Selection of matching area in SAR Scene-Matching-Aided navigation based on manifold learning

Selection of suitable matching area is one of the key issues for image-matching-aided navigation system, but it is also a very challenging mission, especially with the multi-source image matching tasks. In this paper, a novel method to analyze the matching suitability of the satellite optical photograph to the realtime SAR in candidate flying regions is put forward. At first, several typical low-level image features are extracted. Then manifold learning is used to reduce the dimension of the sampled features, so as to generate new high-level image features with better discrimination ability. Finally, with the new features generated by manifold learning, we used support vector machines (SVM) to divide the candidate regions into two classes for suitable or unsuitable for matching. The experimental result shown that the proposed method is valid and effective.