STUDY OF REMOTE-SENSING IMAGE TEXTURE ANALYSIS AND CLASSIFICATION USING WAVELET

In the past one difficulty of texture analysis was the lack of adequat e tools to characterize different scales of texture effectively. Recen t developments in multiresolution analysis such as the Gabor and wavel et transforms, help to overcome this difficulty. This paper introduces a new approach to characterize texture at multiple scales. The perfor mances of the wavelet transform are measured in terms of sensitivity a nd selectivity for the classification of 25 types of remote sensing te xture relief images under the condition of different wavelet decomposi tion models, different filter lengths, different resolutions and diffe rent mother bodies. The reliability exhibited by texture signatures of wavelet transforms are beneficial for accomplishing segmentation, cla ssification and subtle discrimination of texture.