Abstract
In this work, an effective classification of hyperspectral images is modelled and simulated with the proximal support vector machine (PSVM) by integrating them with the deep learning approach. The modelled new deep proximal support vector machines are designed in a manner to handle the existing complexity, discrepancies and irregularities in the traditional hyperspectral image classifiers. This paper investigates the applicability of the new deep linear and nonlinear proximal support vector machines as applied for hyperspectral image classification. In respect of the new deep PSVM classifier, it is modelled for deep linear PSVM and deep nonlinear PSVM to perform classification of spectral images so as to bring out the best classifier model. To test and validate the proposed deep PSVM classifiers University of Pavia datasets, Indian Pine datasets and Kennedy Space Centre datasets are employed as test beds and results are attained. The developed new deep PSVM classifiers are developed with varied kernel functions to do the classification process. The deep learning technique enhances the linear and nonlinear PSVM classifier models to perform more effectively during the learning process and carry out the classification using auto-encoders and decoders. Results attained during the process infer that the developed new deep PSVM (linear and nonlinear) has come out with better classification accuracy in comparison with that of the other techniques from literature for the same datasets. Statistical analysis validates the randomness that occurs in the proposed deep learning techniques as applied for spectral image classification.









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Vaddi R, Manoharan P (2020) Hyperspectral image classification using CNN with spectral and spatial features integration. Infrar Phys Technol 66:103296
Cui B, Ma X, Xie X, Ren G, Ma Y (2017) Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering. Infrar Phys Technol 81:79–88
Shi G, Huang H, Li Z, Duan Y (2020) Multi-manifold locality graph preserving analysis for hyperspectral image classification. Neurocomputing
Elmaizi A, Nhaila H, Sarhrouni E, Hammouch A, Nacir C (2019) A novel information gain based approach for classification and dimensionality reduction of hyperspectral images. Procedia Comput Sci 148:126–134
Liu F, Wang Q (2020) A sparse tensor-based classification method of hyperspectral image. Signal Process 168:107361
Zhang L, Zhang L, Du B, You J, Tao D (2019) Hyperspectral image unsupervised classification by robust manifold matrix factorization. Inf Sci 485:154–169
Al-Sarayreh M, Reis M, Yan WQ, Klette R (2020) Potential of deep learning and snapshot hyperspectral imaging for classification of species in meat. Food Control 66:107332
Pan B, Shi Z, Xu X (2018) MugNet: Deep learning for hyperspectral image classification using limited samples. ISPRS J Photogramm Remote Sens 145:108–119
Mohan A, Venkatesan M (2020) HybridCNN based hyperspectral image classification using multiscale spatiospectral features. Infrar Phys Technol 669:103326
Cui B, Zhong L, Yin B, Ren G, Lu Y (2019) Hyperspectral image classification based on multiple kernel mutual learning. Infrar Phys Technol 99:113–122
Medjahed SA, Ouali M (2018) Band selection based on optimization approach for hyperspectral image classification. Egypt J Remote Sens Space Sci 21(3):413–418
Lan R, Li Z, Liu Z, Gu T, Luo X (2019) Hyperspectral image classification using k-sparse denoising autoencoder and spectral–restricted spatial characteristics. Appl Soft Comput 74:693–708
Mirzaei S, Khosravani S (2019) Hyperspectral image classification using non-negative tensor factorization and 3D convolutional neural networks. Signal Process Image Commun 76:178–185
Le BT, Ha TTL (2019) Hyperspectral image classification based on average spectral-spatial features and improved hierarchical-ELM. Infrared Phys Technol 102:103013
Vaddi R, Manoharan P (2020) CNN based hyperspectral image classification using un-supervised band selection and structure-preserving spatial features. Infrared Phys Technol 54:103457
Zhang N, Pan Y, Feng H, Zhao X, Yang X, Ding C, Yang G (2019) Development of Fusarium head blight classification index using hyperspectral microscopy images of winter wheat spikelets. Biosyst Eng 186:83–99
