between the RCLSTM block proposed here and a
typical residual block (Figure 7(a)) and an RRCU block (Figure 7(b)), already
described in Section 3.3.

(a) Typical Residual Block

(b) RRCU

(c) RCLSTM (proposed)

Figure 7: Different variants of residual and recurrent residual convolutional
units.

The second proposal derives from the RRCNN-1, replacing the RRCUs by the
RCLSTM block in the encoder and bottleneck. Figure 8 depicts the proposed
recurrent residual network hereafter called RRCNN-2.

Figure 8: RRCNN-2 Architecture.

The third proposed architecture proposed in this work, named RRCNN-3, is a
kind of variant of the prior architecture resulting from replacing the RCLSTM
block in the bottleneck with a singular convolutional LSTM block, as depicted
in Figure 9.

Figure 9: RRCNN-3 Architecture.

##  5 Experimental Analysis

This section presents the datasets and the experimental setup employed in this
study. The deep learning methods presented in Section 3 were used for
comparison purposes with the architectures proposed in Section 4.

###  5.1 Dataset

This study employed Sentinel-1 data from a site in the Brazilian Legal Amazon
in the Pará state that extends over $115\times 186$ Km². The site is
characterized by mixed land cover, mainly dense evergreen forests and pastures
(see Figure 10).

Figure 10: Geographical location of the study site in the Pará state, Brazil.

To build the input data for the deep learning models described in Section 3
and Section 4, seven images from this same geographical site with resolution
of $9327\times 5767\times 2$ (width $\times$ height $\times$ polarizations -
VV and VH) were captured with a periodicity of approximately two months,
starting in August 2019 (Figure 11(a)) and ending in August 2020 (Figure
11(b)).

Figure 11: The SAR images: (a) Initial date; (b) Final date; (c) Ground truth
of the deforestation that occurred in the period. Legend -  gray: past
deforestation (1988-2018);  red: deforestation (2019-2020);  blue: no-
deforestation.

The reference map of the deforestation that occurred in this period is
available on the INPE website222http://terrabrasilis.dpi.inpe.br/ (Figure
11(c)). It is worth mentioning that this dataset is highly unbalanced, with
only 1.06% of the pixels belonging to the deforestation class, 34.04%
corresponding to past-deforestation class, and 64.9 % to the no-deforestation
class.

The input consists of a tensor $\mathbf{I}\in\mathbf{R}^{H\times W\times 2D}$
resulting from stacking $D$ multitemporal SAR images with 2 polarizations
along the third dimension. In our experiments, we explored two scenarios:
$D=2$ to represent the conventional bitemporal set and $D=7$ to represent an
extended multitemporal sequence. Figure 12 shows this process for the
bitemporal case.

Figure 12: Model Input construction employed for the current experiments.
Bitemporal example for data acquired from the same sensor.

Each input tensor was splited into 60 tiles of $932\times 961$ pixels. A
cross-validation strategy with six folds was adopted during the training. Each
tile was part of the test set only once. Then, the final prediction was a
mosaic of all test tiles covering the whole image.

During training and validation the network receives as input tensor patches of
$128\times 128$ pixels cropped from the training tiles, with maximum overlap
of 70% allowed.

###  5.2 Networks configuration and hyuperparameters

The experimental analysis reported in the next section compares the results
obtained with the approaches discussed in Section 3, which serve as baselines,
with the models proposed in Section 4. Table 1 shows the architectures
evaluated in our experiments.

