Multi-objective multi-verse optimizer based unsupervised band selection for hyperspectral image classification

References

• Abdel-Basset, M., D. El-Shahat, H. Faris, and S. Mirjalili. 2019. “Computers & Industrial Engineering a Binary Multi-Verse Optimizer for 0-1 Multidimensional Knapsack Problems with Application in Interactive Multimedia Systems.” Computers & Industrial Engineering 132: 187–206. doi:10.1016/j.cie.2019.04.025.
   Web of Science ®Google Scholar
• Amigo, J., H. Babamoradi, and S. Elcoroaristizabal. 2015. ”Hyperspectral Image Analysis. A Tutorial.” Analytica Chimica Acta 10.1016/j.aca.2015.09.030.
   PubMed Web of Science ®Google Scholar
• Bioucas-Dias, J., A. Plaza, G. Camps-Valls, P. Scheunders, N.M. Nasrabadi, and J. Chanussot. 2013. “Hyperspectral Remote Sensing Data Analysis and Future Challenges.” Geoscience and Remote Sensing Magazine, IEEE 1 (2): 6–36. doi:10.1109/MGRS.2013.2244672.
   Web of Science ®Google Scholar
• Boggavarapu, L. N. P. and M. Prabukumar. 2017. “Survey on Classification Methods for Hyper Spectral Remote Sensing Imagery.” 2017 International Conference On Intelligent Computing And Control Systems Tamilnadu 538–542. doi:10.1109/ICCONS.2017.8250520.
   Google Scholar
• Boggavarapu, L. N. P and M. Prabukumar. 2020a. “Hyperspectral Image Classification Using Fuzzy- Embedded Hyperbolic Sigmoid Nonlinear Principal Component and Weighted Least Squares Approach.” Journal of Applied Remote Sensing 14: 1. doi:10.1117/1.JRS.14.024501.
   Google Scholar
• Boggavarapu, L. N. P and M. Prabukumar. 2020b. “A New Framework for Hyperspectral Image Classification Using Gabor Embedded Patch Based Convolution Neural Network.” Infrared Physics & Technology 110: 103455. doi:10.1016/j.infrared.2020.103455.
   Web of Science ®Google Scholar
• Boggavarapu, L. N. P and M. Prabukumar. 2020c. Classification of Hyper Spectral Remote Sensing Imagery Using Intrinsic Parameter Estimation. 852–862. Cham: Springer. 10.1007/978-3-030-16660-1_83.
   Google Scholar
• Bousmaha, R., M. Hamou, and A. Amine. 2021. “Automatic Selection of Hidden Neurons and Weights in Neural Networks for Data Classification Using Hybrid Particle Swarm Optimization, Multi-Verse Optimization Based on Lévy Flight.” Evolutionary Intelligence. doi:10.1007/s12065-021-00579-w.
   Web of Science ®Google Scholar
• Bruce, L., C. Koger, and J. Li. 2002. “Dimensionality Reduction of Hyperspectral Data Using Discrete Wavelet Transform Feature Extraction.” Geoscience and Remote Sensing, IEEE Transactions On 40 (10): 2331–2338. doi:10.1109/TGRS.2002.804721.
   Web of Science ®Google Scholar
• Cao, X., B. Wu, D. Tao, and L. Jiao. 2016. “Automatic Band Selection Using Spatial-Structure Information and Classifier-Based Clustering.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (9): 1–9. doi:10.1109/JSTARS.2015.2509461.
   Web of Science ®Google Scholar
• Dou, Z., K. Gao, and X. Zhang. 2020. “Band Selection of Hyperspectral Images Using Attention-Based Autoencoders.” IEEE Geoscience and Remote Sensing Letters, 10.1109/LGRS.2020.2967815.
   Google Scholar
• Elsayed Abd Elaziz, M., D. Oliva, and A. Ewees. 2019. “Multi-Level Thresholding-Based Grey Scale Image Segmentation Using Multi-Objective Multi-Verse Optimizer.” Expert Systems with Applications 125. doi:10.1016/j.eswa.2019.01.047.
