Satellite Image Classification Methods and Landsat 5TM Bands

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Satellite Image Classification Methods and Landsat  5TM Bands Powered By Docstoc
					                                                           (IJCSIS) International Journal of Computer Science and Information Security,
                                                           Vol. 11, No. 6, June 2013

              METHODS and LANDSAT 5TM BANDS

            Jamshid Tamouk                                    Nasser Lotfi                                 Mina Farmanbar
Department of Computer Engineering              Department of Computer Engineering                Department of Computer Engineering
          EMU University                                 EMU University                                    EMU University
      Famagusta, North Cyprus                         Famagusta, North Cyprus                          Famagusta, North Cyprus

Abstract—This paper attempts to find the most accurate                             TABLE 1: DESCRIPTION OF BANDS IN LANDSAT 5 TM [11]
classification method among parallelepiped, minimum distance
and chain methods. Moreover, this study also challenges to find               Band     Wavelength(µm)          Spectral     Resolution (m)
the suitable combination of bands, which can lead to better                      1          0.45 – 0.52      Blue-Green                 30
results in case combinations of bands occur. After comparing                     2          0.52 – 0.60           Green                 30
these three methods, the chain method over perform the other                     3          0.63 – 0.69            Red                  30
methods with 79% overall accuracy. Hence, it is more accurate                    4          0.76 – 0.90         Near IR                 30
than minimum distance with 67% and parallelepiped with                           5          1.55 – 1.75         Mid-IR                  30
65%. On the other hand, based on bands features, and also by                     6       10.40 – 12.50       Thermal IR                120
                                                                                 7          2.08 – 2.35         Mid-IR                  30
combining several researchers' findings, a table was created
which includes the main objects on the land and the suitable
combination of the bands for accurately detecting of landcover            Methods of classification mainly follow two approaches,
objects. During this process, it was observed that band 4 (out of         namely supervised and unsupervised classification.
7 bands of Landsat 5TM) is the band, which can be used for                Supervised classification is the classification that needs to
increasing the accuracy of the combined bands in detecting                interact with user (i.e. training the system) who has
objects on the land.                                                      knowledge (ground truth) about that area before image
                                                                          processing. However, in unsupervised classification, it is not
Keywords: parallelepiped, minimum distance, chain method,
                                                                          necessary to have high knowledge about areas and it does
classification, Landsat 5TM, satellite band
                                                                          not need to train the system. System starts grouping the
                                                                          pixels, which are similar in brightness value into unique
                         I.    INTRODUCTION                               clusters. Then after finishing clustering, the user will start to
                                                                          label each of the groups (classes).
  The focus of this paper is solely on some satellite image               The following two tables, which are resulted from some of
classification methods for land and also Landsat 5TM bands                the classification methods' comparison introduce by
and the suitable combination of them to have higher                       different researchers. According Table 2 (Hosseini et. al),
accurate results during classification.                                   overall accuracy of minimum distance (73.77%) is much
                                                                          better than parallelepiped method (34.27%) maximum
A. Classification methods                                                 likelihood method (with 85.83% overall accuracy) provides
  The basic principle of the classification is classifying of             a higher accuracy than minimum distance.
images based on placing pixels with similar brightness value
into the same group. It is done by selecting limited area or                  TABLE 2: ACCURACY OF DIFFERENT CLASSIFICATION METHODS AND
instance from images and then to assign the label (i.e. name)                                      ALGORITHMS [6]
and color to that area. The images of the satellite (Landsat                    Supervised Classification Method    Overall Accuracy %
5TM) which are used in this paper, has 7 bands for                                    Paralleleped                     34.27
capturing the image of the earth. Each of these bands uses                         Minimum Distance                    73.77
different wavelength for capturing the images, in a way that                      Maximum Likelihood                   85.83
it causes to have 7 images from the same area but with                    Table 3 is the part of comparison table of classification
different characteristics. The Landsat 5TM bands                          methods, which is given by Todd [12]. According to this
descriptions are shown in the Table 1:                                    table, maximum likelihood method with 90.2% accuracy is

                                                                                                      ISSN 1947-5500
                                                         (IJCSIS) International Journal of Computer Science and Information Security,
                                                         Vol. 11, No. 6, June 2013

a more accurate supervised classification in comparing with               TABLE 4: OIF INDEX OF DIFFERENT BANDS COMBINATION BY ASCENDING
                                                                                                     ORDER [13]
minimum distance (with 75.5% accurate) and
parallelepiped (with 87.1% accurate).