Table 1: Networks Architectures

Architectures | Encoder | Bottleneck | Decoder | Output
---|---|---|---|---
| MP(C(3×3,32)) | C(3×3,128) | US(C(3×3,128)) |
U-Net | MP(C(3×3,64)) | C(3×3,128) | US(C(3×3,64)) | Softmax (C(1×1, #Classes))
| MP(C(3×3,128)) | C(3×3,128) | US(C(3×3,32)) |
| MP(RB(3×3,32)) | RB(3×3,128) | US(C(3×3,128)) |
ResU-Net | MP(RB(3×3,64)) | RB(3×3,128) | US(C(3×3,64)) | Softmax (C(1×1, #Classes))
| MP(RB(3×3,128)) | RB(3×3,128) | US(C(3×3,32)) |
| MP(RRCU(3×3,32)) |  | US(RRCU(3×3,128)) |
R2U-Net | MP(RRCU(3×3,64)) | RRCU(3×3,128) | US(RRCU(3×3,64)) | Softmax (C(1×1, #Classes))
| MP(RRCU(3×3,128)) |  | US(RRCU(3×3,32)) |
| MP(RRCU(3×3,32)) |  | TC(3×3,128) |
RRCNN-1 | MP(RRCU(3×3,64)) | RRCU(3×3,128) | TC(3×3,64) | Softmax (C(1×1, #Classes))
| MP(RRCU(3×3,128)) |  | TC(3×3,32) |
| MP(RCLSTM(3×3,32)) |  | TC(3×3,128) |
RRCNN-2 | MP(RCLSTM(3×3,64)) | RCLSTM(3×3,128) | TC(3×3,64) | Softmax (C(1×1, #Classes))
| MP(RCLSTM(3×3,128)) |  | TC(3×3,32) |
| MP(RCLSTM(3×3,32)) |  | TC(3×3,128) |
RRCNN-3 | MP(RCLSTM(3×3,64)) | Conv. LSTM(3×3,128) | TC(3×3,64) | Softmax (C(1×1, #Classes))
| MP(RCLSTM(3×3,128)) |  | TC(3×3,32) |

The parametrization is (Kernel Height x Kernel Width, Number of filters).
Symbols: C (Convolution), MP (Max-pooling), RB (Residual Block), US (Up-
sampling), TC (Transpose Convolution), RRCU (Recurrent Residual Convolutional
Unit), RCLSTM (Residual Convolutional LSTM block).

The employed parameter setup follows: batch size equal to $32$, Adam optimizer
with learning rate equal to $1e^{-3}$, and $\beta$ equal to $0.9$, and, to
avoid over-fitting, an early stopping strategy with patience equal to $10$.

Considering that the dataset is highly unbalanced, the emplowed loss function
was the weighted categorical cross entropy, given by Ho & Wookey [2019]:

$\textrm{Loss}_{wcce}=-\frac{1}{M}\sum^{K}_{k=1}\sum^{M}_{m=1}w_{k}\cdot
y_{m}^{k}\cdot\textrm{log}(\hat{y}_{m}^{k})\textrm{,}$ (11)

where $M$ is the number of training pixels, $K$ is the number of classes,
$w_{k}$ is the weight for class $k$, $y_{m}^{k}$ is the target label for
training example $m$ for class $k$, $x_{m}$ is the input for training example
$m$ and $\hat{y}_{m}^{k}$ refers to the predicted probability for training
example $m$ for class $k$.

In the present case, adopted the weights $0.2$ for class no-deforestation and
$0.8$ for class deforestation. Following the PRODES methodology, the past
deforestation class was ignored during training, validation, and testing. Only
patches having at least 2% of pixels of the deforestation class were used for
training. In addition, a data augmentation procedure was applied for training
and validation; these operations included rotation (multiples of 90º) and
flipping (horizontal, vertical) transformations. The threshold to separate the
deforestation and no-deforestation classes was 50%.

###  5.3 Evaluation metrics

The generated deforestation maps classify each pixel into categories of
deforestation and no-deforestation. Designating deforestation as a “positive”
and no-deforestation as “negative”, there are four possible outcomes: true
positive (TP) being a correctly identified deforestation/positive, true
negative (TN) being a correctly identified no-deforestation, false positive
(FP) being an unchanged pixel labeled as deforestation and false negative (FN)
a changed pixel labeled as no-deforestation.

Among the several performance metrics that have been used to evaluate the
results of a deforestation detection process, three of the most common are
Precision, Recall, and F1-Score, as can be seen in the studies mentioned in
Section2 and also in review articles about change detection with remote
sensing data [Parelius, 2023, Shafique et al., 2022].

The Precision metric denotes the ratio between the number of correctly
classified deforestation pixels and the total number of pixels identified as
deforestation.

$\textrm{Precision}=\frac{TP}{TP+FP}.$ (12)

The Recall metric, conversely, is equivalent to the true positive rate,
representing the ratio of accurately classified deforestation pixels to the
total number of original deforestation pixels.