   Web of Science ®Google Scholar
• Emmerich, M. and A. Deutz. 2018. “A Tutorial on Multiobjective Optimization: Fundamentals and Evolutionary Methods.” Natural Computing 3: 17. doi:10.1007/s11047-018-9685-y.
   Web of Science ®Google Scholar
• Feng, J., J. Chen, Q. Sun, R. Shang, X. Cao, X. Zhang, and L. Jiao. 2020. “Convolutional Neural Network Based on Bandwise-Independent Convolution and Hard Thresholding for Hyperspectral Band Selection.” IEEE Transactions on Cybernetics, 1–15. 10.1109/TCYB.2020.3000725.
   Google Scholar
• Feng, J., D. Li, J. Gu, X. Cao, R. Shang, X. Zhang, and L. Jiao. 2021. “Deep Reinforcement Learning for Semisupervised Hyperspectral Band Selection.” IEEE Transactions on Geoscience and Remote Sensing, 1–19. 10.1109/TGRS.2021.3049372.
   Google Scholar
• Feng, J., Z. Ye, S. Liu, X. Zhang, J. Chen, R. Shang, and L. Jiao. 2021. “Dual-Graph Convolutional Network Based on Band Attention and Sparse Constraint for Hyperspectral Band Selection.” Knowledge-Based Systems 231: 107428. doi:10.1016/j.knosys.2021.107428.
   Web of Science ®Google Scholar
• Gao, P., J. Wang, H. Zhang, and Z. Li 2018. “Boltzmann Entropy-Based Unsupervised Band Selection for Hyperspectral Image Classification.” IEEE Geoscience and Remote Sensing Letters., 1–5. 10.1109/LGRS.2018.2872358.
   Google Scholar
• Gong, M., M. Zhang, and Y. Yuan. 2015. ”Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images.” IEEE Transactions on Geoscience and Remote Sensing 54. 1–1. doi:10.1109/TGRS.2015.2461653.
   Web of Science ®Google Scholar
• Graña, M., M.A. Veganzons, and B. Ayerdi. Hyperspectral_Remote_Sensing_Scenes [Internet]. http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes
   Google Scholar
• Hughes, G.F. 1968. “On the Mean Accuracy of Statistical Pattern Recognizers.” IEEE Transactions on Information Theory 14 (1): 55–63. doi:10.1109/TIT.1968.1054102.
   Web of Science ®Google Scholar
• Jia, S., G. Tang, J. Zhu, and Q. Li. 2015. “A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection.” IEEE Transactions on Geoscience and Remote Sensing 54: 1–15. doi:10.1109/TGRS.2015.2450759.
   Web of Science ®Google Scholar
• Jiang, X., J. Lin, J. Liu, S. Li, and Y. Zhang. 2018. “A Coarse-To-Fine Optimization for Hyperspectral Band Selection.” IEEE Geoscience and Remote Sensing Letters., 1–5. 10.1109/LGRS.2018.2878033.
   Google Scholar
• Kandhway, P. and A.K. Bhandari. 2019. “Spatial Context Cross Entropy Function Based Multilevel Image Segmentation Using Multi-Verse Optimizer.” Multimedia Tools and Applications 78: 22613–22641. doi:10.1007/s11042-019-7506-7.
   Web of Science ®Google Scholar
• Kwan, C., B. Ayhan, B. Budavari, Y. Lu, D. Perez, J. Li, S. Bernabé, and A. Plaza. 2020. ”Deep Learning for Land Cover Classification Using Only a Few Bands”. Remote Sensing 12 : 200010.3390/rs12122000.
   Web of Science ®Google Scholar
• Li, W., F. Feng, H. Li, and Q. Du. 2018. “Discriminant Analysis-Based Dimension Reduction for Hyperspectral Image Classification: A Survey of the Most Recent Advances and an Experimental Comparison of Different Techniques.” IEEE Geoscience and Remote Sensing Magazine 6: 15–34. doi:10.1109/MGRS.2018.2793873.
