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Sensors 2017, 17(2), 314;

Hyperspectral Image Classification with Spatial Filtering and \(l_{(2,1)}\) Norm

School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
Author to whom correspondence should be addressed.
Received: 8 December 2016 / Revised: 24 January 2017 / Accepted: 4 February 2017 / Published: 8 February 2017
(This article belongs to the Section Remote Sensors)
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Recently, the sparse representation based classification methods have received particular attention in the classification of hyperspectral imagery. However, current sparse representation based classification models have not considered all the test pixels simultaneously. In this paper, we propose a hyperspectral classification method with spatial filtering and \(l_{(2,1)}\) norm (SFL) that can deal with all the test pixels simultaneously. The \(l_{(2,1)}\) norm regularization is used to extract relevant training samples among the whole training data set with joint sparsity. In addition, the \(l_{(2,1)}\) norm loss function is adopted to make it robust for samples that deviate significantly from the rest of the samples. Moreover, to take the spatial information into consideration, a spatial filtering step is implemented where all the training and testing samples are spatially averaged with its nearest neighbors. Furthermore, the non-negative constraint is added to the sparse representation matrix motivated by hyperspectral unmixing. Finally, the alternating direction method of multipliers is used to solve SFL. Experiments on real hyperspectral images demonstrate that the proposed SFL method can obtain better classification performance than some other popular classifiers. View Full-Text
Keywords: alternating direction method of multipliers; hyperspectral classification; outliers; spatial filtering and \(l_{(2,1)}\) norm (SFL) alternating direction method of multipliers; hyperspectral classification; outliers; spatial filtering and \(l_{(2,1)}\) norm (SFL)

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Li, H.; Li, C.; Zhang, C.; Liu, Z.; Liu, C. Hyperspectral Image Classification with Spatial Filtering and \(l_{(2,1)}\) Norm. Sensors 2017, 17, 314.

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