$\textrm{Recall}=\frac{TP}{TP+FN}.$ (13)

With Recall and Precision, the F1-Score is calculated as follows:

$\textrm{F1-Score}=\frac{2\cdot\textrm{Precision}\cdot\textrm{Recall}}{\textrm{Precision}+\textrm{Recall}}.$
(14)

###  5.4 Computational Resources

The preliminary experiments were conducted on the following system
configuration:

  * 1.

Processor: Thirty-Two-Core AMD Ryzen™ Threadripper™ PRO 5975WX Processor -
3.60GHz 128MB L3 Cache (280W)

  * 2.

Memory: 8 x 64GB PC4-25600 3200MHz DDR4 ECC RDIMM (512GB total)

  * 3.

GPU Accelerators: 3 x NVIDIA® RTX A6000 - 48GB GDDR6 - PCIe 4.0 x16

  * 4.

Operating System: Ubuntu 22.04.2 LTS

The use of all GPUs available on the machine for training was enabled by the
Mirrored Strategy (tf.distribute.MirroredStrategy333Available on
https://www.tensorflow.org/api$\\_$docs/python/tf/distribute/MirroredStrategy)
function of the TensorFlow deep learning framework. This data parallelism
approach is intended to accelerate the training process by allowing a deep
learning model to be replicated across multiple GPUs, where each GPU retains a
full copy of the model. During training, each replica processes a portion of
the training data, and then gradient updates are synchronized between GPUs to
update the global model. According to Pang et al. [2020], using this function
further accelerates training and allows for larger models by leveraging memory
from multiple GPUs.

##  6 Experimental Results

In this section, we delve into the findings derived from our experimental
analysis to evaluate the performance of the deforestation detection
architectures outlined in Section 4. Figures 13 through 15 showcase the
performance metrics of the methods introduced in this study, alongside those
referenced in Section 3, which serve as our baseline for comparison. These
figures provide a comprehensive assessment of accuracy, encompassing
Precision, Recall, and F1-score, derived from experiments on both bitemporal
and multitemporal input data. The reported scores have been derived using
K-fold cross-validation with a value of $K=6$, following the methodology
detailed in Section 5.2.

Figure 13: Comparison of methods in terms of Precision for bitemporal and
multitemporal data Figure 14: Comparison of methods in terms of Recall for
bitemporal and multitemporal data Figure 15: Comparison of methods in terms of
F1-Score for bitemporal and multitemporal data

The first finding that emerges from the analysis of the figures is that all
performance metrics derived from multi-temporal data consistently outperformed
those obtained from bi-temporal data. Notably, the only exception was the
Precision for RRCNN-2, which declined by a small amount for the multitemporal
data. This observation corroborates the hypothesis that signs of change in SAR
images get less apparent with time. Consequently, using a sequence rather than
a mere pair of SAR images improves the chance of capturing changes that occur
during the target observation interval.

The second conclusion from our experiments is that recurrent variants, namely
the R2U-Net and the three RRCNN variants, consistently outperformed their
strictly convolutional counterparts, namely U-Net and ResU-Net. This trend was
apparent in all three metrics we examined.

As for the three proposed variants, the RRCNN-1 consistently outperformed the
other two, with RRCNN-2 holding a slight edge over RRCNN-3. Remarkably,
RRCNN-1 showcased the best results among all the architectures we examined,
surpassing the top-performing baseline, the R2U-Net, by 2% in F1-Score.

Another aspect deserving of examination is the computational efficiency.
Figure 16 provides insight into the count of trainable parameters associated
with each scrutinized configuration.

Figure 16: Number of Parameters of each model for the bitemporal and
multitemporal data

By looking at each bar group in Figure 16, one observes that adopting a
sequence of images instead of a mere image pair had a marginal impact on the
parameter count for each model. It is also observed that the RRCNN-2 and
RRCNN-3 were the variants with the largest parameter count, followed closely
by R2U-Net.

Interestingly RRCNN-1 stands out by carrying approximately half the number of
parameters when contrasted with R2U-Net, bringing it close to the parameter
count of ResU-Net. This implies that RRCNN-1 successfully incorporates
recurrence into its model framework with minimal alterations to the overall
parameter load compared to ResU-Net, a fully convolutional network.

Figure 17 and Figure 18 show the training and inference times. By and large,
as expected, these Figures show a profile similar to those of the parameter
values.

