A band selection method of hyperspectral remote sensing based on particle frog leaping algorithm

Abstract

Dimensionality reduction is becoming an important problem in hyperspectral image classification. Band selection as an effective dimensionality reduction method has attracted more research interests. In this paper, a band selection method for hyperspectral remote sensing images based on subspace partition and particle frog leaping optimization algorithm is proposed. Three new evolution strategies are designed to form a probabilistic network extension structure to avoid local convergence. At the same time, the information entropy of the selected band subset is used as the weight of inter-class separability, and a new band selection criterion function is constructed. The simulation results show that the proposed algorithm has certain advantages over the existing similar algorithms in terms of classification accuracy and running time.

References

[1] FengJ, JiaoL C, ZhangX, SunT. IEEE Transactions on Geoscience & Remote Sensing, 2014, 52: 4092

[2] DuQ, YangH. IEEE Geoscience & Remote Sensing Letters, 2008, 5: 564

[3] GrovesP, BajcsyPMethodology for Hyperspectral Band and Classification Model SelectionAdvances in Techniques for Analysis of Remotely Sensed Data, 2003, 120

[4] SamadzadeganF, MahmoudiF TOptimum Band Selection in Hyperspectral Imagery Using Swarm Intelligence Optimization AlgorithmsInternational Conference on Image Information Processing, 2011, 1

[5] KambhatlaN, LeenT K. Neural Computation, 1997, 9: 1493

[6] BallJ E, BruceL M, YounanN H. IEEE Geoscience & Remote Sensing Letters, 2007, 4: 382

[7] ZhuK, YueL, YangM, WangL, XieB, ChengY. Journal of Jilin University, 2016, 46: 876

[8] MedjahedS A, SaadiT A, BenyettouA, OualiM. Egyptian Journal of Remote Sensing & Space Science, 2016, 19: 163

[9] BhardwajK, PatraS. Isprs Journal of Photogrammetry & Remote Sensing, 2018, 138: 139

[10] WangM, WanY, YeZ, GaoX, LaiX. A Band Selection Method for Airborne Hyperspectral Image based on Chaotic Binary Coded Gravitational Search Algorithm, 2017,

[11] LiZ, KamleshM, CheeP L, SiewC N. Decision Support Systems, 2017,

[12] EusuffM M, LanseyK E, LanseyK. Journal of Water Resources Planning & Management, 2003, 129: 210

[13] SuH, DuQ, ChenG, DuP. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2014, 7: 2659

[14] WangL, WeiF. Journal of Harbin Institute of Technology, 2013, 45: 100

[15] PahlavaniP, HasanlouM, NahrS T. Journal of the Indian Society of Remote Sensing, 2016, 45: 1

[16] ShuklaU P, NandaS J. Expert Systems With Applications, 2018, 97: 336

This work has been supported by the National Natural Science Foundation of China (No.61571149).