Classification in high-resolution SAR data

Ground surveillance and target recognition by radar has become increasingly important over the years. Modem digitally controlled radar systems have the ability to operate quasi simultaneously in two or more different modes, e.g. after detection of moving targets by MTI these target hypotheses are recorded by a high-resolution spotlight SAR. To classify the SAR signatures different techniques have been investigated. The objective of our work was to support the decision process in choosing the best combination of methods for the problem of ground target classification in high-resolution SAR images. The criteria of optimizing the classification are correctness (low false alarm rate (FAR)), robustness, and computational effort. The investigations have been carried out using the MSTAR public target dataset. In the paper we describe the examination of new classifier approaches like support vector machine (SVM) and relevance vector machine (RVM) in combination with superresolution methods like the CLEAN algorithm. For this purpose we have developed an experimental software system. Its processing chain consists of the following modules: preprocessing, feature extraction, and classification. The tests with the SVM have shown that without preprocessing too many support vectors (up to 50%) are used. Therefore the RVM has been chosen to overcome this disadvantage. The preprocessing methods have been used to reduce the noise and to restore/extract the significant SAR signature. The result of our investigations is an assessment of the different methods and several method combinations. Based on these results the investigation will be extended by more realistic new datasets with a resolution as high as or higher than the MSTAR data.