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An approach of weighted Wishart distance learning, shorted for W2-based distance learning, is proposed for polarimetric synthetic aperture radar (PolSAR) image classification. It aims to adjust the Wishart distance by enhancing discrimination as well as exploiting spatial information. The proposed distance learning keeps samples within the same category close and separates samples from the different classes far apart. It is effectively implemented by solving a linear programming. Input of W2-based distance learning is called weighted Wishart feature, which is designed specifically for PolSAR data to describe the Wishart distribution, achieve regional consistency, and reduce speckle noise. Weight is calculated according to an adaptive window, where homogeneous samples are derived based on a connected region and extracted edge information. With this feature, W2-based distance learning is a whole scheme to adjust the Wishart distance. Furthermore, our experiments with benchmark data sets suggest that the proposed scheme provides both improved performance in terms of visual effect and classification accuracy. The achieved overall accuracy is better by more than 7% compared to other state-of-art methods. [ABSTRACT FROM PUBLISHER]

Sparse representation-based classification (SRC) and collaborative representation-based classification (CRC) have been proposed for image classification. Sparsity is overemphasized in SRC, but ignored in CRC, which has low representation complexity. In this article, a new local collaborative representation scheme is proposed for the classification of hyperspectral imagery (HSI). First, for a test sample with an unknown label, a few significant atoms are selected based on their contributions in representing the test sample, where the-norm is replaced by the-norm like CRC to reduce the complexity. Next, the test sample is represented by the active atoms. Finally, the class label of the test sample is determined based on the contribution of each class of active atoms. Additionally, in order to improve the robustness of the active atoms, both the test sample and the samples in its neighbourhood as context information are considered in the selection of active atoms and the class determination. Experimental results on three real HSI data sets confirm the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

This study clarifies the implicit potential deficiency caused by the sparse cardinality parameterkin Rong et al. (2014). In addition,k = β × W × M × N(0.9 ≤ β < 1) is suggested to avert this potential deficiency, whereβis a ratio controlling the amount of sparse cardinality,Wis the number of multispectral bands andM × Nis the size of panchromatic image. With the choice ofksuggested in this study, the low rank matrixLand sparse matrixSobtained by Go Decomposition (Zhou and Tao 2011) can be iteratively optimized and solved. Thus, instead of choosingkasW × M × Nin Rong et al. (2014), the potential deficiency thatLis directly obtained as an analytic solution can be averted. [ABSTRACT FROM AUTHOR]

High-frequency injection (HFI)-based methods are proved to be powerful in pansharpening multispectral (MS) images. In this article, based on one of the low-rank and sparse (LRS) decomposition algorithms, i.e. Go Decomposition (GoDec), a HFI-based pansharpening method exploiting spatial structure sharpness of both MS images and a low-frequency panchromatic (PAN) image component is proposed. The spectral and spatial measure of local perceived sharpness (S3) is employed to estimate sharpness of the corresponding MS and low-frequency PAN component blocks, and the sharper one is used to construct the spatial structure of the sharpened MS images. Experimental results with QuickBird, IKONOS and WorldView-2 data demonstrate that the proposed method is comparable with or even better than other popular methods. [ABSTRACT FROM AUTHOR]