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Fuzzy rule-based hyperspectral band selection algorithm with ant colony optimization

  • S.I. : Multifaceted Intelligent Computing Systems (MICS)
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Abstract

The issue of band selection is extremely important in dealing with the plague of dimensionality in hyperspectral images. This study offers a hybrid band selection strategy based on the split-and-merge concept. This novel technique provides suitable band subgroups based on entropy and mutual information utilizing a fuzzy rule-based system without dismissing the real relevance of the band information. Then, using ant colony optimization, it finds the most promising hyperspectral bands from these subsets. On three prominent hyperspectral image data sets, four state-of-the-art techniques are compared with the suggested method to assess the importance of the proposed band selection strategy. In terms of kappa coefficient and overall accuracy, this approach outperforms others significantly.

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References

  1. Aviris—airborne visible/infrared imaging spectrometer [online]. http://aviris.jpl.nasa.gov/

  2. https://www.spiedigitallibrary.org

  3. Chang CI, Du Q, Sun TL, Althouse MLG (1999) A joint band prioritization and band decorrelation approach to band selection for hyperspectral image classification. IEEE Trans Geosci Remote Sens 37(6):2631–2641

    Article  Google Scholar 

  4. Lashkia G, Anthony L (2004) Relevant, irredundant feature selection and noisy example elimination. IEEE Trans Syst Man Cybern B Cybern 34(2):888–897

    Article  Google Scholar 

  5. Shannon E (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423

    Article  MathSciNet  Google Scholar 

  6. Huang R, He M (2005) Band selection based on feature weighting for classification of hyperspectral data. IEEE Geosci Remote Sens Lett 2(2):156–159

    Article  Google Scholar 

  7. Hazra J, Dutta ARCP (2017) A hybrid approach for band selection of hyperspectral images. In: Hybrid intelligence for image analysis and understanding. Wiley. Chap. 11, pp 263–282

  8. Zhang L, Zhong BHJGY (2007) Dimensionality reduction based on clonal selection for hyperspectral imagery. IEEE Geosci Remote Sens Lett 45(12):4172–4186

    Article  Google Scholar 

  9. Feng J, Jiao XZLC, Sun T (2014) Hyperspectral band selection based on trivariate mutual information and clonal selection. IEEE Trans Geosci Remote Sens 52(7):4092–4105

    Article  Google Scholar 

  10. Patra S, Prahlad Modi LB (2015) Hyperspectral band selection based on rough set. IEEE Trans Geosci AND Remote Sens 53(10):5495–5503

    Article  Google Scholar 

  11. Jia S, Guihua Tang JZQL (2016) A novel ranking-based clustering approach for hyperspectral band selection. IEEE Trans Geosci AND Remote Sens 54(1):88–102

    Article  Google Scholar 

  12. Habermann M, Fremont V, Shiguemori EH (2019) Supervised band selection in hyperspectral images using single-layer neural networks. Int J Remote Sens:1–12

  13. Bajcsy P, Groves P (2004) Methodology for hyperspectral band selection. Photogramm Eng Remote Sens 70:793–802

    Article  Google Scholar 

  14. Tan K, Du P (2011) Combined multi-kernel support vector machine and wavelet analysis for hyperspectral remote sensing image classification. Chin Opt Lett 9(1):11003–11006

    Article  Google Scholar 

  15. Du Q, Yang H (2008) Similarity-based unsupervised band selection for hyperspectral image analysis. IEEE Geosci Remote Sens Lett 5(4):564–568

    Article  Google Scholar 

  16. Yang H, D Q, Chen G (2011) Unsupervised hyperspectral band selection using graphics processing units. IEEE J Sel Top Appl Earth Observ Remote Sens 4(3):660–668

    Article  Google Scholar 

  17. Cariou C, Chehdi SLMK (2011) Bandclust: an unsupervised band reduction method for hyperspectral remote sensing. IEEE Geosci Remote Sens Lett 8(3):565–569

    Article  Google Scholar 

  18. Lei L, Prasad JEFS, Bruce LM (2012) Locality preserving dimensionality reduction and classification for hyperspectral image analysis. IEEE Trans Geosci Remote Sens 50(4):1185–1198

    Article  Google Scholar 

  19. Agarwal A, El-Ghazawi HEAT, Le-Moigne J (2007) Efficient hierarchical-pca dimension reduction for hyperspectral imagery. Proc IEEE Int Symp Signal Process Inf Technol: 353–356

  20. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(22):2323–2326

    Article  Google Scholar 

  21. Chang CI, Wang S (2006) Constrained band selection for hyperspectral imagery. IEEE Trans Geosci Remote Sens 44(6):1575–1585

    Article  Google Scholar 

  22. Martinez-Uso A, Pla JMSF, Garcia-Sevilla P (2007) Clustering based hyperspectral band selection using information measures. IEEE Trans Geosci Remote Sens 45(12):4158–4171

    Article  Google Scholar 

  23. Martinez-Uso A, Pla JMSF, Garcia-Sevilla P (2012) Clustering based hyperspectral band selection using information measures. IEEE Trans Geosci Remote Sens 5(2):531–543

