© The Institution of Engineering and Technology
Hyperspectral image (HSI) consists of hundreds of contiguous spectral bands, which can be used in the classification of different objects on the earth. The inclusion of both spectral as well as spatial features stands essential in order that high classification accuracy is achieved. However, incorporation of the spectral and spatial information without preserving the intrinsic structure of the data leads on to downscaling the classification accuracy. To address the issue aforementioned, the proposed method which involves using unsupervised spectral band selection based on three major constrains: (i) low reconstruction error with neighbourhood bands, (ii) low noise, (iii) high information entropy, is put forward. In addition, the structure-preserving recursive filter is used to extract spatial features. Finally, the classification is performed using convolutional neural networks (CNNs) with different sets of convolutional, pooling, and fully connected layers. To test the performance of the proposed method, experiments have been carried out with three benchmark HSI datasets Indian pines, University of Pavia, and Salinas. These experiments reveal that the proposed method offers better classification accuracy over the purportedly state-of-the-art methods in terms of standard metrics like overall accuracy, average accuracy, and kappa coefficient (K). The proposed method has attained OAs of 99.9, 98.9, and 99.93% for the three datasets, respectively.
References
-
-
1)
-
4. Licciardi, G., Marpu, P.R., Chanussot, J., et al: ‘Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles’, IEEE Geosci. Remote Sens. Lett., 2011, 9, (3), pp. 447–451.
-
2)
-
29. Zhan, K., Wang, H., Xie, Y., et al: ‘Albedo recovery for hyperspectral image classification’, J. Electron. Imaging, 2017, 26, (4), pp. 1–12, doi:10.1117/1.JEI.26.4.043010.
-
3)
-
18. Zhang, M., Gong, M., Chan, Y.: ‘Hyperspectral band selection based on multi-objective optimization with high information and low redundancy’, Appl. Soft Comput., 2018, 70, pp. 604–621, .
-
4)
-
31. Yang, J., Zhao, Y., Chan, J.C.: ‘Learning and transferring deep joint spectral–spatial features for hyperspectral classification’, IEEE Trans. Geosci. Remote Sens., 2017, 55, (8), pp. 4729–4742, doi: 10.1109/TGRS.2017.2698503.
-
5)
-
50. Sawant, S.S., Prabukumar, M.: ‘Band fusion based hyper spectral image classification’, Int. J. Pure. Appl. Math., 2017, 117, (17), pp. 71–76.
-
6)
-
55. Prabukumar, M., Shrutika, S.: ‘Band clustering using expectation–maximization algorithm and weighted average fusion-based feature extraction for hyperspectral image classification’, J. Appl. Remote Sens., 2018, 12, (4), pp. 1–24.
-
7)
-
11. Sawant, S.S., Prabukumar, M., Samiappan, S.: ‘A band selection method for hyperspectral image classification based on cuckoo search algorithm with corre-lation based initialization’. 10th IEEE GRSS WHISPERS is workshop on Hy-perspectral image and signal processing: Evolution in Remote Sensing (WHISPERS), Am-sterdam, Netherlands, 2019.
-
8)
-
46. Luo, F., Du, B., Zhang, L., et al: ‘Feature learning using spatial-spectral hypergraph discriminant analysis for hyperspectral image’, IEEE Trans. Cybernet., 2019, 49, (7), pp. 2406–2419, doi: 10.1109/TCYB.2018.2810806.
-
9)
-
2. Lowe, A., Harrison, N., French, A.P.: ‘Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress’, Plant Methods, 2017, 13, p. 80.
-
10)
-
10. Luo, F., Zhang, L., Zhou, X., et al: ‘Sparse-adaptive hypergraph discriminant analysis for hyperspectral image classification’, IEEE Geosci. Remote Sens. Lett., 2020, 17, (6), pp. 1082–1086, doi: 10.1109/LGRS.2019.2936652.
-
11)
-
36. Li, C., Yang, S.X., Yang, Y., et al: ‘Hyperspectral remote sensing image classification based on maximum overlap pooling convolutional neural network’, Sensors (Basel), 2018, 18, (10), p. 3587, .
-
12)
-
57. Vaddi, R., Prabukumar, M.: ‘CNN based hyperspectral image classification using unsupervised band selection and structure-preserving spatial features’, Infrared Phys. Technol., 2020, 110, p. 103457, .
-
13)
-
22. Zhang, L., Zhang, Q., Du, B., et al: ‘Simultaneous spectral-spatial feature selection and extraction for hyperspectral images’, IEEE Trans. Cybernet, 2016, 48, (1), pp. 1–13, doi: 10.1109/TCYB.2016.2605044.
