REMOTE SENSING IMAGE TEXTURE ANALYSIS AND CLASSIFICATION 
WITH WAVELET TRANSFORM 
Changqing Zhu 
Zhengzhou Institute of Surveying and Mapping 
Zhengzhou 450052 CHINA 
KEY WORDS: Remote-Sensing , Analysis, Modeling , Texture. 
ABSTRACT: 
One difficulty of texture analysis in the past was the lack of adequate tools to characterize different 
scales of textures effectively. Recent develmpments in multiresolution analysis such as the Cabor and 
wavelet transforms help to overcome this difficult. This paper intruducs a new approach to characterize 
texture at multiple scales. The performance of wavelet transform are measured in terms of sensitivety and 
selectivity for the classification of twenty remote sensing textures. The reliability exhibited by texture sig- 
natures of such transforms are beneficial for accomplishing segmention, classification and subtle discrimi- 
nation of texture. 
1. INTRODUCTION 
Textures provide important characteristics for 
the analysis of many types of images including nat- 
ural scenes, remote sensing data and biomedical 
modalities. The perception of texture is believed to 
play an important role in the human visual system 
for recognition and interpretation. Perious methods 
of analysis for accomplishing texture classification 
maybe roughly divided into three categories : statis- 
tical, structural and spectral [1][2][3]. These 
methods have been successful for many fields, but 
they share one common weakness. That is, the pri- 
marily focus on the coupling between image pixels 
on a single scale. More recently, the methods 
based on multichannal or multiresolution analysis 
have received a lot of attention. Recent develop- 
ments in spatial/frequency analysis such as Gabor 
transform, Wigner distribution, DCT , and wavelet 
transform provide good mutiresolution analytical 
tools. Specifically, wavelet transform plaies an im- 
portant part in texture analysis. 
There were some studies for texture classifica- 
tion by wavelet tramform. Carter [4] first reported 
texture classification results using Morlet and Mex- 
ican hat wavelets. He achieved 98 percent accuracy 
on 6 types of natural textures. Andrew and Jian 
[5] stuied texture classification by wavelet packet 
signatures. They achieved more than 98 percent ac- 
curacy on 25 types of natural textures. In this pa- 
per, we introuduced a new approach to characterize 
remote sensing texture images at multiple-scales 
with wavelet transform. The performance of 
wavelet transform are measured for the classifica- 
tion of twenty aerial remote sensing texture im- 
ages. Wavelet representions for twenty images in 
the same resolution were classified with few errors 
by a simple minimun-distance classifier. The classi- 
fication for six images in different resalutions had 
been done, too. 
2. THE METHOD OF CLASSIFICATION 
2. 1 Image Features by Orthonoral Wavelet Trans- 
form 
By orthonoral wavelet transform, we mean the 
decomposition. of an. image into multiple levels 
framework. Each level and each portion represent 
themselves special properties of frequency and spa- 
tial. Fig. 1 shows a level wavelet decomposition, in 
which c represent low frequency information, dl 
the vertical edge information, d2 the horizonal edge 
information, and d3 inclining edge information. 
Fig. 2 shows a 4-levels wavelet decomposition and 
obtains seventeen sub-images include original im- 
age. Here wavelet is selected for Daubechics 
1036 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B3. Vienna 1996 
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