Abstract
Selection of useful bands plays a very important role in hyperspectral image classification. In the past decade, metaheuristic algorithms have been used as promising methods for solving this problem. However, many metaheuristic algorithms may provide unsatisfactory performance due to their slow or premature convergence. Therefore, how to develop algorithms well balancing the exploration and exploitation, and find the suitable bands precisely is still a challenge. In this paper, a new hybrid global optimization algorithm, which is based on the Wind Driven Optimization (WDO) and Cuckoo Search (CS) is proposed to solve hyperspectral band selection problems. Both WDO and CS have strong searching ability and require less control parameters, but easily suffer from premature convergence due to loss of diversity of population. The proposed approach uses the Chebyshev chaotic map to initialize the population at initial step. The population is divided into two subgroups and WDO and CS are adopted for these two subgroups independently. By division, these two subgroups can share suitable information and utilize each other’s pros, thus avoid premature convergence, and obtain best optimal solution. Furthermore, the Levy flight step size in CS algorithm is adaptively adjusted based on fitness value and current iteration number, which helps in boosting the convergence speed of algorithm. The experimental results on three standard benchmark datasets namely, Pavia University, Botswana and Indian Pines, prove the superiority of the proposed approach over standard WDO and CS approaches as well as the other traditional approaches in terms of classification accuracy with fewer bands.
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References
Abed-alguni BH, Alkhateeb F (2018) Intelligent hybrid cuckoo search and b -hill climbing algorithm. J King Saud Univ - Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2018.05.003
Bayraktar Z, Komurcu M, Werner DH (2010) Wind Driven Optimization (WDO): a novel nature-inspired optimization algorithm and its application to electromagnetics, 2010 IEEE Antennas Propag. Soc Int Symp:1–4. https://doi.org/10.1109/APS.2010.5562213
Bayraktar Z, Komurcu M, Bossard JA, Werner DH (2013) The wind driven optimization technique and its application in electromagnetics. IEEE Trans Antennas Propag 61:2745–2757. https://doi.org/10.1109/TAP.2013.2238654
Boggavarapu LNP, Manoharan P (2020) Classification of hyper spectral remote sensing imagery using intrinsic parameter estimation. Springer International Publishing, Berlin. https://doi.org/10.1007/978-3-030-16660-1
Bouyer A, Hatamlou A (2018) An efficient hybrid clustering method based on improved cuckoo optimization and modified particle swarm optimization algorithms. Appl Soft Comput J 67:172–182. https://doi.org/10.1016/j.asoc.2018.03.011
Chang CI, Wang S (2006) Constrained band selection for hyperspectral imagery. IEEE Trans Geosci Remote Sens 44:1575–1585. https://doi.org/10.1109/TGRS.2006.864389
Feng J, Jiao LC, Zhang X, Sun T (2014) Hyperspectral band selection based on trivariate mutual information and clonal selection. IEEE Trans Geosci Remote Sens 52:4092–4115. https://doi.org/10.1109/TGRS.2013.2279591
Feng S, Itoh Y, Parente M, Duarte MF (2017) Hyperspectral band selection from statistical wavelet models. IEEE Trans Geosci Remote Sens 55:2111–2123. https://doi.org/10.1109/TGRS.2016.2636850
Garg H (2019) A hybrid GSA-GA algorithm for constrained optimization problems a hybrid GSA-GA algorithm for constrained optimization problems. Inf Sci (Ny) 478:499–523. https://doi.org/10.1016/j.ins.2018.11.041
Ghamisi P, Benediktsson JA (2015) Feature selection based on hybridization of genetic algorithm and particle swarm optimization. IEEE Geosci Remote Sens Lett 12:309–313. https://doi.org/10.1109/LGRS.2014.2337320
http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes (n.d.)