                                                                                            IOF index

                                                                                                                                       IOF index



                   CLASSIFICATION METHODS[12]

       Method           Estimated process time   Accuracy %
 Maximum Likelihood         18 min                  90.2                  123            12.832          1          134             22.605              11
  Minimum Distance          15 min                  75.5                  127            16.739          2          157             22.724              12
    parallelepiped          15 min                  87.1                  124            17.229          3          357             22.840              13
     ISODATA               2.25 min                 90.6                  237            18.043          4          245             23.918              14
                                                                          125            18.359          5          145             24.316              15
In comparing accuracy of maximum likelihood (supervised                   137            19.160          6          247             24.858              16
classification) with ISODATA method (unsupervised                         135            19.693          7          147             25.724              17
classification) according to the above table, we can see that             235            20.596          8          457             27.442              18
their accuracies are approximately same. Here can see that                257            21.314          9          347             29.209              19
                                                                          234            22.169          10         345             29.230              20
opposite the previous table (table 1.2) parallelepiped is
much accurate than minimum distance.                                    In Table 5, which made by Hobson [5] all possible
Based on the accuracy tables for supervised classification in           combination of bands (35 set of combined bands) in three-
the above tables (researches), maximum likelihood                       band combination has been examined which the best
classification method is the most accurate method                       combination of the bands belongs to the bands 4, 5, 6 with
comparing with parallelepiped and minimum distance to                   57.3673 OIF. According to the below table it can be
mean methods and about parallelepiped and minimum                       understood that from the best 10 tree-band combination out
distance there are different ideas. Some of researchers’                of 35, eight of these combination include band 4 in their
finding like Hashemi el. al [4], Oruc el. al [9] and Todd [12]          combination. It means 80% of 10 best three-band
shows parallelepiped is more accurate than minimum                      combinations (145, 457, 167, 246, 347,146, 346, 356, 467,
distance but the others findings like Lim el. al [7] and                and 456) have band 4 in their combination.
Hosseini el. al [6] show the opposite of that.
                                                                                          TABLE 5: OIF OF 35 COMBINED BANDS [5]
B. 1.2 Suitable combination of the bands for land covers
                                                                             Bans                                      Bans
                                                                                                        OIF                                         OIF
                                                                         Combination                               Combination
  The other issue is about the way of combining the                      123                12.6385                237                        16.1890
different bands of satellite for achieving a good result. In             124                18.9822                245                        27.8149
other words, for having more accurate result from                        125                20.2492                246                        35.9016
classification of satellite images we should consider the                126                15.6910                247                        23.4820
                                                                         127                15.0502                256                        28.8717
selection of the suitable or correct combination of bands
                                                                         134                22.7656                257                        22.9567
(according to the object or objects on the land, which we                135                22.8736                267                        20.3396
want to classify). According to the following formula, “[n!              136                18.8254                345                        31.3270
/ (r! (n-r)!)]”, 7 bands with three-band combination can                 137                17.5892                346                        42.0967
have, 35 kinds of combination of bands. Below is the table               145                31.9984                347                        39.9820
of the combination of different bands and the corresponding              146                40.1405                356                        44.0859
                                                                         147                26.9859                357                        30.0630
"OIF Index" values, where OIF is used to show how useful                 156                29.5769                367                        24.4388
is the combination of the bands, based on the correlations of            157                24.5532                456                        57.3673
them:                                                                    167                35.7702                457                        33.7486
Table 4 below, which made by Wenbo el. al [13] for 20                    234                19.5080                467                        46.6954
selected bands combinations in to ascending order. In this               235                21.0872                567                        31.8615
table we can find that, OIF index of the combination of                 According to other researchers' findings mentioned below
bands TM 3, 4, 5 (ETM+3, 4, 5) is the highest OIF index.                and after finding the best bands combination for
In this table from best 10 three-band combinations out of 20            classification of land covers, now we want to see which
three-band combinations, 8 of them include band 4 which                 combination of bands are suitable for each of the main
gives highest number of occurrence in the combinations. It              objects (such as water, vegetation, soil, snow and ice, sand
means 80% of 10 best three-band combinations (134, 157,                 and so on ) on the land.
357, 245, 145, 247, 147, 457, 347, and 345) have band 4 in              In order to recognize the water on land surface or
their combination.                                                      distinguish between land and water (coastal and see) a
                                                                        combination of band2 and band5 can be used as below:
                                                                        The ratio band2/band5 is greater than one for water and less
                                                                        than one for land in large areas of coastal zone. With this
                                                                        method, water and land can be separated directly.

