Multiobjective endmember extraction for hyperspectral image

Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis, and it is also one of the most challenging tasks 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, an...

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Published in:IEEE International Geoscience and Remote Sensing Symposium proceedings pp. 1161 - 1164
Main Authors: Rong Liu, Bo Du, Liangpei Zhang
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2017
Subjects:
Complexity theory
endmember extraction
Hyperspectral imaging
Hyperspectral remote sensing
Linear programming
multi-objective
Optimization
Particle swarm optimization
ISSN:2153-7003
Online Access:Get full text
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Abstract Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis, and it is also one of the most challenging tasks 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, and several different optimization objectives have been proposed from different perspectives. In all of these methods, there is only one objective function to be optimized, which represents a specific characteristic of the remote sensing image. However, one single-objective function may not provide satisfactory 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 and root-mean-square error (RMSE) minimization are simultaneously optimized. The result set of Pareto-optimal solutions contains a number of non-dominated solutions, from which the user can judge relatively and pick up the most promising one according to the problem requirements. Experiments on two real hyperspectral images were conducted to evaluate the proposed MODPSO algorithm, which confirmed the effectiveness of the proposed algorithm.
AbstractList Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis, and it is also one of the most challenging tasks 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, and several different optimization objectives have been proposed from different perspectives. In all of these methods, there is only one objective function to be optimized, which represents a specific characteristic of the remote sensing image. However, one single-objective function may not provide satisfactory 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 and root-mean-square error (RMSE) minimization are simultaneously optimized. The result set of Pareto-optimal solutions contains a number of non-dominated solutions, from which the user can judge relatively and pick up the most promising one according to the problem requirements. Experiments on two real hyperspectral images were conducted to evaluate the proposed MODPSO algorithm, which confirmed the effectiveness of the proposed algorithm.
Author Bo Du
Rong Liu
Liangpei Zhang
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  organization: Sch. of Comput., Wuhan Univ., Wuhan, China
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  surname: Liangpei Zhang
  fullname: Liangpei Zhang
  organization: State Key Lab. of Inf. Eng. in Surveying, Wuhan Univ., Wuhan, China
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Snippet Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis, and it is also one of the most challenging tasks due to the...
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StartPage 1161
SubjectTerms Complexity theory
endmember extraction
Hyperspectral imaging
Hyperspectral remote sensing
Linear programming
multi-objective
Optimization
Particle swarm optimization
Title Multiobjective endmember extraction for hyperspectral image
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