Jia J, Yang N, Zhang C, Yue A, Yang J, Zhu D (2013) Object-oriented feature selection of high spatial resolution images using an improved relief algorithm. Math Comput Model 58:619–626. https://doi.org/10.1016/j.mcm.2011.10.045
Kailath T (1967) The divergence and Bhattacharyya distance measures in signal selection. IEEE Trans Commun Technol 15:52–60. https://doi.org/10.1109/TCOM.1967.1089532
Kumar BLNP (2020) Hyperspectral image classification using fuzzy-embedded hyperbolic sigmoid nonlinear principal component and weighted least squares approach. J Appl Remote Sens 14(2):024501. https://doi.org/10.1117/1.JRS.14.024501
Kumar BLNP, Manoharan P (2020) A new framework for hyperspectral image classification using Gabor embedded patch-based convolution neural network. Infrared Phys Technol 110:103455. https://doi.org/10.1016/j.infrared.2020.103455
Li S, Wu H, Wan D, Zhu J (2011) Knowledge-based systems an effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine. Knowledge-Based Syst 24:40–48. https://doi.org/10.1016/j.knosys.2010.07.003
Liu Y, Wang G, Chen H, Dong H, Zhu X, Wang S (2011) An improved particle swarm optimization for feature selection. J Bionic Eng 8:191–200. https://doi.org/10.1016/S1672-6529(11)60020-6
MartÍnez-UsÓMartinez-Uso A, Pla F, Sotoca JM, GarcÍa-Sevilla P (2007) Clustering-based Hyperspectral band selection using information measures. IEEE Trans Geosci Remote Sens 45:4158–4171. https://doi.org/10.1109/TGRS.2007.904951
Max-dependency C (2005) Feat Select Based Mutual Info 27:1226–1238
Medjahed SA, Ouali M (2018) Band selection based on optimization approach for hyperspectral image classification. Egypt J Remote Sens Sp Sci. https://doi.org/10.1016/j.ejrs.2018.01.003
Medjahed SA, Saadi TA, Benyettou A, Ouali M (2015) Binary cuckoo search algorithm for band selection in hyperspectral image classification. IAENG Int J Comput Sci 42:1–9
Medjahed SA, Ait Saadi T, Benyettou A, Ouali M (2016) Gray wolf optimizer for hyperspectral band selection. Appl Soft Comput J 40:178–186. https://doi.org/10.1016/j.asoc.2015.09.045
Melgani F, Bruzzone L (2004) Classification Hyperspect Remote Sens 42:1778–1790
Manoharan Prabukumar, Shrutika Sawant, Sathishkumar Samiappan, Loganathan Agilandeeswari(2018) Three-dimensional discrete cosine transform-based feature extraction for hyperspectral image classification, J. Appl. Remote Sens. 12(4):046010 (2018). https://doi.org/10.1117/1.JRS.12.046010
Prabukumar M, Sawant SS (2018) Band clustering using expectation – maximization algorithm and weighted average fusion-based feature extraction for hyperspectral image classification. J Appl Remote Sens 12. https://doi.org/10.1117/1.JRS.12.
Quan Q, He F, Li H (2020) A multi-phase blending method with incremental intensity for training detection networks. Vis Comput. https://doi.org/10.1007/s00371-020-01796-7
Sawant SS, Manoharan P (2019) New framework for hyperspectral band selection using modified wind-driven optimization algorithm. Int J Remote Sens 00:1–22. https://doi.org/10.1080/01431161.2019.1607609
Sawant SS, Manoharan P (2020) Unsupervised band selection based on weighted information entropy and 3D discrete cosine transform for hyperspectral image classification. Int J Remote Sens 41:3948–3969. https://doi.org/10.1080/01431161.2019.1711242
Sawant SS, Prabukumar M (2017) Semi-supervised techniques based hyper-spectral image classification: a survey, in: 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, 2017, pp 1-8. https://doi.org/10.1109/IPACT.2017.8244999
Sawant Shrutika, Manoharan Prabukumar (2020) A Review on Graph-Based Semi-Supervised Learning Methods for Hyperspectral Image Classification, The Egyptian Journal of Remote Sensing and Space Sciences, 23: 243-248. https://doi.org/10.1016/j.ejrs.2018.11.001
Sawant S, Prabukumar M (2020) A survey of band selection techniques for hyperspectral image classification. J Spectr Imaging 1:1–18. https://doi.org/10.1255/jsi.2020.a5
Sawant S, Manoharan P, Samiappan S (2018) Ranking and Grouping based Feature Selection for Hyperspectral Image Classification, Proceedings Asian Conference on Remote Sensing, pp. 2305–2313
Sawant S, Manoharan P, Samiappan S (2019) A band selection method for hyperspectral image classification based on cuckoo search algorithm with correlation based in- itialization,in: 10th workshop on Hyperspectral imaging and signal processing: Evolution in remote sensing (WHISPERS), Amsterdam, Netherlands, pp. 1–4.