   Web of Science ®Google Scholar
• Liu, Q., Y. Sun, R. Hang, and H. Song. 2017. “Spatial–spectral Locality-Constrained Low-Rank Representation with Semi-Supervised Hypergraph Learning for Hyperspectral Image Classification.” In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1–12. doi:10.1109/JSTARS.2017.2700490.
   Google Scholar
• Lorencs, A., I. Mednieks, and Siņica-Siņavskis J. 2018. “Selection of Informative Hyperspectral Band Subsets Based on Entropy and Correlation.” International Journal of Remote Sensing 39 (20): 1–18. doi:10.1080/01431161.2018.1468107.
   Web of Science ®Google Scholar
• Luo, X., R. Xue, and J. Yin. 2017. “Information-Assisted Density Peak Index for Hyperspectral Band Selection.”In IEEE Geoscience and Remote Sensing Letters, 1–5. doi:10.1109/LGRS.2017.2741494.
   Google Scholar
• Ma, L., M. Crawford, X. Yang, and Y. Guo. 2015. “Local-Manifold-Learning-Based Graph Construction for Semisupervised Hyperspectral Image Classification.” In IEEE Transactions on Geoscience and Remote Sensing, 53. doi:10.1109/TGRS.2014.2365676.
   Google Scholar
• Mafarja, M. and S. Mirjalili. 2017. “Whale Optimization Approaches for Wrapper Feature Selection.” Applied Soft Computing 62. doi:10.1016/j.asoc.2017.11.006.
   Web of Science ®Google Scholar
• Martínez-Usó, A., F. Pla, J. Sotoca, and P. García-Sevilla. 2008. “Clustering-Based Hyperspectral Band Selection Using Information Measures.” Geoscience and Remote Sensing, IEEE Transactions On 45 (12): 4158–4171. doi:10.1109/TGRS.2007.904951.
   Web of Science ®Google Scholar
• Medjahed, S.A., T. Saadi, A. Benyettou, and M. Ouali. 2015. “Gray Wolf Optimizer for Hyperspectral Band Selection.” Applied Soft Computing 40. doi:10.1016/j.asoc.2015.09.045.
   Web of Science ®Google Scholar
• Melgani, F. and L. Bruzzone. 2004. “Classification of Hyperspectral Remote Sensing Images with Support Vector Machines.” Geoscience and Remote Sensing, IEEE Transactions On 42 (8): 1778–1790. doi:10.1109/TGRS.2004.831865.
   Web of Science ®Google Scholar
• Mirjalili, S., P. Jangir, S.Z. Mirjalili, S. Saremi, and I. Trivedi. 2017. “Optimization of Problems with Multiple Objectives Using the Multi-Verse Optimization Algorithm.” Knowledge-Based Systems 134. doi:10.1016/j.knosys.2017.07.018.
   Web of Science ®Google Scholar
• Pan, B., Z. Shi, and X. Xu. 2018. “Multiobjective-Based Sparse Representation Classifier for Hyperspectral Imagery Using Limited Samples.” IEEE Transactions on Geoscience and Remote Sensing, 1–11. 10.1109/TGRS.2018.2853268.
   Google Scholar
• Pan, B., Z. Shi, and X. Xu. 2019. “Analysis for the Weakly Pareto Optimum in Multiobjective-Based Hyperspectral Band Selection.” IEEE Transactions on Geoscience and Remote Sensing, 1–12. 10.1109/TGRS.2018.2886853.
   Google Scholar
• Prabukumar, M. and S. Sawant. 2018. “Plankton Aerosol, Cloud, Ocean Ecosystem Mission: Atmosphere Measurements for Air Quality Applications.” Journal of Applied Remote Sensing 12 (4): 1. doi:10.1117/1.JRS.12.046015.
