Deep convolutional neural networks for SAR patch categorization

Abstract

The categorization of Synthetic Aperture Radar (SAR) patches consists of feature extraction and classification. Recently, very good results were obtained using a convolutional neural networks for categorization of image patches. This paper presents deep convolutional networks for Synthetic Aperture Radar patch categorization. Several structures of deep convolutional networks are introduced. We have tested convolutional networks with 10 and 20 layers and analyzed recognition rate by changing SAR patch size. We have designed a custom database of SAR patches, which were cut from several spotlight TerraSAR-X products. Database consisted of 6 categories with approximately 1000 samples per each category. Experimental results showed that the Convolutional neural networks can achieve 84 % accuracy using patches with a size of 200 × 200 pixels and it performs slightly better than algorithm for categorization, which use dual tree oriented wavelet transformation, spectral features and Support vector machine, which achieved accuracy of 80 % using the same training and testing sets.