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Geology Disaster
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Drainage pattern classification by texture analysis
Mitsuharu Tokunaga
Central Computer Services Co. Ltd.
4-3-13 Tranomon, Minatoku, Tokyo 105 Japan
Toshiaki Hashimoto, Shunji Murai
Institute of Industrial Science, University of Tokyo
7-22 Roppongi, Minako, Tokyo 106 Japan
Abstract
Although a geological map is essential to the exploration of underground resources, it is not prepared in some countries, especially in developing classification enables to give effective information for the exploration. The drainage pattern classification has been carried out by an expert fro aerial photographs or satellite images. The process is very time consuming and the results depend on the skillfulness and experience of the interpreter. So it is very desirable to carry out the drainage pattern classification automatically and objectively with a computer.
This paper describes the automated drainage pattern classification by texture analysis of DEM from SPOT images.
Method of Texture
The flow of texture analysis is shown in Fig. 1
- Generation of DEM
The DEM on 40m grid size generated by stereo matching using SPOT images. The located of test sties are as follows.
| KITAMI area |
about 20km * 20km |
(fig,. 2) |
| ASO area |
about 20km * 20km |
(fig 3) |
Fig. 1 Flow of Texture Analysis
With the current program, there can be seen some matching errors.
- Grid size of DEM
When the grid size is small, fine textures (high frequency component) are analysed. Otherwise, rough textures (low frequency component) are analysed.
The four case in 40m, 80m, 160m and 320m were selected in this study.
- Quantization level of DEM
The size od co-occurrence matrix is decided on the quantization level of DEM. When the DEM is distributed between 1 and n, the co-occurrence matrix become n*n matrix size, the analyses were carried out with the images level sliced by 10m,20m, 40m and 80m.
The size of co-occurrence matrix in eh study area is shown in table 1
Table 1 Quantitative level
| Level slice pitch |
Quantization level |
| 10m |
80*80 - 120*120 |
| 20m |
40*40 - 60*60 |
| 40m |
20*20 - 30*30 |
| 80m |
10*10 - 15*15 |
- Window size
The window size corresponds to the size of calculated area in co-occurrence matrix, therefore the change of window size influence the texture of geographical features.
The analyses were carried out on three cases of window size of 5*5, 11*11 and 15*15.
- Calculation of co-occurrence Matrix
The co-occurrence probability of (k,l) in the window is defined as P (k,1). Where k, I : value of DEM.
When the direction of co-occurrence is I ad quantization level of DEM is n, Co-occurrence probability Pi (k, l) in the direction I is expressed by the following matrix.
- Types of Texture
The following textures were derived from Co-occurrence matrix
- Energy
- Entropy
- Correlation
- Homogeneity
- Moment of Inertia
Fig. 3 DEM of ASO Area from SPOT Stereo Images
Fig. 4 Grid size of DEM
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