   Web of Science ®Google Scholar
• Prabukumar, M., S. Sawant, S. Samiappan, and A. Loganathan. 2018. “Plankton Aerosol, Cloud, Ocean Ecosystem Mission: Atmosphere Measurements for Air Quality Applications.” Journal of Applied Remote Sensing 12 (4): 1. doi:10.1117/1.JRS.12.046010.
   Web of Science ®Google Scholar
• Sawant, S., M. Prabukumar, and S. Samiappan. 2018. “Ranking and Grouping Based Feature Selection for Hyperspectral Image Classification.” Conference: 39th Asian Conference on Remote Sensing (ACRS), Kuala Lumpur, Malaysia.
   Google Scholar
• Sawant, S. and M. Prabukumar. 2018. “A Review on Graph-Based Semi-Supervised Learning Methods for Hyperspectral Image Classification.” The Egyptian Journal of Remote Sensing and Space Science 23. doi:10.1016/j.ejrs.2018.11.001.
   Web of Science ®Google Scholar
• Sawant, S. and M. Prabukumar. 2019. “New Framework for Hyperspectral Band Selection Using Modified Wind-Driven Optimization Algorithm.” International Journal of Remote Sensing 40 (20): 1–22. doi:10.1080/01431161.2019.1607609.
   Web of Science ®Google Scholar
• Sawant, S., M. Prabukumar, and S. Samiappan. 2019. A Band Selection Method for Hyperspectral Image Classification Based on Cuckoo Search Algorithm with Correlation Based Initialization. 1–4. Netherlands: Amsterdam. 10.1109/WHISPERS.2019.8920950.
   Google Scholar
• Sawant, S., M. Prabukumar, and S. Samiappan. 2020. “A Modified Cuckoo Search Algorithm Based Optimal Band Subset Selection Approach for Hyperspectral Image Classification.” Journal of Spectral Imaging. doi:10.1255/jsi.2020.a6.
   Google Scholar
• Sawant, S. and M. Prabukumar. 2020a. “A Survey of Band Selection Techniques for Hyperspectral Image Classification.” Journal of Spectral Imaging 9. doi:10.1255/jsi.2020.a5.
   Google Scholar
• Sawant, S. and M. Prabukumar. 2020b. “Unsupervised Band Selection Based on Weighted Information Entropy and 3D Discrete Cosine Transform for Hyperspectral Image Classification.” International Journal of Remote Sensing 41 (10): 3948–3969. doi:10.1080/01431161.2019.1711242.
   Web of Science ®Google Scholar
• Sawant, S. and M. Prabukumar. 2020c. “Hyperspectral Band Selection Based on Metaheuristic Optimization Approach.” Infrared Physics & Technology 107: 103295. doi:10.1016/j.infrared.2020.103295.
   Web of Science ®Google Scholar
• Sawant, S. and M. Prabukumar. 2021. “A Hybrid Optimization Approach for Hyperspectral Band Selection Based on Wind Driven Optimization and Modified Cuckoo Search Optimization.” Multimedia Tools and Applications 80 (2): 1–24. doi:10.1007/s11042-020-09705-9.
   Web of Science ®Google Scholar
• Seyedali, M., M. Seyed, and A. Hatamlou. 2015. “Multi-Verse Optimizer: A Nature-Inspired Algorithm for Global Optimization.” Neural Computing & Applications 27. doi:10.1007/s00521-015-1870-7.
   Web of Science ®Google Scholar
• Su, H., Q. Du, G. Chen, and P. Du. 2014. “Optimized Hyperspectral Band Selection Using Particle Swarm Optimization.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (6): 2659–2670. doi:10.1109/JSTARS.2014.2312539.
   Web of Science ®Google Scholar
• Sui, C., J. Zhou, C. Li, Q. Zheng, J. Feng, X. Mei, and J. Wang 2021. “Unsupervised Hyperspectral Band Selection with Multi-Graph Integrated Embedding and Robust Self-Contained Regression.” IEEE Transactions on Geoscience and Remote Sensing, 10.1109/TGRS.2021.3068779.
