Airport Targets Extraction from SAR Imagery Based on Wavelet Texture and Improved Fuzzy Cmeans Clustering

Abstract:

The paper presents definitions of several wavelet textures and a novel automatic airport targets extraction method, an unsupervised method without seed point and training areas, from Synthetic Aperture Radar (SAR) imagery. Wavelet channels at different directions through multiscale wavelet transformation were obtained. Then the four crucial frequency cooccurrence matrix features for the textures were computed in each channel of wavelet to get the eigenvectors of the wavelet textures.Principal component analysis (PCA) was adopted to eliminate the correlation of features; the feature reduction was successful and the segmentation performance could be maintained.Finally followed by implementing the improved fuzzy cmeans clustering method to the feature space, the desirable airports extraction was achieved. Experiments show that with the method,the extracting and the location precision are 97.3% and 99.3% respectively.This study provides a good reference for the location of airports in the SAR imagery.

Key words:synthetic aperture radar; wavelet texture; frequency cooccurrence matrix; improved fuzzy cmeans clustering; feature space; automatic extraction