SAR scene characterization using complex wavelets

This paper presents SAR image classification based on feature descriptors within the dual tree oriented discrete wavelet transform. The non-parametric approach to the feature extraction and supervised learning is presented in this paper. The spectral features, known from sound processing were used for subband features within the wavelet domain. Those features characterizing each subband of oriented wavelet transform were used for supervised classification using support vector machine. The database with 1300 images with 200 × 200 pixels was designed using 30 different high resolution TerraSAR-X spotlight images. 10 percent of all features for each class were used for training. The efficiency of presented method was compared with Gray Level Co-occurrence Matrix (GLCM) method and log commulants of Fourier transform. The experimental results showed improved classification results compared to the state-of-the-art methods used in this paper.