Figure 17: Training Time of each model for bitemporal and multitemporal data
Figure 18: Inference Time of each model for bitemporal and multitemporal data

As observed in Figures 13 through 15 together with Figures 17 \- 18, RRCNN-1
very clearly presented the best compromise between accuracy and computational
efficiency. Among the variants analyzed, RRCNN-1 presented the best accuracy,
and, on the other hand, processing times, particularly the inference time,
were close to that of the fastest network, the U-Net.

To evaluate the deforestation maps generated in the experiments, Figure 19
illustrates the change maps produced in each experiment in snips of the target
site. Each row refers to a different architecture, and each column shows the
results for a different snip obtained with the bitemporal and multitemporal
inputs.

Figure 19: Predicted deforestation maps in two snips from the test set. Legend
-  past deforestation;  deforestation (true positives);  no deforestation
(true negatives);  false positives;  false negatives.

As observed in Figure 19 the false positive spots are minimal, being noticed
only in minor regions, mainly in maps generated with bitemporal input (Figure
19-(g), (k), (o), (s) and (w)) and even smaller spots in Figure 19-(p) and
(v), that represent maps generated with a multitemporal input.

Regarding false negative spots, their decrease is noticed in the maps
generated with multitemporal inputs in relation to bitemporal inputs,
confirming the quantitative results. The change map generated by the RRCNN-1
with the multitemporal input was the closest to the ground-truth, as can be
seen in Figure 19(p). The other developed architectures, RRCNN-2 and RRCNN-3,
also delivered well defined maps.

##  7 Conclusions and Future Work

The present research seeks for developing solutions for deforestation
monitoring using deep learning aproaches. Until the present stage of this
investigation, three change detection architectures relying on recurrent
residual learning have been formulated, RRCNN-1, RRCNN-2 and RRCNN-3. These
methods were compared with three techniques from the literature, U-Net, ResU-
Net and R2U-Net, the latter being a residual recurrent network.

Preliminary experiments were conducted using Sentinel-1 SAR images
corresponding to a region of the Brazilian Amazon rainforest. The ground-truth
used in this work was collected from the PRODES Project, which was developed
by the National Institute for Space Research (INPE). The performance of the
techniques was compared using bitemporal data, which are usually emphasized in
the literature reports, and also multitemporal data.

RRCNN-1 presented the best performance in most metrics, achieving a F1-Score
of 66,5% with the bitemporal input and 71,6% with the multitemporal input.
RRCNN-2 had the best Precision (97,1%), the second best F1-Score (65,2%) and
the third best Recall (49,0%) with the bitemporal input and the second best
Recall and F1-Score with the multitemporal input, 55,9% and 70,8%,
respectively. RRCNN-3 achieved the second best Recall (49,2%) and Precision
(95,9%) in the bitemporal case and the third best Precision (97,2%) with the
multitemporal input.

Based on the assessed metrics and the change maps generated through the tested
networks, it became evident that the incorporation of an extended sequence of
images significantly enhanced the deforestation detection performance,
highlighting the potential benefits of incorporating a comprehensive longer
temporal context in the analysis. RRCNN-1, particularly, delivered significant
improved results with a multitemporal input compared to the bitemporal case,
while incurring a training time increase of nearly 2 minutes.

The RRCNN-2 and RRCNN-3 networks are designed with ConvLSTM layers in their
architectures, which led to longer training and inference times. This is
attributed to the inherently high computational complexity associated with
LSTM operations, resulting in more demanding computational resources during
the learning process.

The next steps for this research include tests with other datasets commonly
employed in deforestation detection surveys.

## Acknowledgments

The authors would like to thank the financial support provided by CNPq, CAPES
and FAPERJ.

