Wavelet-Fourier Descriptors for SAR Patch Scene Categorization

Abstract

This paper presents SAR patch categorization based on feature descriptors within the dual tree complex wavelet transform using non-parametric features, which were estimated for each wavelet based subband, which was additionally transformed using a Fourier transform. Spectral properties of wavelet transform were characterized using thefirst and second moments, Kolmogorov Sinai entropy and coding gainwithin an oriented dual tree complex wavelet transform (2D ODTCWT). A database with 2000 images representing 20 different classes with 100 images per class was used for estimation of classification efficiency. A window size for estimation feature parameters was estimated. A supervised learning stage was implemented with support vector machine using 10 % and 20% of the test images per class. The experimental results showed that the non-parametric features achieved 94.3 % accuracy, when 20 % of database was used for supervised training.