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Research Articles

Hybrid optimal joint spatial-spectral hyperspectral image classification using modified DHO-based GIF with JRKNN

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Pages 585-598 | Received 08 Dec 2021, Accepted 28 Feb 2023, Published online: 13 Mar 2023

References

  • Giannantonio T, Alperovichb A, Semeraro P, et al. Intra-operative brain tumor detection with deep learning-optimized hyperspectral imaging. arXiv preprint arXiv:2302.02884 (2023).
  • Bhattacharya RK, Das Chatterjee N, Das K. Impact of instream sand mining on habitat destruction or transformation using coupling models of HSI and MLR. Spatial Information Research. 2020;28(1):67–85.
  • Shimoni M, Haelterman R, Parneel C. Hypersectral imaging for military and security applications: combining myriad processing and sensing techniques. IEEE Geosci Remote Sens Magaz. 2019;7(2):101–117.
  • Zhao X, Zhang M, Tao R, et al. Cross-domain classification of multisource remote sensing data using fractional fusion and spatial-spectral domain adaptation. IEEE J Sel Top Appl Earth Obs Remote Sens. 2022;15:5721–5733.
  • Xu F, Zhang G, Song C, et al. Multi-scale and cross-level attention learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 2023;61(3):1773–1786.
  • Dong Y, Liu M, Zhang L, et al. Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image classification. IEEE Trans Image Proc. 2022;31:1559–1572.
  • Ma Y, Li R, Yang G, et al. A research on the combination strategies of multiple features for hyperspectral remote sensing image classification. J Sens. 2018;2018(1):7341973.
  • Binol H, et al. Ensemble learning based multiple kernel principal component analysis for dimensionality reduction and classification of hyperspectral imagery. Math Probl Eng. 2018;2018:1–14.
  • Li S, Song W, Fang L, et al. Deep learning for hyperspectral image classification: an overview. IEEE Trans Geosci Remote Sens. 2019;57(9):6690–6709.
  • Tong F, Zhang Y. Spectral-spatial and cascaded multilayer random forests for tree species classification in airborne hyperspectral images. IEEE Trans Geosci Remote Sens. 2022;60(1):207–220.
  • Sawant SS, Prabukumar M, Loganathan A, et al. Multi-objective multi-verse optimizer based unsupervised band selection for hyperspectral image classification. Int J Remote Sens. 2022;43(11):3990–4024.
  • Kaul A, Raina S. Support vector machine versus convolutional neural network for hyperspectral image classification: A systematic review. Concurr Comput: Pract Exper. 2022;34(15):e6945.
  • Zhang A, Pan Z, Fu H, et al. Superpixel nonlocal weighting joint sparse representation for hyperspectral image classification. Remote Sens. 2022;14(9):2125.
  • Sun L, Qihao C, Zhiguo C, et al. Hyperspectral image super-resolution method based on spectral smoothing prior and tensor tubal row-sparse representation. Remote Sens. 2022;14(9):2142.
  • Khader A, Xiao L, Yang J. A model-guided deep convolutional sparse coding network for hyperspectral and multispectral image fusion. Int J Remote Sens. 2022;43(6):2268–2295.
  • Sun L, Xu B, Lu Z, et al. Hyperspectral image classification based on a multi-scale weighted kernel network. Chin J Elect. 2022;31(5):832–843.
  • Shang Y, Zheng X, Li J, et al. A comparative analysis of swarm intelligence and evolutionary algorithms for feature selection in SVM-based hyperspectral image classification. Remote Sens. 2022;14(13):3019.
  • Fu A, Ma X, Wang H, et al. Classification of hyperspectral image based on hybrid neural networks. In: IGARSS 2018 - 2018 IEEE Int Geosci Remote Sens Symp. Valencia, Spain. 2018; 2643-2646.
  • Wang X. Hyperspectral image classification powered by khatri-rao decomposition based multinomial logistic regression. IEEE Trans Geosci Remote Sens. 2022;60(2):1433–1446.
  • Shinde S, Patidar H. Hyperspectral image classification using principle component analysis and deep convolutional neural network. J Amb Intel Human Comput. 2022: 1–7. https://doi.org/10.1007/s12652-022-03876-z.
  • Yuan Y, Jin M. Multi-type spectral spatial feature for hyperspectral image classification. Neurocomput. 2022;492:637–650.
  • Jayaprakash C, Damodaran BB, Sowmya V, et al. Dimensionality reduction of hyperspectral images for classification using randomized independent component analysis. 5th international conference on signal processing and integrated networks; Noida, India. IEEE; 2018. p. 492–496.
  • Hashemi-Nasab FS, Parastar S. Vis-NIR hyperspectral imaging coupled with independent component analysis for saffron authentication. Food Chem. 2022;393:133450.
  • Li L, Wen X, Fan W, et al. An effective feature extraction method via spectral-spatial filter discrimination analysis for hyperspectral image. Multimed Tool Appl. 2022;81:40871–40904.
  • Wang H, Wang X, Cheng Y. Graph meta transfer network for heterogeneous few-shot hyperspectral image classification. IEEE Trans Geosci Remote Sens. 2023;61:5501112.
  • Zhang M, Li W, Zhao X, et al. Morphological transformation and spatial-logical aggregation for tree species classification using hyperspectral imagery. IEEE Trans Geosci Remote Sens. 2023;61:1–12.
  • Zhang Y, Zhang M, Li W, et al. Language-aware domain generalization network for cross-scene hyperspectral image classification. IEEE Trans Geosci Remote Sens. 2023;61:5501312.
