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ISPRS Int. J. Geo-Inf. 2017, 6(11), 344; https://doi.org/10.3390/ijgi6110344

Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares

1
The Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
2
School of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, China
This paper is an extended version of our paper published in 2017 IEEE International Geoscience and Remote Sensing Symposium.
*
Author to whom correspondence should be addressed.
Received: 19 September 2017 / Revised: 26 October 2017 / Accepted: 3 November 2017 / Published: 6 November 2017
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Abstract

As a widely used classifier, sparse representation classification (SRC) has shown its good performance for hyperspectral image classification. Recent works have highlighted that it is the collaborative representation mechanism under SRC that makes SRC a highly effective technique for classification purposes. If the dimensionality and the discrimination capacity of a test pixel is high, other norms (e.g., 2 -norm) can be used to regularize the coding coefficients, except for the sparsity 1 -norm. In this paper, we show that in the kernel space the nonnegative constraint can also play the same role, and thus suggest the investigation of kernel fully constrained least squares (KFCLS) for hyperspectral image classification. Furthermore, in order to improve the classification performance of KFCLS by incorporating spatial-spectral information, we investigate two kinds of spatial-spectral methods using two regularization strategies: (1) the coefficient-level regularization strategy, and (2) the class-level regularization strategy. Experimental results conducted on four real hyperspectral images demonstrate the effectiveness of the proposed KFCLS, and show which way to incorporate spatial-spectral information efficiently in the regularization framework. View Full-Text
Keywords: hyperspectral; image classification; least squares; collaborative representation; sparse representation; posterior probability; regularization hyperspectral; image classification; least squares; collaborative representation; sparse representation; posterior probability; regularization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Liu, J.; Wu, Z.; Xiao, Z.; Yang, J. Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares. ISPRS Int. J. Geo-Inf. 2017, 6, 344.

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