## References

  * Alom et al. [2019] Alom, M. Z., Yakopcic, C., Hasan, M., Taha, T. M., & Asari, V. K. (2019).  Recurrent residual u-net for medical image segmentation.  Journal of Medical Imaging, 6, 014006–014006.
  * Amin et al. [2019] Amin, A., Choumert-Nkolo, J., Combes, J.-L., Motel, P. C., Kéré, E. N., Ongono-Olinga, J.-G., & Schwartz, S. (2019).  Neighborhood effects in the brazilian amazônia: Protected areas and deforestation.  Journal of Environmental Economics and Management, 93, 272–288.
  * Asokan & Anitha [2019] Asokan, A., & Anitha, J. (2019).  Change detection techniques for remote sensing applications: A survey.  Earth Science Informatics, 12, 143–160.
  * Bai et al. [2022] Bai, T., Wang, L., Yin, D., Sun, K., Chen, Y., Li, W., & Li, D. (2022).  Deep learning for change detection in remote sensing: a review.  Geo-spatial Information Science, (pp. 1–27).
  * Ban & Yousif [2016] Ban, Y., & Yousif, O. (2016).  Change detection techniques: A review.  Multitemporal remote sensing: methods and applications, (pp. 19–43).
  * Basavaraju et al. [2022] Basavaraju, K., Sravya, N., Lal, S., Nalini, J., Reddy, C. S., & Dell’Acqua, F. (2022).  Ucdnet: A deep learning model for urban change detection from bi-temporal multispectral sentinel-2 satellite images.  IEEE Transactions on Geoscience and Remote Sensing, 60, 1–10.
  * Calin [2020] Calin, O. (2020).  Deep Learning Architectures.  Cham, Switzerland: Springer Nature.
  * Chen et al. [2021] Chen, H., Qi, Z., & Shi, Z. (2021).  Remote sensing image change detection with transformers.  IEEE Transactions on Geoscience and Remote Sensing, 60, 1–14.
  * Dawelbait & Morari [2012] Dawelbait, M., & Morari, F. (2012).  Monitoring desertification in a savannah region in sudan using landsat images and spectral mixture analysis.  Journal of Arid Environments, 80, 45–55.
  * De Bem et al. [2020] De Bem, P. P., de Carvalho Junior, O. A., Fontes Guimarães, R., & Trancoso Gomes, R. A. (2020).  Change detection of deforestation in the brazilian amazon using landsat data and convolutional neural networks.  Remote Sensing, 12, 901.
  * Diniz et al. [2022] Diniz, J. M. F. d. S., Gama, F. F., & Adami, M. (2022).  Evaluation of polarimetry and interferometry of sentinel-1a sar data for land use and land cover of the brazilian amazon region.  Geocarto International, 37, 1482–1500.
  * Doblas et al. [2020] Doblas, J., Shimabukuro, Y., Sant’Anna, S., Carneiro, A., Aragão, L., & Almeida, C. (2020).  Optimizing near real-time detection of deforestation on tropical rainforests using sentinel-1 data.  Remote Sensing, 12, 3922.
  * Fang et al. [2023] Fang, Y., Zhai, Q., Zhang, Z., & Yang, J. (2023).  Change point detection for fine-grained mfr work modes with multi-head attention-based bi-lstm network.  Sensors, 23, 3326.
  * Giljum et al. [2022] Giljum, S., Maus, V., Kuschnig, N., Luckeneder, S., Tost, M., Sonter, L. J., & Bebbington, A. J. (2022).  A pantropical assessment of deforestation caused by industrial mining.  Proceedings of the National Academy of Sciences, 119, e2118273119.
  * Goodfellow et al. [2016] Goodfellow, I., Bengio, Y., & Courville, A. (2016).  Deep learning.  MIT press.
  * Graves [2013] Graves, A. (2013).  Generating sequences with recurrent neural networks.  arXiv preprint arXiv:1308.0850, .
  * Han et al. [2017] Han, X., Zhong, Y., Cao, L., & Zhang, L. (2017).  Pre-trained alexnet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification.  Remote Sensing, 9, 848.
  * He et al. [2016] He, K., Zhang, X., Ren, S., & Sun, J. (2016).  Deep residual learning for image recognition.  In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).
  * Hethcoat et al. [2020] Hethcoat, M. G., Carreiras, J., Edwards, D., Bryant, R. G., & Quegan, S. (2020).  Detecting tropical selective logging with sar data requires a time series approach.  bioRxiv, (pp. 2020–03).
  * Ho & Wookey [2019] Ho, Y., & Wookey, S. (2019).  The real-world-weight cross-entropy loss function: Modeling the costs of mislabeling.  IEEE access, 8, 4806–4813.
