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A band selection approach based on wavelet support vector machine ensemble model and membrane whale optimization algorithm for hyperspectral image

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Abstract

Hyperspectral Image (HSI) has become one of the important remote sensing sources for object interpretation by its abundant band information. Among them, band selection is considered as the main theme in HSI classification to reduce the data dimension, and it is a combinatorial optimization problem and difficult to be completely solved by previous techniques. Whale Optimization Algorithm (WOA) is a newly proposed swarm intelligence algorithm that imitates the predatory strategy of humpback whales, and membrane computing is able to decompose the band information into a series of elementary membranes that decreases the coding length. In addition, Support Vector Machine (SVM) combined with wavelet kernel is adapted to HSI datasets with high dimension and small samples, ensemble learning is an effective tool that synthesizes multiple sub-classifiers to solve the same problem and obtains accurate category label for each sample. In the paper, a band selection approach based on wavelet SVM (WSVM) ensemble model and membrane WOA (MWOA) is proposed, experimental results indicate that the proposed HSI classification technique is superior to other corresponding and newly proposed methods, achieves the optimal band subset with a fast convergence speed, and the overall classification accuracy has reached 93% for HSIs.

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References

  1. Amigo JM, Babamoradi H, Elcoroaristizabal S (2015) Hyperspectral image analysis: A tutorial. Analytica Chimica Acta 896:34–51

    Article  Google Scholar 

  2. Veraverbeke S, Dennison P, Gitas I, et al. (2018) Hyperspectral remote sensing of fire: State-of-the-art and future perspectives. Remote Sens Environ 216:105–121

    Article  Google Scholar 

  3. Imani M, Ghassemian H (2020) An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges. Inform Fusion 59:59–83

    Article  Google Scholar 

  4. Tu B, Zhang X, Kang X, et al. (2019) Spatial density peak clustering for hyperspectral image classification with noisy labels. IEEE Trans Geosci Remote Sens 57:5085–5097

    Article  Google Scholar 

  5. Hufnagl B, Lohninger H. (2020) A graph-based clustering method with special focus on hyperspectral imaging. Anal Chim Acta 1097:37–48

    Article  Google Scholar 

  6. El Rahman S.A. (2015) Hyperspectral imaging classification using ISODATA algorithm: Big data challenge. International Conference on e-Learning. IEEE, 247–250

  7. Sumarsono A, Du Q. (2016) Low-rank subspace representation for supervised and unsupervised classification of hyperspectral imagery. IEEE J Sel Top Appl Earth Obs Remote Sens 9:4188–4195

    Article  Google Scholar 

  8. Liu C, Li J, He L. (2018) Superpixel-based semisupervised active learning for hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 12:357–370

    Google Scholar 

  9. Haut JM, Paoletti ME, Plaza J, et al. (2018) Active learning with convolutional neural networks for hyperspectral image classification using a new Bayesian approach. IEEE Trans Geosci Remote Sens 56:6440–6461

    Article  Google Scholar 

  10. Paoletti ME, Haut JM, Plaza J, et al. (2018) A new deep convolutional neural network for fast hyperspectral image classification. ISPRS J Photogramm Remote Sens 145:120–147

    Article  Google Scholar 

  11. Sun G, Chen T, Su Y, et al. (2018) Internet traffic classification based on incremental support vector machines. Mobile Netw Appl 23:789–796

    Article  Google Scholar 

  12. Liu R, Yang B, Zio E, et al. (2018) Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech Syst Signal Process 108:33–47

    Article  Google Scholar 

  13. Jain DK, Dubey SB, Choubey RK, et al. (2018) An approach for hyperspectral image classification by optimizing SVM using self organizing map. J Comput Sci 25:252–259

    Article  Google Scholar 

  14. Liu L, Huang W, Liu B, et al. (2018) Semisupervised hyperspectral image classification via Laplacian least squares support vector machine in sum space and random sampling. IEEE J Sel Top Appl Earth Obs Remote Sens 11:4086–4100

