Abstract
This paper presents a spatial-spectral method for hyperspectral image classification in the regularization framework of kernel sparse representation. First, two spatial-spectral constraint terms are appended to the sparse recovery model of kernel sparse representation. The first one is a graph-based spatially-smooth constraint which is utilized to describe the contextual information of hyperspectral images. The second one is a spatial location constraint, which is exploited to incorporate the prior knowledge of the location information of training pixels. Then, an efficient alternating direction method of multipliers is developed to solve the corresponding minimization problem. At last, the recovered sparse coefficient vectors are used to determine the labels of test pixels. Experimental results carried out on three real hyperspectral images point out the effectiveness of the proposed method.
Highlights
Hyperspectral imaging sensors capture digital images in hundreds of narrow and contiguous spectral bands spanning the visible to infrared spectrum
This paper considers incorporating the spatial-spectral information of hyperspectral images in the regularization framework of kernel SRC (KSRC)
A regularized kernel sparse representation method for spatial-spectral classification of hyperspectral images has been presented in this paper, where the spatial-spectral information of hyperspectral images is incorporated by appending two spatial-spectral constraint terms to the sparse recovery model of KSRC
Summary
Hyperspectral imaging sensors capture digital images in hundreds of narrow and contiguous spectral bands spanning the visible to infrared spectrum. Previous works have highlighted that hyperspectral image classification should focus on analyzing spectral features, and incorporate the information of spatially-adjacent pixels. SRC can achieve good performance in hyperspectral image classification [6], it is hard to classify the data that is not linearly separable To overcome this drawback, kernel SRC (KSRC) is proposed in [13] to capture the nonlinear similarity of features. This paper considers incorporating the spatial-spectral information of hyperspectral images in the regularization framework of KSRC. A spatial location constraint is introduced to capture the prior knowledge of the location information of training pixels and is thereby appended to the sparse recovery model of KSRC. As for the final classification procedure, the sparse coefficient vectors obtained by solving the corresponding regularization problem are used to determine the labels of test pixels.
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