  * Hochreiter & Schmidhuber [1997] Hochreiter, S., & Schmidhuber, J. (1997).  Long short-term memory.  Neural computation, 9, 1735–1780.
  * Khankeshizadeh et al. [2022] Khankeshizadeh, E., Mohammadzadeh, A., Moghimi, A., & Mohsenifar, A. (2022).  Fcd-r2u-net: Forest change detection in bi-temporal satellite images using the recurrent residual-based u-net.  Earth Science Informatics, 15, 2335–2347.
  * Khelifi & Mignotte [2020] Khelifi, L., & Mignotte, M. (2020).  Deep learning for change detection in remote sensing images: Comprehensive review and meta-analysis.  Ieee Access, 8, 126385–126400.
  * Kou et al. [2019] Kou, C., Li, W., Liang, W., Yu, Z., & Hao, J. (2019).  Microaneurysms segmentation with a u-net based on recurrent residual convolutional neural network.  Journal of Medical Imaging, 6, 025008–025008.
  * Li et al. [2022] Li, Z., Yan, C., Sun, Y., & Xin, Q. (2022).  A densely attentive refinement network for change detection based on very-high-resolution bitemporal remote sensing images.  IEEE Transactions on Geoscience and Remote Sensing, 60, 1–18.
  * Liang & Hu [2015] Liang, M., & Hu, X. (2015).  Recurrent convolutional neural network for object recognition.  In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3367–3375).
  * Ling et al. [2022] Ling, J., Hu, L., Cheng, L., Chen, M., & Yang, X. (2022).  Ira-mrsnet: A network model for change detection in high-resolution remote sensing images.  Remote Sensing, 14, 5598.
  * Masolele et al. [2021] Masolele, R. N., De Sy, V., Herold, M., Marcos, D., Verbesselt, J., Gieseke, F., Mullissa, A. G., & Martius, C. (2021).  Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using landsat time series.  Remote Sensing of Environment, 264, 112600.
  * Moustafa et al. [2021] Moustafa, M. S., Mohamed, S. A., Ahmed, S., & Nasr, A. H. (2021).  Hyperspectral change detection based on modification of unet neural networks.  Journal of Applied Remote Sensing, 15, 028505–028505.
  * Mubashar et al. [2022] Mubashar, M., Ali, H., Grönlund, C., & Azmat, S. (2022).  R2u++: a multiscale recurrent residual u-net with dense skip connections for medical image segmentation.  Neural Computing and Applications, 34, 17723–17739.
  * Nair & Hinton [2010] Nair, V., & Hinton, G. E. (2010).  Rectified linear units improve restricted boltzmann machines.  In Proceedings of the 27th international conference on machine learning (ICML-10) (pp. 807–814).
  * Newbold et al. [2015] Newbold, T., Hudson, L. N., Hill, S. L., Contu, S., Lysenko, I., Senior, R. A., Börger, L., Bennett, D. J., Choimes, A., Collen, B. et al. (2015).  Global effects of land use on local terrestrial biodiversity.  Nature, 520, 45–50.
  * Nicolau et al. [2021] Nicolau, A. P., Flores-Anderson, A., Griffin, R., Herndon, K., & Meyer, F. J. (2021).  Assessing sar c-band data to effectively distinguish modified land uses in a heavily disturbed amazon forest.  International Journal of Applied Earth Observation and Geoinformation, 94, 102214.
  * Ortega et al. [2021] Ortega, M. X., Feitosa, R. Q., Bermudez, J. D., Happ, P. N., & De Almeida, C. A. (2021).  Comparison of optical and sar data for deforestation mapping in the amazon rainforest with fully convolutional networks.  In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 3769–3772).  IEEE.
  * Pang et al. [2020] Pang, B., Nijkamp, E., & Wu, Y. N. (2020).  Deep learning with tensorflow: A review.  Journal of Educational and Behavioral Statistics, 45, 227–248.
  * Panuju et al. [2020] Panuju, D. R., Paull, D. J., & Griffin, A. L. (2020).  Change detection techniques based on multispectral images for investigating land cover dynamics.  Remote Sensing, 12, 1781.
  * Papadomanolaki et al. [2019] Papadomanolaki, M., Verma, S., Vakalopoulou, M., Gupta, S., & Karantzalos, K. (2019).  Detecting urban changes with recurrent neural networks from multitemporal sentinel-2 data.  In IGARSS 2019-2019 IEEE international geoscience and remote sensing symposium (pp. 214–217).  IEEE.
