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Remote Sens. 2017, 9(12), 1255; https://doi.org/10.3390/rs9121255

Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

1
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
2
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
3
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510640, China
4
The Guangzhou Key Laboratory of Digital Content Processing and Security Technologies, Guangzhou 510665, China
*
Author to whom correspondence should be addressed.
Received: 28 September 2017 / Revised: 23 November 2017 / Accepted: 30 November 2017 / Published: 2 December 2017
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

Although sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework. View Full-Text
Keywords: hyperspectral image (HSI) classification; sparse multinomial logistic regression (SMLR); extreme sparse multinomial logistic regression (ESMLR); extended multi-attribute profiles (EMAPs); linear multiple features learning (MFL); Lagrange multiplier hyperspectral image (HSI) classification; sparse multinomial logistic regression (SMLR); extreme sparse multinomial logistic regression (ESMLR); extended multi-attribute profiles (EMAPs); linear multiple features learning (MFL); Lagrange multiplier
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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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Cao, F.; Yang, Z.; Ren, J.; Ling, W.-K.; Zhao, H.; Marshall, S. Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification. Remote Sens. 2017, 9, 1255.

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