   Google Scholar
• Sun, V., X. Geng, L. Ji, and Y. Lu. 2014. “A New Band Selection Method for Hyperspectral Image Based on Data Quality. Selected Topics in Applied Earth Observations and Remote Sensing.” IEEE Journal Of 7: 2697–2703. doi:10.1109/JSTARS.2014.2320299.
   Web of Science ®Google Scholar
• Vaddi, R. and M. Prabukumar. 2017. “Comparative Study of Feature Extraction Techniques for Hyper Spectral Remote Sensing Image Classification : A Survey.” 2017 International Conference On Intelligent Computing And Control Systems, 543–548. Tamilnadu. doi:10.1109/ICCONS.2017.8250521.
   Google Scholar
• Vaddi, R. and M. Prabukumar. 2020a. “CNN Based Hyperspectral Image Classification Using Un-Supervised Band Selection and Structure-Preserving Spatial Features.” Infrared Physics & Technology 110: 103457. doi:10.1016/j.infrared.2020.103457.
   Web of Science ®Google Scholar
• Vaddi, R. and M. Prabukumar. 2020b. Probabilistic PCA Based Hyper Spectral Image Classification for Remote Sensing Applications. 863–869. Cham: Springer. 10.1007/978-3-030-16660-1_84.
   Google Scholar
• Veldhuizen, D. and G. Lamont. 1999. “Evolutionary Computation and Convergence to a Pareto Front.” Late Breaking Papers at the Genetic Programming 1998 Conference, University of Wisconsin in Madison, Wisconsin.
   Google Scholar
• Villa, A., J. Benediktsson, J. Chanussot, and C. Jutten. 2012. “Hyperspectral Image Classification with Independent Component Discriminant Analysis.” Geoscience and Remote Sensing, IEEE Transactions On 49 (12): 4865–4876. doi:10.1109/TGRS.2011.2153861.
   Web of Science ®Google Scholar
• Wolpert, D. and W. Macready. 1997. “No Free Lunch Theorems for Optimization.” Evolutionary Computation, IEEE 1 (1): 67–82. doi:10.1109/4235.585893.
   Google Scholar
• Xie, F., F. Li, C. Lei, and L. Ke. 2018. “Representative Band Selection for Hyperspectral Image Classification.” ISPRS International Journal of Geo-Information 7 (9): 338. doi:10.3390/ijgi7090338.
   Web of Science ®Google Scholar
• Xu, X., Z. Shi, and B. Pan. 2017. “A New Unsupervised Hyperspectral Band Selection Method Based on Multiobjective Optimization.” IEEE Geoscience and Remote Sensing Letters 14: 1–5. doi:10.1109/LGRS.2017.2753237.
   Web of Science ®Google Scholar
• Yuan, Y., J. Lin, and Q. Wang. 2015. “Dual-Clustering-Based Hyperspectral Band Selection by Contextual Analysis.” IEEE Transactions on Geoscience and Remote Sensing 54: 1–15. doi:10.1109/TGRS.2015.2480866.
   Web of Science ®Google Scholar
• Zhang, M., M. Gong, and Y. Chan. 2018. “Hyperspectral Band Selection Based on Multi-Objective Optimization with High Information and Low Redundancy.” Applied Soft Computing 70. doi:10.1016/j.asoc.2018.06.009.
   Web of Science ®Google Scholar
• Zhang, W., X. Li, and L. Zhao. 2019. “Discovering the Representative Subset with Low Redundancy for Hyperspectral Feature Selection.” Remote Sensing 11 (11): 1341. doi:10.3390/rs11111341.
   Web of Science ®Google Scholar
• Zhu, Z., S. Jia, S. He, Y. Sun, Z. Ji, and L. Shen. 2015. “Three-Dimensional Gabor Feature Extraction for Hyperspectral Imagery Classification Using a Memetic Framework.” Information Sciences 298: 274–287. doi:10.1016/j.ins.2014.11.045.
   Web of Science ®Google Scholar