An opposition equilibrium optimizer for context-sensitive entropy dependency based multilevel thresholding of remote sensing images

Haindl, 2016, A competition in unsupervised color image segmentation, Pattern Recognit., 57, 136, 10.1016/j.patcog.2016.03.003 GIS (Geographic Information System) | National Geographic Society, (n.d.). https://www.nationalgeographic.org/encyclopedia/geographic-information-system-gis/ (accessed August 9, 2020). Bhandari, 2014, Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy, Expert Syst. Appl., 41, 3538, 10.1016/j.eswa.2013.10.059 Bhandari, 2016, A novel color image multilevel thresholding based segmentation using nature inspired optimization algorithms, Expert Syst. Appl., 63, 112, 10.1016/j.eswa.2016.06.044 Bhandari, 2015, Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms, Expert Syst. Appl. Jia, 2019, Masi Entropy for satellite color image segmentation using tournament-based lévy multiverse optimization algorithm, Remote Sens., 11, 942, 10.3390/rs11080942 Sankur, 2001, Image thresholding techniques: a survey over categories, Pattern Recognit., 34, 1573 Zaitoun, 2015, Survey on image segmentation techniques, Procedia Comput. Sci., 65, 797, 10.1016/j.procs.2015.09.027 Sezgin, 2004, Survey over image thresholding techniques and quantitative performance evaluation, J. Electron. Imaging, 13, 146, 10.1117/1.1631315 Otsu, 1979, IEEE Trans. Syst. Man. Cybern. C, 62, 10.1109/TSMC.1979.4310076 Portes de Albuquerque, 2004, Image thresholding using Tsallis entropy, Pattern Recognit. Lett., 25, 1059, 10.1016/j.patrec.2004.03.003 Kapur, 1985, A new method for gray-level picture thresholding using the entropy of the histogram, Comput. Vision, Graph. Image Process., 29, 273, 10.1016/0734-189X(85)90125-2 Masi, 2005, A step beyond Tsallis and Rényi entropies, Phys. Lett. Sect. A Gen. At. Solid State Phys., 338, 217 A. Renyi, On Measures of Entropy and Information, in: Proc. Fourth Berkeley Symp. Math. Stat. Probab. Vol. 1 Contrib. to Theory Stat., University of California Press, Berkeley, Calif., 1961: pp. 547–561. https://projecteuclid.org/euclid.bsmsp/1200512181. Pal, 1996, On minimum cross-entropy thresholding, Pattern Recognit., 29, 575, 10.1016/0031-3203(95)00111-5 Wunnava, 2020, A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer, Eng. Appl. Artif. Intell., 94, 10.1016/j.engappai.2020.103836 Abutaleb, 1989, Automatic thresholding of gray-level pictures using two-dimensional entropy, Comput. Vision, Graph. Image Process., 47, 22, 10.1016/0734-189X(89)90051-0 J. Liu, W. Li, Y. Tian, Automatic thresholding of gray-level pictures using two-dimensional Otsu method, (1991) 325–327. Sahoo, 2004, A thresholding method based on two-dimensional Renyi’s entropy, Pattern Recognit., 37, 1149, 10.1016/j.patcog.2003.10.008 Sahoo, 2006, Image thresholding using two-dimensional Tsallis–Havrda–Charvát entropy, Pattern Recognit. Lett., 27, 520, 10.1016/j.patrec.2005.09.017 Wunnava, 2020, A differential evolutionary adaptive Harris hawks optimization for two dimensional practical Masi entropy-based multilevel image thresholding, J. King Saud Univ. - Comput. Inf. Sci. Panda, 2017, An evolutionary gray gradient algorithm for multilevel thresholding of brain MR images using soft computing techniques, Appl. Soft Comput., 50, 94, 10.1016/j.asoc.2016.11.011 Wunnava, 2020, An adaptive Harris hawks optimization technique for two dimensional grey gradient based multilevel image thresholding, Appl. Soft Comput., 95, 10.1016/j.asoc.2020.106526 Patra, 2014, A novel context sensitive multilevel thresholding for image