    Article  Google Scholar 

  15. Khodadadzadeh M, Ghamisi P, Contreras C, et al. (2018) Subspace multinomial logistic regression ensemble for classification of hyperspectral images. International Geoscience and Remote Sensing Symposium. IEEE, 5740–5743

  16. Krawczyk B, Minku LL, Gama J, et al. (2017) Ensemble learning for data stream analysis: a survey. Inform Fusion 37:132– 156

    Article  Google Scholar 

  17. Sun J, Li H, Fujita H, et al. (2020) Class-imbalanced dynamic financial distress prediction based on adaboost-SVM ensemble combined with SMOTE and time weighting. Inform Fusion 54:128–144

    Article  Google Scholar 

  18. Lin J, Chen H, Li S, et al. (2019) Accurate prediction of potential druggable proteins based on genetic algorithm and bagging-SVM ensemble classifier. Artif Intell Med 98:35–47

    Article  Google Scholar 

  19. Feng W, Huang W, Bao W. (2019) Imbalanced hyperspectral image classification with an adaptive ensemble method based on SMOTE and rotation forest with differentiated sampling rates. IEEE Geosci Remote Sens Lett 16:1879–1883

    Article  Google Scholar 

  20. Li Y, Xie T, Wang P, et al. (2018) Joint spectral-spatial hyperspectral image classification based on hierarchical subspace switch ensemble learning algorithm. Appl Intell 48:4128–4148

    Article  Google Scholar 

  21. Falco N, Xia J, Kang X, et al. (2020) Supervised classification methods in hyperspectral imaging—recent advances. Data Handling in Science and Technology. Elsevier, 32, 247–279

  22. Bilal M, Hanif M.S. (2019) High performance real-time pedestrian detection using light weight features and fast cascaded kernel SVM classification. J Signal Process Sys 91:117–129

    Article  Google Scholar 

  23. Su H, Li X, Yang B, et al. (2018) Wavelet support vector machine-based prediction model of dam deformation. Mech Syst Signal Process 110:412–427

    Article  Google Scholar 

  24. Sun W, Du Q. (2019) Hyperspectral band selection: A review. IEEE Geosci Remote Sens Mag 7:118–139

    Article  Google Scholar 

  25. Wang Q, Zhang F, Li X. (2018) Optimal clustering framework for hyperspectral band selection. IEEE Trans Geosci Remote Sens 56:5910–5922

    Google Scholar 

  26. Nakamura RYM, Fonseca LMG, Dos Santos JA, et al. (2013) Nature-inspired framework for hyperspectral band selection. IEEE Trans Geosci Remote Sens 52:2126–2137

    Article  Google Scholar 

  27. Su H, Du Q, Chen G, et al. (2014) Optimized hyperspectral band selection using particle swarm optimization. IEEE J Sel Top Appl Earth Obs Remote Sens 7:2659–2670

    Article  Google Scholar 

  28. Ghosh A, Datta A, Ghosh S. (2013) Self-adaptive differential evolution for feature selection in hyperspectral image data. Appl Soft Comput 13:1969–1977

    Article  Google Scholar 

  29. Tschannerl J, Ren J, Yuen P, et al. (2019) MIMR-DGSA: Unsupervised Hyperspectral band selection based on information theory and a modified discrete gravitational search algorithm. Inform Fusion 51:189–200

    Article  Google Scholar 

  30. Medjahed SA, Saadi TA, Benyettou A, et al. (2016) Gray wolf optimizer for hyperspectral band selection. Appl Soft Comput 40:178–186

    Article  Google Scholar 

  31. Wang M, Wu C, Wang L, et al. (2019) A feature selection approach for hyperspectral image based on modified ant lion optimizer. Knowl-Based Syst 168:39–48

    Article  Google Scholar 

  32. Zhang X, Liu Z, Miao Q, et al. (2018) Bearing fault diagnosis using a whale optimization algorithm-optimized orthogonal matching pursuit with a combined time-frequency atom dictionary. Mech Syst Signal Process 107:29–42