Imani M, Ghassemian H (2020) An overview on spectral and spatial information fusion for hyperspectral image classification: current trends and challenges. Inf Fusion 59:59–83
Barman B, Patra S (2020) Variable precision rough set based unsupervised band selection technique for hyperspectral image classification. Knowl Based Syst 193:105414
Noviyanto A, Abdulla WH (2020) Signifying the information carrying bands of hyperspectral imaging for honey botanical origin classification. J Food Eng 33:110281
Han M, Cong R, Li X, Fu H, Lei J (2020) Joint spatial–spectral hyperspectral image classification based on convolutional neural network. Pattern Recogn Lett 130:38–45
Cao F, Guo W (2020) Cascaded dual-scale crossover network for hyperspectral image classification. Knowl Based Syst 189:105122
Li D, Wang Q, Kong F (2020) Adaptive kernel sparse representation based on multiple feature learning for hyperspectral image classification. Neurocomputing
Liu Q, Li Z, Shuai S, Sun Q (2020) Spectral group attention networks for hyperspectral image classification with spectral separability analysis. Infrared Phys Technol 25:103340
Zhang Z (2020) Semi-supervised hyperspectral image classification algorithm based on graph embedding and discriminative spatial information. Microprocess. Microsyst. 33:103070
Zhang B, Zhao L, Zhang X (2020) Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images. Remote Sens Environ 247:111938
Pan E, Mei X, Wang Q, Ma Y, Ma J (2020) Spectral-spatial classification for hyperspectral image based on a single GRU. Neurocomputing 387:150–160
Azar SG, Meshgini S, Rezaii TY, Beheshti S (2020) Hyperspectral image classification based on sparse modelling of spectral blocks. arXiv preprint arXiv:2005.08191
Okwuashi O, Ndehedehe CE (2020) Deep support vector machine for hyperspectral image classification. Pattern Recognit 332:107298
Fang J, Cao X (2020) Multidimensional relation learning for hyperspectral image classification. Neurocomputing 410:211–219
Tu X, Shen X, Fu P, Wang T, Sun Q, Ji Z (2020) Discriminant sub-dictionary learning with adaptive multiscale superpixel representation for hyperspectral image classification. Neurocomputing 409:131–145
Chu Y, Lin H, Yang L, Zhang D, Diao Y, Fan X, Shen C (2020) Hyperspectral image classification based on discriminative locality preserving broad learning system. Knowl Based Syst 27:106319
Fung GM, Mangasarian OL (2005) Multicategory proximal support vector machine classifiers. Mach Learn 59(1–2):77–97
Kang S, Cho S, Kang P (2015) Constructing a multi-class classifier using one-again-st-one approach with different binary classifiers. Neurocomputing 149:677–682
Chen Y, Jiang H, Li C, Jia X, Ghamisi P (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232–6251
Zhu L, Chen Y, Ghamisi P, Benediktsson JA (2018) Generative adversarial networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 56(9):5046–5063
Chen Y, Lin Z, Zhao X, Wang G, Gu Y (2014) Deep learning-based classification of hyperspectral data. IEEE J Sel Top Appl Earth Observ 76:2094–2107
Zhang Y, Cao G, Li X, Wang B (2018) Cascaded random forest for hyperspectral image classification. IEEE J Sel Top Appl Earth Observ 11(4):1082–1094
Nachimuthu DS, Baladhandapani A (2014) Multidimensional texture characterization: on analysis for brain tumor tissues using MRS and MRI. J Digit Imaging 27(4):496–506
Ranganayaki V, Deepa SN (2019) Linear and non-linear proximal support vector machine classifiers for wind speed prediction. Clust Comput 22(1):379–390
Natarajan YJ, Nachimuthu DS (2019) New SVM kernel soft computing models for wind speed prediction in renewable energy applications. Soft Comput 21:1–18
Ranganayaki V, Deepa SN (2017) Svm based neuro fuzzy model for short term wind power forecasting. Natl Acad Sci Lett 40(2):131–134
Orr GB, Müller KR (eds) (2003) Neural networks: tricks of the trade. Springer
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Kalaiarasi, G., Maheswari, S. Deep proximal support vector machine classifiers for hyperspectral images classification. Neural Comput & Applic 33, 13391–13415 (2021). https://doi.org/10.1007/s00521-021-05965-0
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DOI: https://doi.org/10.1007/s00521-021-05965-0