Texture analysis using differnce statistics for land cover classification
Michiyusu Akasaka, Katsunoii Furuya and Ryutaro Tateishi
Remote Sensing and Image Research Center
Chiba university
1-33 Yayoi-Cho Chiba City Chiba 250 Japan
Abstract
To improve land cover classification accuracy spatial information should be considered this study deals with difference statistics method statistics which is one of texture analysis the optimum parameters of difference statistics were investigated and the way to apply difference statistics to land cover classification was proposed among four known features from to provide statistics inaugural second moment and entropy are found to provide better results for land cover classification concerning multi spectral band for difference statistics near infrared band 3 land sat is found to be best.
Introduction
Satellite imagery data include both the spectral information and the spatial information how ever most of approaches to land cover classification has been used only spectral information systematic and suitable land cover classification method using spatial information is not yet established in this study difference statistics were picked up to extract the textural information of land sat Tm image SPOT HRV image authors investigated the use of textual features for classification of land cover.
Texture contain information about the spatial distribution of tonal variations with a band texture analysis is separated in to statistical texture analysis and structural analysis four standard approaches to statistical texture analysis make use of features based on co-occurrence matrix on difference statistics on run length matrix and on the fourier power spectrum respectively co- occurrence matrix and difference statistics have a capability of discriminating textual feature than the others co occurrence matrix method is based on the second order joint probability densities of parts of gray levels while difference statistics method is based on first order probability density function therefore distribution on difference statistics is more stable than co-occurrence matrix method fore this season difference statistics method is applied for texture analysis in this study.
Difference statistics
Procedure to obtain density function to calculate difference statistics is given in
fig 1.
ex.) displacement
d ={ r ,
q )
= { 4, 0- 360° )
r : inter sample spacing distance
q : angle
Pd (k) = f (K) / N
K : difference of image data at two points with the displacement
d (r,
q )
( k=0-225, integer )
P
d (k) : density function (Probability of difference k)
f(k) : frequency of difference k
N: number of surrounding pixels
( N=24 in this ex. )
Fig.1 Procedure to obtain density function.
Four texture are defined from each of those density functions pd they are follows.
- Angular Second Moment ( A.S.M )
- Contrast
- Mean
- Entropy