-
14)
-
3. Khan, M.J., Khan, H.S., Yousaf, A., et al: ‘Modern trends in hyperspectral image analysis: a review’, IEEE Access, 2018, 6, pp. 14118–14129.
-
15)
-
21. Zhang, Y., Jiang, X., Wang, X., et al: ‘Spectral-spatial hyperspectral image classification with superpixel pattern and extreme learning machine’, Remote Sens., 2019, 11, p. 1983, doi: 10.3390/rs11171983.
-
16)
-
24. Vaddi, R., Manoharan, P.: ‘Probabilistic PCA based hyperspectral image classifica-tion for remote sensing applications’, in: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds.): ‘Intelligent systems design and applications’. (Springer, Cham, 2020), vol. 941, pp. 863–869.
-
17)
-
12. Feng, J., Jiao, L.C., Zhang, X., et al: ‘Hyperspectral band selection based on trivariate mutual information and clonal selection’, IEEE Trans. Geosci. Remote Sens., 2014, 52, (7), pp. 4092–4105.
-
18)
-
47. Boggavarapu, L.N.P., Prabukumar, M.: ‘Hyper spectral image classification using fuzzy embedded hyperbolic sigmoid nonlinear principal component and weighted least square approach’, J. Appl. Remote Sens., 2020, 14, (2), pp. 1–18, .
-
19)
-
6. Bandos, T.V., Bruzzone, L., Camps-Valls, G.: ‘Classification of hyperspectral images with regularized linear discriminant analysis’, IEEE Trans. Geosci. Remote Sens., 2009, 47, (3), pp. 862–873.
-
20)
-
21)
-
60. Luo, F., Zhang, L., Du, B., et al: ‘Dimensionality reduction with enhanced hybrid-graph discriminant learning for hyperspectral image classification’, IEEE Trans. Geosci. Remote Sens., 2020, 58, (8), pp. 5336–5353, doi: 10.1109/TGRS.2020.2963848.
-
22)
-
45. Xie, F., Li, F., Lei, C., et al: ‘Representative band selection for hyperspectral image classification’, ISPRS Int. J. Geo-Inf., 2018, 7, p. 338, doi:10.3390/ijgi7090338.
-
23)
-
54. Vaddi, R., Prabukumar, M.: ‘Stratified sampling method based training pixels selection for hyper spectral remote sensing image classification’, Int. J. Pure. Appl. Math., 2017, 117, (17), pp. 121–126, .
-
24)
-
13. Sawant, S.S., Prabukumar, M.: ‘Semi-supervised techniques based hyperspectral image classification: a survey’. Int. Conf. on Innovations in Power and Advanced Computing Technologies [i-PACT2017], VIT Vellore, 2017.
-
25)
-
14. Sawant, S.S., Prabukumar, M.: ‘Unsupervised band selection based on weighted information entropy and 3d discrete cosine transform for hyperspectral image classification’, Int. J. Remote Sens., 2020, 41, (10), pp. 3948–3969.
-
26)
-
43. Melganiand, F., Bruzzone, L.: ‘Classification of hyperspectral remotesensing images with support vector machines’, IEEE Trans. Geosci. Remote Sens., 2004, 42, (8), pp. 1778–1790.
-
27)
-
15. Sawant, S., Prabukumar, M.: ‘Hyperspectral band selection based on meta heuristic optimization approach’, Infrared Phys. Technol., 2020, 107, p. 103295, .
-
28)
-
52. Boggavarapu, L.N.P., Prabukumar, M.: ‘Robust classification of hyper spectral remote sensing images combined with multi hypothesis prediction and 3 dimensional discrete wavelet transform’, Int. J. Pure. Appl. Math., 2017, 117, (17), pp. 115–120, .
-
29)
-
42. Gao, Q., Lim, S., Jia, X.: ‘Hyperspectral image classification using convolutional neural networks and multiple feature learning’, Remote Sens., 2018, 10, p. 299.
-
30)
-
41. Wang, Q., Zhang, F., Li, X.: ‘Hyperspectrral band selection via optimal neighborhood reconstruction’, IEEE Trans. Geosci. Remote Sens. (T-GRS), 2020, 58, (12), 8465–8476, doi: 10.1109/TGRS.2020.2987955.
-
31)
-
51. Sawant, S.S., Prabukumar, M., Samiappan, S.: ‘Ranking and grouping based feature selection for hyperspectral image classification’. 39th Asian Conf. on Remote Sensing (ACRS 2018) Kuala Lumpur, Malaysia, 2018.
-
32)
-
33. 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), pp. 6232–6251, .
-
33)
-
56. He, K., Sun, J.: ‘Convolutional neural networks at constrained time cost’. 2015 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 5353–5360, doi: 10.1109/CVPR.2015.7299173.