segmentation, Appl. Soft Comput., 23, 122, 10.1016/j.asoc.2014.06.016 Pare, 2016, A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve, Appl. Soft Comput., 47, 76, 10.1016/j.asoc.2016.05.040 Pare, 2017, An optimal color image multilevel thresholding technique using grey-level co-occurrence matrix, Expert Syst. Appl., 87, 335, 10.1016/j.eswa.2017.06.021 Díaz-Cortés, 2018, A multi-level thresholding method for breast thermograms analysis using Dragonfly algorithm, Infrared Phys. Technol., 93, 346, 10.1016/j.infrared.2018.08.007 Kandhway, 2020, Spatial context-based optimal multilevel energy curve thresholding for image segmentation using soft computing techniques, Neural Comput. Appl., 32, 8901, 10.1007/s00521-019-04381-9 A. Faramarzi, M. Heidarinejad, B. Stephens, S. Mirjalili, Equilibrium optimizer: A novel optimization algorithm, Knowledge-Based Syst. 191 (2020) 105190. https://doi.org/https://doi.org/ 10.1016/j.knosys.2019.105190. Tizhoosh, 2005, Opposition-Based Learning: A New Scheme for Machine Intelligence, Int. Conf. Comput. Intell. Model. Control Autom. Int. Conf. Intell. Agents, Web Technol. Internet Commer., 695 Dhargupta, 2020, Selective Opposition based Grey Wolf Optimization, Expert Syst. Appl., 151, 10.1016/j.eswa.2020.113389 Yao, 1999, Evolutionary programming made faster, Evol. Comput. IEEE Trans., 3, 82, 10.1109/4235.771163 Naik, 2016, A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition, Appl. Soft Comput., 38, 661, 10.1016/j.asoc.2015.10.039 J.J. Liang, B.Y. Qu, P.N. Suganthan, Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization, in: 2013. Simon, 2009, Biogeography-Based Optimization, Evol. Comput. IEEE Trans., 12, 702, 10.1109/TEVC.2008.919004 Heidari, 2019, Harris hawks optimization: Algorithm and applications, Futur. Gener. Comput. Syst., 97, 849, 10.1016/j.future.2019.02.028 Shadravan, 2019, The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems, Eng. Appl. Artif. Intell., 80, 20, 10.1016/j.engappai.2019.01.001 Mirjalili, 2016, The Whale Optimization Algorithm, Adv. Eng. Softw., 95, 51, 10.1016/j.advengsoft.2016.01.008 Mirjalili, 2014, Grey Wolf Optimizer, Adv. Eng. Softw., 69, 46, 10.1016/j.advengsoft.2013.12.007 Tanabe, 2014, Improving the search performance of SHADE using linear population size reduction, 1658 Carrasco, 2020, Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review, Swarm Evol. Comput., 54, 10.1016/j.swevo.2020.100665 Zar, 1999 Holm, 1979, A Simple Sequentially Rejective Multiple Test Procedure, Scand, J. Stat., 6, 65 Landsat Image Gallery, (n.d.). https://landsat.visibleearth.nasa.gov/ (accessed June 12, 2020). Agrawal, 2013, Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm, Swarm Evol. Comput., 11, 16, 10.1016/j.swevo.2013.02.001 Lin Zhang, 2011, FSIM: A Feature Similarity Index for Image Quality Assessment, IEEE Trans. Image Process., 20, 2378, 10.1109/TIP.2011.2109730 Zhou, 2004, Image quality assessment: from error visibility to structural similarity, IEEE Trans. Image Process., 13, 600, 10.1109/TIP.2003.819861 Ghosh, 2007, A Context-Sensitive Technique for Unsupervised Change Detection Based on Hopfield-Type Neural Networks, IEEE Trans. Geosci. Remote Sens., 45, 778, 10.1109/TGRS.2006.888861 Halim, 2020, Performance assessment of the metaheuristic optimization algorithms: an exhaustive review, Artif. Intell. Rev. Liu, 2013, A parameter control method of evolutionary algorithms using exploration and exploitation measures with a practical application for fitting Sovova's mass transfer model, Appl. Soft Comput., 13, 3792, 10.1016/j.asoc.2013.05.010