                                                                                                          ISSN 1947-5500
                                                         (IJCSIS) International Journal of Computer Science and Information Security,
                                                         Vol. 11, No. 6, June 2013

 Another method is to use the band ratio between band 4                   Band 4  Band 3 
and band 2 [1]. So, if the result of band2/band5 become                                         (1  L)
greater than one (band2/band5>1) then the object will be                  Band 4  Band 3  1 
water; otherwise, it will be land or any other objects. Also
in the following combination of bands is used for                       L depends on Land cover, but 0.5 is a suggested value for
calculating the water index and classifying the images                  many land cover conditions
based on water and non-water by Hosseini el. al [6]:                    According to Hashemi el. al [4] research finding for
Water Index = (Band 1 + Band 2 + Band 3) / (Band 4 +                    discriminating or distinguishing between salt and sodium
Band 5 + Band 7) [4]. That is the ratio of visible spectrum             soils, use of Landsat 5TM bands (2,4,6) and 7 are more
bands to be reflected by infrared bands.                                accurate than other combination of the bands.
The other important object on the land is vegetation or the             Also according to the Hashemi el. al [4] finding the ratio
area with vegetative cover. According to the Shan long el.              spectral (TM3 - TM4) / (TM2 - TM4), show the best
al [10] finding, the best combination of bands for detecting            correlation with the Soil EC data. It means that by
or recognizing the vegetation is combination of band4 and               combination of the band2, band3 and band4 we can
band3.                                                                  distinguish different kinds of soil from each other and other
NDVI = (Band 4 - Band 3) / (Band 4 + Band 3) [10]                       objects.
 This combination of bands 3 and 4 is called "normalized                The other objects on surface of the land, are snow and ice
difference vegetative index" (NDVI). In above formula the               (i.e. glacier area) which based on the Todd research finding
bands which are used are NIR = Reflectance in Near                      [12] can be detected or recognized by using the
Infrared Band (band 4) and RED = Reflectance in the RED                 combination of bands 4 and 5 (ice or snow index = band4 /
Band (band 3). It is often used in detection of small                   band5), bands 3 and 5 (ice or snow index = band3 / band5),
differences between vegetation classes and sometimes used               or bands 3, 4 and 5.
for distinguishing vegetative area from other areas or                   “Glacier extent mapping from satellite data has been the
objects. In these bands, vegetation and soil contrast is at its         focus of many recent research papers. Bayr et al. (1994)
maximum. It means soil and vegetation can be easily                     used a threshold of a ratio image of TM 4 to TM 5 bands to
differentiated from each other.                                         delineate glacier area, while Rott (1994) used a threshold of
The NDVI (Normalized Difference Vegetative Index)                       a TM 3 to TM 5 ratio image. Paul (2000) found that the TM
values in the range of -1.0 to 1.0, where Vegetated areas               4 to TM 5 ratio technique yielded the best results for
will typically have values greater than zero and Negative               glacier mapping on Gries Glacier, especially in regions
values indicate non-vegetated surface features such as water,           with low insolation” [12].
barren, ice, snow, or clouds [10].                                      "Also using the visible-red channel (TM 3) and two of the
So, if NDVI is greater than zero (if NDVI > 0) then that                infrared channels (TM 4 and 5) allowed for an excellent
area is the vegetative cover area; otherwise, it is land (other         distinction of the ice cap. This is because snow and ice have
objects). Following is the other vegetative index formulas,             very high spectral reflectance in the visible-red (RED) and
which are discussed by Muzein [8]:                                      the near-infrared (NIR) wavelength regions and very low
Corrected Naturalized Differential Vegetation Index                     spectral reflectance’s in the middle-infrared (MIR)
                                                                        wavelength region" [12].
                           Band 5  Band 5MIN 
                                                                        Based on the above discussion, the best combination for
 Band 4  Band 3  
                   1                       
                                                                        distinction of ice and snow is combination of bands 3, 4 and
 Band 4  Band 3   Band 5MAX  Band 5MIN                        5.
                                                                        Below there are some other combinations of the bands
Percent Vegetation Cover:
                                                                        which are, the findings of several researchers. Based on the
  Standardiz ed   NVDI
                                                                        result of the below researches, combination of bands 3, 4, 7
                                                                        is good for detecting the water boundary or costal, soil
              Band 4 
Simple Ratio                                                          moisture and iron compounds. Bands 4,3, 2 are used for
              Band 3                                                  vegetation and crop analysis, bands 4, 5, 3 for soil moisture
Reduced Simple Ratio:                                                   and vegetation analysis, bands 3, 2, 1 for landcover and
 Band 4        Band 5  Band 5MIN                                 underwater features, bands 7, 4, 3 and bands 7, 4 , 2 for
          1                                                       change detection, soil type and vegetation stress. [2, 3 and 7]
 Band 3   Band 5MAX  Band 5MIN 
                                                                                            II.     PROBLEM DEFINITION
Soil Adjusted Vegetation Index: Minimizes the secondary                 Researchers depending on their application should be able
backscattering effect of canopy-transmitted soil background             to choose suitable method for classification of their images.
reflect radiation. It describes both vegetation cover and soil          Obviously, it is difficult for the researchers who are new in
background.                                                             this field. On the other hand, most of the researchers who
                                                                        use Landsat images in their research need to know more
                                                                        about the Landsat 5TM bands and usage of each band. In