  * Parelius [2023] Parelius, E. J. (2023).  A review of deep-learning methods for change detection in multispectral remote sensing images.  Remote Sensing, 15, 2092.
  * Peng et al. [2019] Peng, D., Zhang, Y., & Guan, H. (2019).  End-to-end change detection for high resolution satellite images using improved unet++.  Remote Sensing, 11, 1382.
  * Perbet et al. [2019] Perbet, P., Fortin, M., Ville, A., & Béland, M. (2019).  Near real-time deforestation detection in malaysia and indonesia using change vector analysis with three sensors.  International Journal of Remote Sensing, 40, 7439–7458.
  * Reis et al. [2020] Reis, M. S., Dutra, L. V., Sant’Anna, S. J. S., & Escada, M. I. S. (2020).  Multi-source change detection with palsar data in the southern of pará state in the brazilian amazon.  International Journal of Applied Earth Observation and Geoinformation, 84, 101945.
  * Rumelhart et al. [1986] Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986).  Learning representations by back-propagating errors.  nature, 323, 533–536.
  * Shafique et al. [2022] Shafique, A., Cao, G., Khan, Z., Asad, M., & Aslam, M. (2022).  Deep learning-based change detection in remote sensing images: A review.  Remote Sensing, 14, 871.
  * Shi et al. [2022] Shi, C., Zhang, Z., Zhang, W., Zhang, C., & Xu, Q. (2022).  Learning multiscale temporal–spatial–spectral features via a multipath convolutional lstm neural network for change detection with hyperspectral images.  IEEE Transactions on Geoscience and Remote Sensing, 60, 1–16.
  * Shi et al. [2020] Shi, W., Zhang, M., Zhang, R., Chen, S., & Zhan, Z. (2020).  Change detection based on artificial intelligence: State-of-the-art and challenges.  Remote Sensing, 12, 1688.
  * Singh [1989] Singh, A. (1989).  Review article digital change detection techniques using remotely-sensed data.  International journal of remote sensing, 10, 989–1003.
  * Stauffer & McKinney [1978] Stauffer, M., & McKinney, R. (1978).  Landsat image differencing as an automated land cover change detection technique.  Technical Report.
  * Sule & Wood [2020] Sule, S. D., & Wood, A. (2020).  Application of principal component analysis to remote sensing data for deforestation monitoring.  In Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII (pp. 17–29).  SPIE volume 11528.
  * Valeriano et al. [2004] Valeriano, D. M., Mello, E. M., Moreira, J. C., Shimabukuro, Y. E., Duarte, V., Souza, I., Santos, J., Barbosa, C. C., & Souza, R. (2004).  Monitoring tropical forest from space: the prodes digital project.  International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 35, 272–274.
  * Wang et al. [2022] Wang, G., Li, B., Zhang, T., & Zhang, S. (2022).  A network combining a transformer and a convolutional neural network for remote sensing image change detection.  Remote Sensing, 14, 2228.
  * Wang & Tian [2016] Wang, Y., & Tian, F. (2016).  Recurrent residual learning for sequence classification.  In Proceedings of the 2016 conference on empirical methods in natural language processing (pp. 938–943).
  * Yue et al. [2018] Yue, B., Fu, J., & Liang, J. (2018).  Residual recurrent neural networks for learning sequential representations.  Information, 9, 56.
  * Zhang et al. [2018] Zhang, Z., Liu, Q., & Wang, Y. (2018).  Road extraction by deep residual u-net.  IEEE Geoscience and Remote Sensing Letters, 15, 749–753.
  * Zhao et al. [2020] Zhao, W., Chen, X., Ge, X., & Chen, J. (2020).  Using adversarial network for multiple change detection in bitemporal remote sensing imagery.  IEEE Geoscience and Remote Sensing Letters, 19, 1–5.
  * Zheng et al. [2021a] Zheng, Z., Wan, Y., Zhang, Y., Xiang, S., Peng, D., & Zhang, B. (2021a).  Clnet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery.  ISPRS Journal of Photogrammetry and Remote Sensing, 175, 247–267.
  * Zheng et al. [2021b] Zheng, Z., Zhong, Y., Wang, J., Ma, A., & Zhang, L. (2021b).  Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters.  Remote Sensing of Environment, 265, 112636.