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Remote Sens. 2017, 9(6), 558; doi:10.3390/rs9060558

Multiobjective Optimized Endmember Extraction for Hyperspectral Image

1
The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
2
School of Computer, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez, Lenio Soares Galvao and Prasad S. Thenkabail
Received: 19 March 2017 / Revised: 31 May 2017 / Accepted: 1 June 2017 / Published: 3 June 2017
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
View Full-Text   |   Download PDF [5189 KB, uploaded 3 June 2017]   |  

Abstract

Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis. It is also a challenging task due to the intrinsic complexity of remote sensing images and the lack of priori knowledge. In recent years, a number of EE methods have been developed, where several different optimization objectives have been proposed from different perspectives. In all of these methods, only one objective function has to be optimized, which represents a specific characteristic of endmembers. However, one single-objective function may not be able to express all the characteristics of endmembers from various aspects, which would not be powerful enough to provide satisfactory unmixing results because of the complexity of remote sensing images. In this paper, a multiobjective discrete particle swarm optimization algorithm (MODPSO) is utilized to tackle the problem of EE, where two objective functions, namely, volume maximization (VM) and root-mean-square error (RMSE) minimization are simultaneously optimized. Experimental results on two real hyperspectral images show the superiority of the proposed MODPSO with respect to the single objective D-PSO method, and MODPSO still needs further improvement on the optimization of the VM with respect to other approaches. View Full-Text
Keywords: hyperspectral remote sensing; endmember extraction; multi-objective; particle swarm optimization hyperspectral remote sensing; endmember extraction; multi-objective; particle swarm optimization
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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, R.; Du, B.; Zhang, L. Multiobjective Optimized Endmember Extraction for Hyperspectral Image. Remote Sens. 2017, 9, 558.

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