Sawant S, Prabukumar M, Samiappan S (2020) A modified cuckoo search algorithm based optimal band subset selection approach for hyperspectral image classification. J Spectr Imaging 1:1–20. https://doi.org/10.1255/jsi.2020.a6
Science N, Phenomena C, Walton S, Hassan O, Morgan K, Brown MR (2011) Chaos , Solitons & Fractals Modified cuckoo search : A new gradient free optimisation algorithm, Chaos, Solitons Fractals Interdiscip. J Nonlinear Sci Nonequilibrium Complex Phenom 44:710–718. https://doi.org/10.1016/j.chaos.2011.06.004
Senthil V, Ganesan K, Vasuki S (2018) Maximin distance based band selection for endmember extraction in hyperspectral images using simplex growing algorithm, 7221–7237. https://doi.org/10.1007/s11042-017-4630-0
Su H, Du Q, Chen G, Du P (2014) Optimized hyperspectral band selection using particle swarm optimization. IEEE J Sel Top Appl Earth Obs Remote Sens 7:2659–2670. https://doi.org/10.1109/JSTARS.2014.2312539
Su H, Cai Y, Du Q (2017) Firefly-algorithm-inspired framework with band selection and extreme learning machine for Hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 10:309–320. https://doi.org/10.1109/JSTARS.2016.2591004
Sui C, Tian Y, Xu Y, Xie Y (2015) Unsupervised band selection by integrating the overall accuracy and redundancy. IEEE Geosci Remote Sens Lett 12:185–189. https://doi.org/10.1109/LGRS.2014.2331674
Tavazoei MS, Haeri M (2007) Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms, 187: 1076–1085. https://doi.org/10.1016/j.amc.2006.09.087
Taylor P (2013) Hybrid genetic algorithm for feature selection with hyperspectral data 37–41. https://doi.org/10.1080/2150704X.2013.777485.
Tschannerl J, Ren J, Yuen P, Sun G, Zhao H, Yang Z, Wang Z, Marshall S (2019) MIMR-DGSA : unsupervised hyperspectral band selection based on information theory and a modified discrete gravitational search algorithm. Inf Fusion 51:189–200. https://doi.org/10.1016/j.inffus.2019.02.005
Vaddi R, Manoharan P (2020) Probabilistic PCA based hyperspectral image Classification for remote sensing applications. Springer International Publishing, Berlin. https://doi.org/10.1007/978-3-030-16660-1
Vaddi R, Manoharan P (2020) Hyperspectral image classification using CNN with spectral and spatial features integration. Infrared Phys Technol 107:103296. https://doi.org/10.1016/j.infrared.2020.103296
Vaddi R, Manoharan P (2020) CNN based hyperspectral image classification using unsupervised band selection and structure-preserving spatial features, Infrared Phys Technol 10:103457. https://doi.org/10.1016/j.infrared.2020.103457
Veera Senthil Kumar G, Vasuki S (2017) Clustering based band selection for endmember extraction using simplex growing algorithm in hyperspectral images, 8355–8371. https://doi.org/10.1007/s11042-016-3420-4
Wang Q, S. Member, Lin J, S. Member, Yuan Y, S. Member (2016) Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking 27: 1279–1289.
Xie F, Li F, Lei C, Yang J, Zhang Y (2019) Unsupervised band selection based on artificial bee colony algorithm for hyperspectral image classification. Appl Soft Comput J 75:428–440. https://doi.org/10.1016/j.asoc.2018.11.014
Xu M, Sun Q, He Z, Shi J (2016) Band selection for hyperspectral images based on particle swarm optimization and differential evolution algorithms with hybrid encoding, 16: 629–640. https://doi.org/10.3233/JCM-160645
Yang D, Li G, Cheng G (2007) On the efficiency of chaos optimization algorithms for global optimization, 34: 1366–1375. https://doi.org/10.1016/j.chaos.2006.04.057
Yang X, Deb S, A.C.B. Behaviour (2009) Cuckoo Search via Lévy Flights 210–214
H. Yu, F. He, Pan Y (2019) A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation
Zhang W, Li X, Zhao L (2018) A fast Hyperspectral feature selection method based on band correlation analysis. IEEE Geosci Remote Sens Lett PP 15:1–5. https://doi.org/10.1109/LGRS.2018.2853805
Zhang J, He F, Chen Y (2020) A new haze removal approach for sky / river alike scenes based on external and internal clues, 2085–2107
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The authors thank the Council of Scientific & Industrial Research (CSIR), New Delhi, India for the award of CSIR-SRF and VIT for providing a VIT seed grant for carrying out this research work.
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Sawant, S., Manoharan, P. A hybrid optimization approach for hyperspectral band selection based on wind driven optimization and modified cuckoo search optimization. Multimed Tools Appl 80, 1725–1748 (2021). https://doi.org/10.1007/s11042-020-09705-9
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DOI: https://doi.org/10.1007/s11042-020-09705-9