                                                                                                    ISSN 1947-5500
                                                                (IJCSIS) International Journal of Computer Science and Information Security,
                                                                Vol. 11, No. 6, June 2013

addition, they should know which bands and which                                  parallelepiped and minimum distance approximately are
combination of the bands are good for detecting different                         similar to my research finding such as Table 2 but some
kinds of objects on the land. Therefore, they will be able to                     others are exactly opposite such as Table 3. The reasons for
get a good result if they are able to choose suitable methods                     this could be one of the following reasons: lack of enough
and also suitable bands or combination of the bands for                           data for training or testing, samples distribution, difficulty
their research.                                                                   with selecting sufficient training data for supervised
                                                                                  methods or the insufficient skill of the trainers.
                    III.    PROPOSED METHOD
                                                                                         TABLE 7: USER, PRODUCER AND OVERALL ACCURACY OF
By using Landsat 5TM images as the input data (captured                                 PARALLELEPIPED, MINIMUM DISTANCE AND CHAIN METHOD
from north of Iran) and with training the system by the
information collected about that area (ground truth) for


supervised methods and without training the system for



unsupervised method achieved to the ability to classify
satellite images and also calculate the accuracy of each
methods. Then the classification methods (supervised and
                                                                                                        User accuracy                    68%            72%                   50%              71%
unsupervised classification) are compared based on the                               Paralleled
determined accuracy. On the other hand, Table 8 is created                                            Producer accuracy                  92%            62%                   54%              50%
based on the bands’ features to illustrate the main objects                                           Overall accuracy               [(43+31+27+23)/192]*100=65%
on the land and the suitable combinations of bands for                                                  User accuracy                    77%            67%                   62%              60%
recognizing them. According to this table and by checking                                             Producer accuracy                  91%            67%                   53%              60%
the other research findings on bands and band combinations,                                           Overall accuracy               [(39+32+33+24)/192]*100=67%
it becomes possible to find the most effective band among 7                                             User accuracy                    86%            77%                   73%              80%
bands of Landsat 5TM.                                                                  Chain
                                                                                                      Producer accuracy                  92%            67%                   73%              89%
                                                                                                      Overall accuracy               [(45+41+32+34)/192]*100=79%
                  IV.      PERFORMANCE ANALYSIS
Accuracy is calculated by dividing the number of                                  Below is the table of some important objects and the
object/class’s pixels correctly classified; over the total                        suitable combination of the bands for detecting those
number of pixel belong to that object/class. In other word,                       objects based on the literature given in section B.
Accuracy= number of pixels assigned to the correct class /
number of pixels that actually belong to that class or object.                       TABLE 8: COMBINATION OF BANDS BASED ON THE TYPE OF OBJECT
If this calculation is done for all of objects/classes together,                                               Bands
the result will call Overall accuracy. Table 6 shows the
confusion matrix of minimum distance method, fund for                                                                                                    Combination of the
                                                                                      Objects              1   2 3           4 5 6             7
                                                                                                                                                         bands & Conditions
this paper (It is a table that shows the correct and incorrect                                                 *                 *                      TM 2/TM 5>1
number of assigned objects to each class. It is used for                                                       *             *                          TM 2/TM 4>1
computation of accuracy).                                                                                                                                 Index=(TM 1+TM
                                                                                                           *   * *           * *               *         2+TM 3)/(TM 4+TM
          TABLE 6: ERROR MATRIX TABLE FOR MINIMUM DISTANCE TO MEAN                                                                                            5+TM 7)
                                                                                                               * *           *                              Water appears dark
               Class types determined from reference source                                Coastal               *           *                 *
Class type form