    Google Scholar 

  24. Mitra P, C M, Pal, S K (2002) Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intell 24(3):301–312

    Article  Google Scholar 

  25. Ma JP, Zheng ZB, Tong QX, Zheng LF (2003) An application of genetic algorithms on band selection for hyperspectral image classification. In: Proceedings of the 2nd international conference on machine learning and cybernetics, pp 2810–2813

  26. Yu S, De Backer S, Scheunders P (2002) Genetic feature selection combined with composite fuzzy nearest neighbour classifiers for hyperspectral satellite imagery. Pattern Recogn Lett: 183–190

  27. Zhou S, Zhang JP, Su BK (2009) Feature selection and classification based on ant colony algorithm for hyperspectral remote sensing images. In: 2nd international congress on image and signal processing, CISP ’09, pp 1–4

  28. Firpi HA, Goodman E (2004) Swarmed feature selection. In: Proceedings of the 33rd applied imagery pattern recognition workshop (AIPR’04), pp 112–118

  29. Ding S, Chen L (2009) Classification of hyperspectral remote sensing images with support vector machines and particle swarm optimization. In: Proceedings of the international conference on information engineering and computer science, pp 1–5

  30. Huang R, Li X (2008) Band selection based on evolution algorithm and sequential search for hyperspectral classification. In: Proceedings of the international conference on audio, language and image processing(ICALIP’08), pp 1270–1273

  31. Samadzadegan F, Partovi T (2010) Feature selection based on ant colony algorithm for hyperspectral remote sensing images. In: 2010 2nd workshop on hyperspectral image and signal processing: evolution in remote sensing (WHISPERS), pp 1–4

  32. Liu X, Yu C, Cai Z (2010) Differential evolution based band selection in hyperspectral data classification. In: Proceedings of the international symposium on intelligence computation and applications (ISICA’10), pp 86–94

  33. Khushaba RN, Al-Ani A, AlSukker A, Al-Jumaily A (2008) A combined ant colony and differential evolution feature selection algorithm. In: Proceedings of the international conference on ant colony optimiztion and swarm intelligence (ANTS 2008), pp 1–12

  34. Aditi Roy C, Joydev Hazra KD, Dutta P (2020) Proceedings of research and applications in artificial intelligence, advances in intelligent systems and computing 1355. Comparative study of the effect of different fitness functions in PSO algorithm on band selection of hyperspectral imagery (2020). https://doi.org/10.1007/978-981-16-1543-6_9

  35. Chang CI, Wu CC, Liu KH, Chen HM, Chen CC, Wen CH (2015) Progressive band processing of linear spectral unmixing for hyperspectral imagery. IEEE J Sel Top Appl Earth Observ Remote Sens 8(6):2583–2597

    Article  Google Scholar 

  36. Gong M, Mingyang Zhang YY (2016) Unsupervised band selection based on evolutionary multiobjective optimization for hyperspectral images. IEEE Trans Geosci Remote Sens 54(1):544–557

    Article  Google Scholar 

  37. Zhu G, Yuancheng Huang JLZBFX (2016) Unsupervised hyperspectral band selection by dominant set extraction. IEEE Trans Geosci Remote Sens 54(1):227–239

    Article  Google Scholar 

  38. Xu Y, Qian Du NHY (2017) Particle swarm optimization-based band selection for hyperspectral target detection. IEEE Geosci Remote Sens Lett 14(4):554–558

    Article  Google Scholar 

  39. Jiao L, Jie Feng FLTS, Zhang X (2015) Semisupervised affinity propagation based on normalized trivariable mutual information for hyperspectral band selection. IEEE J Sel Top Appl Earth Observ Remote Sens 8(6):2760–2773

    Article  Google Scholar 

  40. Bai J, Shiming Xiang LS, Pan C (2015) Semisupervised pair-wise band selection for hyperspectral images. IEEE J Sel Top Appl Earth Observ Remote Sens 8(6):2798–2813

    Article  Google Scholar 

  41. Arushi Gupta SS (2020) Comparative analysis of ant colony and particle swarm optimization algorithms for distance optimization. Procedia Comput Sci 173:245–253

    Article  Google Scholar 

  42. AbWahab MN, Nefti-Meziani S, Atyabi A (2015) A comprehensive review of swarm optimization algorithm. PLoS ONE. https://doi.org/10.1371/journal.pone.0122827

    Article  Google Scholar 

  43. Guo G, Wang H, Bell D, Bi Y (2004) Knn model-based approach in classification

  44. Loh W-Y (2011) Classification and regression trees. WIREs Data Min Knowl Discov 1:14–23

    Article  Google Scholar 

  45. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39. https://doi.org/10.1109/MCI.2006.329691

    Article  Google Scholar 

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Chowdhury, A.R., Hazra, J., Dasgupta, K. et al. Fuzzy rule-based hyperspectral band selection algorithm with ant colony optimization. Innovations Syst Softw Eng 20, 161–174 (2024). https://doi.org/10.1007/s11334-021-00432-4

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  • DOI: https://doi.org/10.1007/s11334-021-00432-4

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