<p>There is this paper : <a href="https://arxiv.org/pdf/1810.09111.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1810.09111.pdf</a><br>
Its look like what you want to do !</p>

<p>In the paper they output a Heatmap of the changes between the two images, but you could just detect if there is activation or certain amount of activations to output your binary classification.</p>

<p>Hope that can help you !</p>
Binary output for Change Detection using Convolutional Neural Networks
<p>I am not really sure this is the right place to ask for this kind of question. I am looking for some recommendations for a solution to a specific problem.</p>

<p>I am struggling to find a solution for my university project.
Let's say I have 2 images taken at the same location but at different times. I need to build a model that detects if there is any change between these 2 images.</p>

<p>This is somehow similar to the foreground segmentation/background subtraction/scene change detection problems whose plenty of research works was deployed (For <a href="https://www.researchgate.net/publication/330695424_ChangeNet_A_Deep_Learning_Architecture_for_Visual_Change_Detection_Subvolume_B" rel="nofollow noreferrer">reference</a>):
<a href="https://i.stack.imgur.com/JZ92A.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/JZ92A.png" alt="enter image description here"></a></p>

<p>However, the scope of those problems is far beyond what I want to do. They extract features by CNN from original images and then concatenate extracted features to a greyscale output image by Deconvolutional techniques. For me, I just want to extract features and eventually output a binary (1 or 0) value: 1 if there is any change between 2 images, 0 if there is not. In other words, I don't want to implement the Deconvolutional part.</p>

<p>I have 2 problems:</p>

<ol>
<li>The CD2014 dataset (and also other relevant datasets) have only
ground truths in greyscale images format, not my desired output
(binary 1/0 format). </li>
<li>Furthermore, since my problem is different from
those papers, I cannot find a suitable model to use, I tend to use
VGG-16 but there is no proof that it will work in my problem.</li>
</ol>

<p>Can you suggest me any solutions or materials in this scope?
I truly appreciate any recommendations.</p>

<p>Thank you and have a nice day!</p>