  • Bai J, Shi W, Xiao Z, et al. Achieving better category separability for hyperspectral image classification: a spatial–spectral approach. IEEE Trans Neural Net Learn Syst. 2023: 1–15. https://doi.org/10.1109/TNNLS.2023.3235711.
  • Wang S, Hu X, Sun J, et al. Hyperspectral anomaly detection using ensemble and robust collaborative representation. Infor Sci. 2023;624:748–760.
  • Wang X, Cheng Y, Mei X, et al. Group shuffle and spectral-spatial fusion for hyperspectral image super-resolution. IEEE Trans Comput Imag. 2022;8:1223–1236.
  • Xue Z, Nie X. Low-rank and sparse representation with adaptive neighborhood regularization for hyperspectral image classification. J Geodesy Geoinfor Scien. 2022;5(1):73–90.
  • Hassanzadeh S, Danyali H, Helfroush MS. Hyperspectral images classification based on multiple kernel learning using SWT and KMNF with few training samples. J Spatial Sci. 2022: 1–21. https://doi.org/10.1080/14498596.2022.2097962.
  • Wang W, Han Y, Deng C, et al. Hyperspectral image classification via deep structure dictionary learning. Remote Sens. 2022;14(9):2266.
  • Bo C, Lu H, Wang D. Spectral-spatial k-nearest neighbor approach for hyperspectral image classification. Multimed Tools Appl. 2017;76(8):11113–11130.
  • Fu H, Zhang A, Sun G, et al. A novel band selection and spatial noise reduction method for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 2022;60:5535713.
  • Li L, Sun X, Ji Y, et al. Hyperspectral image classification framework via information fusion and bilateral filtering. 6th advanced information technology, electronic and automation control conference; Beijing, People’s Republic of China. IEEE; 2022.
  • Lv H, Wang Z, Zhang H. Edge protection filtering and convolutional neural network for hyperspectral remote sensing image classification. Infrared Phy Techn. 2022;122:104039.
  • Tu B, Zhou C, Kuang W, et al. Multiattribute sample learning for hyperspectral image classification using hierarchical peak attribute propagation. IEEE Trans Instrumen Measur. 2022;71:6502617.
  • Li S, Tian Y, Xia H. Unmixing-based pan-guided fusion network for hyperspectral imagery. IEEE Trans Geosci Remote Sens. 2022;60:5522017.
  • Xie Z, Duan P, Liu W, et al. Feature consistency-based prototype network for open-set hyperspectral image classification. IEEE Trans Neural Net Learn Syst. 2023: 1–11. doi:10.1109/TNNLS.2022.3232225.
  • Wang L, Zhang J, Liu P, et al. Spectral–spatial multi-feature-based deep learning for hyperspectral remote sensing image classification. Soft Comput. 2017;21(1):213–221.
  • Zhang L, Zhang L, Du B. Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geosci Remote Sens Magaz. 2016;4(2):22–40.
  • Ma X, Wang X, Geng J. Spectral–spatial classification of hyperspectral image based on deep auto-encoder. IEEE J Sel Top Appl Earth Obs Remote Sens. 2016;9(9):4073–4085.
  • Wei W, Song W, Zhang L, et al. Lightweighted hyperspectral image classification network by progressive bi-quantization. IEEE Trans Geosci Remote Sens. 2023;61:5501914.
  • Yu S, Jia S, Xu C. Convolutional neural networks for hyperspectral image classification. Neurocomputing. 2017;219:88–98.
  • Chen Y, Jiang H, Li C, et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens. 2016;54(10):6232–6251.
  • Guo Y, Cao H, Han S, et al. Spectral–spatial HyperspectralImage classification with K-nearest neighbor and guided filter. IEEE Access. 2018;6:18582–18591.
  • Kumar R, Saichandana B, Srinivas K. Dimensionality reduction and classification of hyperspectral images using genetic algorithm. Indonesian J Electr Eng Comput Sci. 2016;3(3):503–511.
  • Sharma S, Buddhiraju K, Dashondhi G. Hyperspectral image classification using ant colony optimization algorithm based on joint spectral-spatial parameters Fort Worth, TX, USA,2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS),2017.
  • Manju S, Helenprabha K. Retracted article: a structured support vector machine for hyperspectral satellite image segmentation and classification based on modified swarm optimization approach. J Amb Intel Humanized Comput. 2021;12:3659–3668.
  • Mirjalili S, Lewis A. The whale optimization algorithm. Adv Eng Software. 2016;95:51–67.
  • Aljarah I, Faris H, Mirjalili S. Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput. 2018;22:1–15.
  • Kumar B, Manoharan P. Whale optimization-based band selection technique for hyperspectral image classification. Int. J Remote Sens. 2021;42(13):5109–5147.
  • Wang M, Jia Z, Luo J, et al. A hyperspectral image classification method based on weight wavelet kernel joint sparse representation ensemble and β-whale optimization algorithm. IEEE J Sel Top Appl Earth Obs Remote Sens. 2021;14:2535–2550.
  • Bhatti UA, Zhaoyuan Y, Jocelyn C, et al. Local similarity-based spatial–spectral fusion hyperspectral image classification with deep CNN and Gabor filtering. IEEE Trans Geosci Remote Sens. 2022;60:1–15.
  • Bhatti UA, Huang M, Di W, et al. Recommendation system using feature extraction and pattern recognition in clinical care systems. Enterprise Infor Syst. 2019; 13(3):329–351.
  • Bhatti UA, Zhaoyuan Y, Jingbing L, et al. Hybrid watermarking algorithm using Clifford algebra with arnold scrambling and chaotic encryption. IEEE Access. 2020;8:76386–76398.
  • Bhatti UA, Huang M, Wang H, et al. Recommendation system for immunization coverage and monitoring. Hum Vaccin Immunother. 2018;14(1):165–171.

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