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This letter presents a novel unsupervised change detection approach for multi-temporal synthetic aperture radar (SAR) image. The proposed method applies the semi-supervised Laplacian support vector machine (SVM) to classify the changed areas and the unchanged areas. A pseudo-training set, which is necessary for initializing the SVM, is generated according to the saliency similarity detection. The pseudo-training set is composed by the labelled changed pixels and the labelled unchanged pixels. The Laplacian SVM explores the prior information of the available labelled samples and combines unlabelled samples to enhance its discrimination. Experimental results on several real SAR image data sets confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In polarimetric synthetic aperture radar (PolSAR) image processing, the number of classes is an important factor for PolSAR image classification. Therefore, how to accurately estimate the number of PolSAR image classes is an important issue. In this article, we propose a novel unsupervised classification method which can accurately estimate the number of classes for PolSAR images. First, the PolSAR image is initialized into many small clusters by using the complementary information from Yamaguchi decomposition and distribution characteristics of data. Second, the improved clustering by fast search and find of density peaks, named as improved CFSFDP algorithm, is introduced to select the appropriate category number. Finally, to improve the representation of each category, the PolSAR data set is classified by an iterative fine-tuning process based on a complex K-Wishart function. The performance of the proposed classification approach is presented and analysed on three real data sets. The experimental results show that the proposed classification method can accurately estimate the category number and enhance the classification accuracy in comparison with other traditional methods. It is also shown that the data distribution characteristic has the additional information beyond the target scattering decomposition, and this information is important for the initialization. [ABSTRACT FROM AUTHOR]

It is well known that various features extraction approaches are utilized in polarimetric synthetic aperture (PolSAR) terrain classification for representing the data characteristic. It needs relevant and effective feature fusion algorithms to process complicated features. To address this issue, this article presents a multimodal sparse representation (MSR) framework based algorithm to fuse the different feature vectors from the complicated data space. Polarimetric data features, decomposition features, and the texture features from Pauli colour-coded image are selected to represent multimodal data in different observation modes. The corresponding multimodal manifold regularizations are added to MSR framework to approximate the data structure. Considering the independence and correlation of features, the intrinsic affinity matrices are calculated from this framework. They are processed via local preserve projection algorithm to project the multimodal features into a low dimensionally intrinsic feature space for subsequent classification. Three datasets are utilized in experiments, Western Xi'an, Flevoland, and San Francisco Bay datasets from the Radarsat-2 system in C-band. The effect of regularization parameters and different dimensional fused features are analysed in visualization and quantitation performance. The experiment results demonstrate that the effectiveness and validity of proposed method are superior to other state-of-art methods. [ABSTRACT FROM AUTHOR]