期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B2
页码：181-186
出版社：Copernicus Publications
摘要：In this paper, the concepts of wavelet analysis and neural networks are applied to the classification of shuttle imaging radar experiment C (SIR-C) synthetic aperture radar (SAR) data from a location in northwest China. Initially, the paper presents the visual elements of tone, texture and structural features on SAR imagery as important bases for image classification and target recognition. The wavelet analysis is used as a method to extract elements of texture and structural features; it involves deriving the energy of sub-image blocks through wavelet decomposition. A improved backpropagation neural network was applied to a multiresolution representation of six images comprising reflectance SAR data and those obtained by the wavelet transform. A simple scene was classified, yielding poplar trees and bushes. Where they were well differentiated the probability of yielding the correct classification was found to be 100%. Erroneous classification occurred in transition areas between cover types where the percentage of correct classification fell slightly. The results suggest that such an integrated approach to classification is applicable for SAR data that involves regular textures and structures with rather strong orientation of land features
关键词：Image Classification; Wavelet Analysis; Neural Networks; Texture and Structural Characteristics; SAR