            #Plots    Water Forest Grassland          Mountain       Totals                                                                               (TM 4-TM 3)/(TM
                                        Agriculture and soil                                                             *   *
                                                                                                                                                              4+TM 3)>0
             Water      39        0              8           4         52               Vegetation
                                                                                                               * *           *                            False color infrared
             Forest       0      32             12           4         48                                        *           * *                         Vegetation conditions
       Grassland &        4       8             33           8         52                                                    * *                          Index=TM 4/TM 5
        Agriculture                                                                    Snow and Ice                      *     *                          Index=TM 3/TM 5
         Mountain         0       8              8          24         40                                                *   * *
No ground truth         43       48             61          40        192                                                *   *                 *
pixels                                                                                    Soil type
                                                                                                               *             *                 *
                                                                                                               *             *   *             *
                                                                                     Salt and Sodium
                  V.    RESULT AND DISCUSSION                                               Soil               * *           *
                                                                                                                                                        (TM 3-TM 4)/(TM 2-
Table 7 is the accuracy table which is created based on the                               Iron
parallelepiped, minimum distance and chain methods error                                                                 *   *                 *              Such as ilmenite
matrix tables gained for this paper. According to this table,                          Soil moisture                     *   *                 *           Best combination
the chain method (with 79% accuracy) has highest accuracy                               differences                      *   * *
in comparing with two others mentioned methods in this                                    Change                         *   *                 *
paper. Also it is found that minimum distance (with 67%                                 detection,
accuracy) has higher accuracy than parallelepiped (with 65%                           disturbed soils          *             *                 *
                                                                                     vegetation stress
accuracy). Some of researchers’ findings about accuracy of

                                                                                                                   ISSN 1947-5500
                                                                  (IJCSIS) International Journal of Computer Science and Information Security,
                                                                  Vol. 11, No. 6, June 2013

Table 8 shows the important object on the land and the best                       [9] Oruc, M., Marangoz, A. M and Buyuksalih, G. (2004). Comparison of
                                                                                      pixel based and object oriented classification approaches using
combination of the bands for detecting them. It is important
                                                                                      Landsat 7ETM spectral bands.
to know that which combination of the bands can detects
which kind of object on the land with more accuracy. In                           [10] Shan-long, L., Xiao-hua, S and Le-jun. Z. (2006). Land cover change
                                                                                      in Ningbo and its surrounding area of Zhejiang Province. Journal of
Table 8, by carefully looking at this table, we can find that                         Zhejiang University SCIENCE A ISSN 1009-3095 (Print); ISSN 1862-
band 4 is used in all of the objects in most of the combined                          1775
bands (around 90% of the combined bands). It can also be
                                                                                  [11] Short, N. M. Technical and historical perspectives of remote sensing.
observed in Table 4 that from the best 10 three-band                                  [online]     viewed      (December       2012),      Available     at:
combinations out of 20, eight of them (i.e. 80% of ten best                 
three-band combinations) include band 4 in their                                  [12] Todd H. A. (2002). Evaluation of remote sensing techniques for ice
combinations (134, 157, 357, 245, 145, 247, 147, 457, 347,                            area classification applied to the tropical quelccaya icecap, Peru, Polar
and 345). The same result is approximately shown in Table 5.                          Geography, 2002, 26, No. 3.
In this table from the best 10 three-band combinations out of                     [13] Wenbo,W., Jing, Y and Tingjun, K. (2008). Study of remote sensing
35, eight of these combinations include band 4 in their                               image fusion and its application in image classification, The
combinations (145, 457, 167, 246, 347,146, 346, 356, 467,                             International Archives of the Photogrammetry. Remote Sensing and
and 456) which is 80% of 10 best three-band combinations.                             Spatial Information Sciences. Vol. XXXVII.
Therefore, these findings confirm that band 4 is the most
useful band to increase the accuracy of combined bands in
detecting the objects on the land.

                        VI.     CONCLUSION
According to this paper, the proposed chain method with 79%
accuracy is more accurate than the other two compared
methods. In addition, Table 8, which identifies the suitable
band combinations for each of the main objects on the land,
was created. Finally, after analyzing the findings of this
paper and some other researchers, similarly it is concluded
that, having band 4 (Near Infrared) in the combinations of
the bands can improve the accuracy of detection and
classification of the objects noticeably.

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    research in Technische Universität Dresden.

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