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Digital Image Processing 2
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Texture analysis using differnce statistics for land cover classification
Data Description
Land sat tm data and spot HRV data were used in this study the test site contains urban residential areas forests paddy golf fields course and marches tm data and HRV data were resample to 30 m and 20 m respectively test data in this study is as follows.
| |
Land sat |
SPOT HRV data |
| Band |
: 1-7 |
: 1-3 ( multi spectral ) |
| Path Row |
: 107-35 |
: 331-279 |
| Date |
: 24 July 1987 |
: 7, April 1986. |
| Processing level |
: Bulk |
: 1 B ( bulk) |
Determination of optimum parameters for land cover classsificastion
Optimum parameters of differences statistics was determined by the following procedures.
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Collection of ground truth data human interpretation of the image with help of existing land use map and topographic maps.
- Comparison parameters of difference statistics.
The following three parameters were investigated.
Inter sample spacing distance : from 1 to 10 pixels
band : Land sat -TM -1-7 ( except bands 6), SPOT HRV =1-3
feature : A.S.M., Contrast, Mean, Entropy
- Selection of optimum parameters.
Land cover classification
The study area was classified by maximum likelihood method using multi spectral data and textual data which is derived by the determined optimum parameters of difference statistics land cover categories were forest paddy field urban open space water and golf course we compared classification accuracy in the cas3e of using multi spectral data and textual data with the case of only multi spectral data .
Fig.2 Flow of processing.
Results of comparison difference statistics
- Intersample Spacing Distance
Fig. 3 shows density function of each inter sample spacing distance of forest by SPOT-HRV band 2.
The distribution is almost the same with inter sample distance exeeds 4 pixels our formar study in mountain area used land sat tm data same result.
- Band
Fig. 4 shows mean and plus or minus standard deviation of entropy of four categories for each spot hrv band in the case of thar inter sample spacing distance is 5 pixels.
Band 3 SPOT-HRV data is compartively better than at the other bands for discrimniation of four categories bt our formarstudy lqand sat band 4 is ther best foer classfication wave length of land sat tm band 4 is 760 to 900 nm and spot hrv band 3 is 790- 890 nm both bands have almost the samewave length region larger tahn the value of urban at band 3 however this relation is reverse at band 1 nad 2 that means textures are different in differnce band image this effedct tells us the use of multi spectral. texture analysis.
- Features
Four Features of SPPOT-HRV band 3 with difference inter sample spacing distance are given in
Fig.5 (a) A.S.M., (b) Contrast, (c) Mean, (d)Entropy, respectively.
These figures statistics this standard deviations of contrast and mean are so big that difference statistics by their features have less useful ness to extract the textural information entropy and A.S.M are found to provide better results for land cover classification .
The distribution of entropy of land sat band TM 4 given in
fig 6.
Distribution of paddy field nearly equal to that of urban.
Fig.3 Density function of each distance.
(SPOT_HRV
band2, golf course)
Fig.4 Comparison of bands.
(SPOT-HRV, distance=5, Entropy)
Fig.5 Four features.
(SPOT-HRV band3)
a)A.S.M., b)Contrast, c)Mean, d)Entropy
Fig.6 Entropy of Landsat-TM band4.
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