Selection of relevant features and TerraSAR-X products for classification of high resolution SAR images

Feature extraction and classification using synthetic aperture radar (SAR) images has been a very active research field over recent last years. Although a lot of features have been proposed and many classifiers have been employed, but there are few works on comparing these features for different TerraSAR-X (TSX) product. In principle, there are many features like gray level co-occurrence matrix, Gabor filters, quadrature mirror filters, and non-linear short time Fourier transform that can be very useful for TSX image classification. However, many of these features may be completely irrelevant for classification when different TSX products (standard or special process products) are used. Therefore, an important research direction is to identify the best features and appropriate TSX product for them using the Support Vector Machine and as a measure of the classification accuracy the precision -recall. The precision-recall was computed for all these features and products and after that we identify the feature and the product that perform better than the other. The results shows that: (1) the best feature extraction method is Gabor filters (with different scales and orientations) for almost of the TSX products with an average (for all the classes) of the precision between 89.72% and 97.41% and an average of the recall between 33.59% and 44.16% (depending by the TSX products) and (2) the best product from the multi-resolution product pyramid is the standard MGD-RE product. Our dataset was TerraSAR-X High Resolution Spotlight products taken over Venice and Toulouse where the actual ground cover was known to us. The novelty of this article lies in the fact that these features are applied for SAR images and compared to each other for a multi-resolution pyramid generated for TerraSAR-X MGD products.