Registration of two or more images of the same scene is an important procedure in InSAR image processing that seeks to extract differential phase information exactly between two images. Meanwhile, the efficiency for large volume data processing is also a key point in the operational InSAR data processing chain. In this paper, some conventional registration methods are analyzed in detail and the parallel algorithm for registration is investigated. Combining parallel computing model with the intrinsic properties of InSAR data, the authors puts forward an image parallel registration scheme over distributed cluster of PCs. The preliminary experiment will be implemented and the result demonstrates feasibility and effectiveness of the proposed scheme.
Digital change detection using multi-temporal remotely sensed imagery is a key topic in the studies of the global environmental changes. Significant efforts have been made in the development of methods for digital change detection. Among the methods, the multivariate alteration detection (MAD) shows great promising. However, the use of mean and covariance matrix of feature vectors in the method makes the detection non-robust because the mean and covariance matrix are influenced by the presence of outliers. In this article two schemes are proposed to improve the robustness of the MAD method. The two schemes, based on different strategies of outlier handling, consist of a two-pass and a one-pass processing, respectively. Finally a preliminary study was carried out to evaluate the feasibility and effectiveness of the proposed schemes.
A novel unsupervised classification scheme called spatial fuzzy C-means clustering is proposed in this article. Based on conventional fuzzy C-means algorithm, our scheme takes spatial homogeneity into consideration by introducing spatial membership and applying SMNF, thus improved robustness against noises or outliers. Preliminary experimental results are also shown to demonstrate effectiveness of our method.