An Integration of Wavelet Analysis and Neural Networks in Synthetic Aperture Radar Image Classification*

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. * This research is supported by the Special Funds for Major State Basic Research Project (Grant No: G2000077900) and supported by Chinese National Natural Science Funds (Grant No: 40071061).