    Article  Google Scholar 

  33. Ala’M AZ, Faris H, Alqatawna J, et al. (2018) Evolving support vector machines using whale optimization algorithm for spam profiles detection on online social networks in different lingual contexts. Knowl-Based Syst 153:91–104

    Article  Google Scholar 

  34. Chen H, Xu Y, Wang M, et al. (2019) A balanced whale optimization algorithm for constrained engineering design problems. Appl Math Model 71:45–59

    Article  MathSciNet  MATH  Google Scholar 

  35. Diaz-Pernil D, Gutierrez-Naranjo MA (2019) Membrane computing and image processing: A short survey. J Membr Comput 1:58–73

    Article  MathSciNet  Google Scholar 

  36. Dai H, Cao Z. (2017) A wavelet support vector machine-based neural network metamodel for structural reliability assessment. Computer-Aided Civ Inf Eng 32:344–357

    Article  Google Scholar 

  37. Pathak RS, Singh A (2016) Mexican hat wavelet transform of distributions. Integral Transform Spec Funct 27:468–483

    Article  MathSciNet  MATH  Google Scholar 

  38. Li L, Liu P, Xing Y, et al. (2018) Time-frequency analysis of acoustic signals from a high-lift configuration with two wavelet functions. Appl Acoust 129:155–160

    Article  Google Scholar 

  39. Tang B, Song T, Li F, et al. (2014) Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine. Renew Energy 62:1–9

    Article  Google Scholar 

  40. Cattani C. (2018) A review on Harmonic wavelets and their fractional extension. J Adv Eng Computat 2:224–238

    Article  Google Scholar 

  41. Grana M, Veganzons M, Ayerdi B Resourse of SalinasA image. http://www.ehu.eus/ccwintco/index.php

  42. Xiang P, Zhou H, Li H, et al. (2020) Hyperspectral anomaly detection by local joint subspace process and support vector machine. Int J Remote Sens 41:3798–3819

    Article  Google Scholar 

  43. Su H, Yu Y, Du Q, et al. (2020) Ensemble learning for hyperspectral image classification using tangent collaborative representation. IEEE Trans Geosci Remote Sens 58:3778–3790

    Article  Google Scholar 

  44. Xu Y, Du B, Zhang F, et al. (2018) Hyperspectral image classification via a random patches network. ISPRS J Photogramm Remote Sens 142:344–357

    Article  Google Scholar 

  45. Okwuashi O, Ndehedehe CE (2020) Deep support vector machine for hyperspectral image classification. Pattern Recogn 103:107298

    Article  Google Scholar 

Download references

Funding

This work was funded by the National Natural Science Foundation of China under Grant No.41901296, 41925007, 41901285, the Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation under Grant No.2018NGCM06, and the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) under Grant No.2642019046.

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Authors and Affiliations

  1. Institute of Geological Survey, China University of Geosciences, Wuhan, 430074, People’s Republic of China

    Mingwei Wang & Ziqi Yan

  2. Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation, Wuhan, 430079, People’s Republic of China

    Mingwei Wang

  3. Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430079, People’s Republic of China

    Jianwei Luo

  4. School of Computer Science, Hubei University of Technology, Wuhan, 430068, People’s Republic of China

    Zhiwei Ye

  5. College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou, 450045, People’s Republic of China

    Peipei He

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  1. Mingwei Wang
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Correspondence to Jianwei Luo.

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Mingwei Wang and Ziqi Yan declare that they have no conflict of interest. Jianwei Luo declares that he has no conflict of interest. Zhiwei Ye declares that he has no conflict of interest. Peipei He declares that she has no conflict of interest.

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Wang, M., Yan, Z., Luo, J. et al. A band selection approach based on wavelet support vector machine ensemble model and membrane whale optimization algorithm for hyperspectral image. Appl Intell 51, 7766–7780 (2021). https://doi.org/10.1007/s10489-021-02270-0

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