A Band Selection Method for Hyperspectral Image Classification based on improved Particle Swarm Optimization

Shen, Jie; Wang, Chao; Wang, Ruili; Huang, Fengchen; Fan, Chao; Xu, Lizhong

doi:10.14257/ijsip.2015.8.4.28

Cited by 4 publications

(2 citation statements)

References 5 publications

(5 reference statements)

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“…To better evaluate the performance of the proposed method, the works of Li et al [27], Xu et al [32], and Shen et al [33] are compared with the proposed method. In [31], a band selection method based on a particle swarm dynamic with sub-swarm optimization was proposed for dimension reduction of hyperspectral sensor data.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Dimension Reduction for Hyperspectral Remote Sensor Data Based on Multi-Objective Particle Swarm Optimization Algorithm and Game Theory

Yang

Zhang

et al. 2019

Sensors

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Information entropy and interclass separability are adopted as the evaluation criteria of dimension reduction for hyperspectral remote sensor data. However, it is rather single-faceted to simply use either information entropy or interclass separability as evaluation criteria, and will lead to a single-target problem. In this case, the chosen optimal band combination may be unfavorable for the improvement of follow-up classification accuracy. Thus, in this work, inter-band correlation is considered as the premise, and information entropy and interclass separability are synthesized as the evaluation criterion of dimension reduction. The multi-objective particle swarm optimization algorithm is easy to implement and characterized by rapid convergence. It is adopted to search for the optimal band combination. In addition, game theory is also introduced to dimension reduction to coordinate potential conflicts when both information entropy and interclass separability are used to search for the optimal band combination. Experimental results reveal that compared with the dimensionality reduction method, which only uses information entropy or Bhattacharyya distance as the evaluation criterion, and the method combining multiple criterions into one by weighting, the proposed method achieves global optimum more easily, and then obtains a better band combination and possess higher classification accuracy.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Dimension Reduction for Hyperspectral Remote Sensor Data Based on Multi-Objective Particle Swarm Optimization Algorithm and Game Theory

Yang

Zhang

et al. 2019

Sensors

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“…The particle swarm optimization based methods implement a defined iterative searching criterion function to obtain a proper band subset that maximizes the intra-class separabilities. Typical algorithms are the simple particle swarm optimization algorithm using the searching criterion function of minimum estimated abundance covariance [24], the parallel particle swarm optimization algorithm [25], and the improved particle swarm optimization algorithm [26]. The particle swarm optimization based methods have lower computational complexity and smaller parameter tuning works, but the methods are easily encountered in local minima and could not guarantee successful global optimization.…”

Section: Introductionmentioning

confidence: 99%

A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification

Sun

Jiang

et al. 2016

Remote Sensing

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Abstract:A novel Symmetric Sparse Representation (SSR) method has been presented to solve the band selection problem in hyperspectral imagery (HSI) classification. The method assumes that the selected bands and the original HSI bands are sparsely represented by each other, i.e., symmetrically represented. The method formulates band selection into a famous problem of archetypal analysis and selects the representative bands by finding the archetypes in the minimal convex hull containing the HSI band points (i.e., one band corresponds to a band point in the high-dimensional feature space). Without any other parameter tuning work except the size of band subset, the SSR optimizes the band selection program using the block-coordinate descent scheme. Four state-of-the-art methods are utilized to make comparisons with the SSR on the Indian Pines and PaviaU HSI datasets. Experimental results illustrate that SSR outperforms all four methods in classification accuracies (i.e., Average Classification Accuracy (ACA) and Overall Classification Accuracy (OCA)) and three quantitative evaluation results (i.e., Average Information Entropy (AIE), Average Correlation Coefficient (ACC) and Average Relative Entropy (ARE)), whereas it takes the second shortest computational time. Therefore, the proposed SSR is a good alternative method for band selection of HSI classification in realistic applications.

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A mutual information-Dempster-Shafer based decision ensemble system for land cover classification of hyperspectral data

Pahlavani

Bigdeli

2016

Front. Earth Sci.

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A Band Selection Method for Hyperspectral Image Classification based on improved Particle Swarm Optimization

Abstract: Abstract

Cited by 4 publications

References 5 publications

Dimension Reduction for Hyperspectral Remote Sensor Data Based on Multi-Objective Particle Swarm Optimization Algorithm and Game Theory

Dimension Reduction for Hyperspectral Remote Sensor Data Based on Multi-Objective Particle Swarm Optimization Algorithm and Game Theory

A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification

A mutual information-Dempster-Shafer based decision ensemble system for land cover classification of hyperspectral data

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