MULTI-SOURCE REMOTE SENSING IMAGE REGISTRATION BASED ON SIFT AND OPTIMIZATION OF LOCAL SELF-SIMILARITY MUTUAL INFORMATION

High-precision and robust matching of multi-source remote sensing image matching is not easy to achieve because of non-linear intensity differences and significant geometric distortions. A new registration method is proposed by integrating the scale-invariant feature transform (SIFT) and optimization of local self-similarity mutual information (LSS_MI). This method consists of two main steps. In the first step, SIFT approach with a reliable outlier removal procedure is implemented. By repeatedly fine turning several selected matched feature point coordination, a series of registration parameters are estimated by least square method and used to construct initial particle swarms. Then, a local self-similarity descriptor (LSS) is computed for pre-matching image pairs and the optimal match parameters are obtained by optimizing LSS_MI based on QPSO. The experimental results verify that the LSS-MI is more robust and accurate than regional mutual information in multi-source remote sensing image.