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This paper presents a unified Non-local Spectral-spatial Centralized Sparse Representation (NL-CSR) model for the hyper-spectral image classification. The proposed model integrates local sparsity and non-local mean centralized induced sparsity. To achieve rich spectral-spatial information, the centralized sparsity enforces the sparse coding vector towards its non-local structural self-similar mean...
In this paper, we propose a novel flexible framework for hyperspectral image (HSI) classification using multi-view spectral-spatial feature extracted by nuclear norm based 2D PCA. We first use the multihyphonthesis (MH) prediction method based on ridge regression to generate the 3D spatial-feature array from the HSI. Then, we apply the nuclear norm based 2D PCA to multi-view slices (the image with...
As single-layer feed-forward neural networks, extreme learning machine (ELM) has recently been used with success for the classification of hyperspectral images (HSIs). However, the results of pure pixel-wise spectral classifiers often appear very noisy with limited training samples. To further improve the accuracy, we propose a novel spectral-spatial information integrating scheme for pixel-wise kernel...
This paper presents a region-based composite kernel framework for spatial-spectral hyperspectral image classification, referred as RCK, by exploiting the local similarities of both the spectral and spatial features via superpixel segmentation. The proposed framework consists of three steps. In the first step, the original hyperspectral image together with its spatial feature image are segmented into...
A projected gradient algorithm (PGA) which is derived from the majorization-minimization (MM) framework has been proposed recently for Hessian-matrix Frobenius norm regularization image restoration model so that it currently provides state-of-the-art performance. Outside the MM framework and for the sake of further accelerating the convergence speed, this paper presents an efficient algorithm for...
In this paper, a color image demosaicking (CDM) method is proposed. For preserving the chromatic information, the original RGB plane is transformed to HSI plane, and then an anisotropic tensor matrix kernel regression based interpolation technique is processed in I-component. Finally, a directional interpolation based on R-G/B-G difference is used for R and B channel refinements. Experimental results...
This letter presents a postprocessing algorithm for a kernel sparse representation (KSR)-based hyperspectral image classifier, which is based on the integration of spatial and spectral information. A pixelwise KSR is first used to find the sparse coefficient vectors of the hyperspectral image. Then, a sparsity concentration index (SCI) rule-guided semilocal spatial graph regularization (SSG), called...
Non-local Means(NLM) is increasingly popular in image denoising. In this paper, the nonlocal structure similarity of images obtained by the iteration is exploited. By combining the nonlocal similarity constraints with total variation regularization, an iterative regularized variational model is proposed, in which the nonlocal weight depends on local structure of patches. An effective algorithm is...
Biomedical event extraction is the process of automatically detecting statements of molecular interactions in research articles. It is the hot issue in the biomedical natural language processing. This paper proposed a composite kernel to extract biomedical events. The results indicate that the method is promising.
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