-
34)
-
7. Yang, J.-M., Yu, P.-T., Kuo, B.-C., et al: ‘Nonparametric fuzzy feature extraction for hyperspectral image classification’, Int. J. Fuzzy Syst., 2010, 12, (3), pp. 208–217.
-
35)
-
58. Phaneendra, L.N., Boggavarapu, K., Prabukumar, M.: ‘A new framework for hyperspectral image classification using gabor embedded patch based convolution neural network’, Infrared Phys. Technol., 2020, 110, p. 103455, .
-
36)
-
53. Duddu, V., Rajesh Pillai, N., Vijay Rao, D., et al: ‘Fault tolerance of neural networks in adversarial settings’, J. Intell. Fuzzy Syst. (JIFS), 2020, 10, (1), doi:10.3233/JIFS-179677.
-
37)
-
30. Signoroni, A., Savardi, M., Baronio, A., et al: ‘Deep learning meets hyperspectral image analysis: a multidisciplinary review’, J. Imaging, 2019, 5, p. 52, doi: 10.3390/jimaging5050052.
-
38)
-
32. Zhong, Z., Li, J., Luo, Z., et al: ‘Spectral-spatial residual network for hyperspectral image classification: A 3-D deep learning framework’, IEEE Trans. Geosci. Remote Sens., 2018, 56, (2), pp. 847–858, doi: 10.1109/TGRS.2017.2755542.
-
39)
-
40)
-
28. Zenzo, S.D., Bernstein, R., Degloria, S.D., et al: ‘Gaussian maximum likelihood and contextual classification algorithms for multicrop classification’, IEEE Trans. Geosci. Remote Sens., 1987, GE-25, (6), pp. 805–814.
-
41)
-
16. Sawant, S.S., Prabukumar, M.: ‘New framework for hyperspectral band selection using modified wind-driven optimization algorithm’, Int. J. Remote Sens., 2019, 40, (20), pp. 7852–7873.
-
42)
-
1. Goetz, A., Vane, G., Solomon, J., et al: ‘Imaging spectrometry for earth remote sensing’, Science (New York, N.Y.), 1985, 228, pp. 1147–1153, doi: 10.1126/science.228.4704.1147.
-
43)
-
37. Wang, A., Wang, Y., Chen, Y.: ‘Hyperspectral image classification based on convolutional neural network and random forest’, Remote Sens. Lett., 2019, 10, (11), pp. 1086–1094, .
-
44)
-
5. Villa, A., Benediktsson, J.A., Chanussot, J., et al: ‘Hyperspectral image classification with independent component discriminant analysis’, IEEE Trans. Geosci. Remote Sens., 2011, 49, (12), pp. 4865–4876.
-
45)
-
9. Luo, F., Huang, H., Duan, Y., et al: ‘Local geometric structure feature for dimensionality reduction of hyperspectral imagery’, Remote Sens., 2017, 9, p. 790, doi: 10.3390/rs9080790.
-
46)
-
39. Vaddi, R., Prabukumar, M.: ‘Hyperspectral image classification using CNN with spectral and spatial features integration’, Infrared Phys. Technol., 2020, 107, p. 103296, .
-
47)
-
34. Chen, Y., Zhu, L., Ghamisi, P., et al: ‘Hyperspectral images classification with gabor filtering and convolutional neural network’, IEEE Geosci. Remote Sens. Lett., 2017, 14, (12), pp. 2355–2359, .
-
48)
-
27. Ma, L., Crawford, M.M., Tian, J.: ‘Local manifold learning-based k -nearest-neighbor for hyperspectral image classification’, IEEE Trans. Geosci. Remote Sens., 2010, 48, (11), pp. 4099–4109.
-
49)
-
17. Zhang, F., Wang, Q., Li, X.: ‘Optimal neighboring reconstruction for hyperspectral band selection’. IGARSS 2018–2018 IEEE Int. Geoscience and Remote Sensing Symp., Valencia, 2018, pp. 4709–4712.
-
50)
-
25. Boggavarapu, L.N.P., Prabukumar, M.: ‘Classification of hyper spectral remote sensing imagery using intrinsic parameter estimation’, in: (Springer Nature, Switzerland, 2020), pp. 1–11, .
-
51)
-
49. Sawant, S.S., Prabukumar, M.: ‘A review on graph-based semi-supervised learning methods for hyperspectral image classification’, Egyptian J. Remote Sens. Space Sci., 2018, 23, (2), pp. 243–248.
-
52)
-
40. Boggavarapu, L.N.P., Prabukumar, M.: ‘Survey on classification methods for hyper spectral remote sensing imagery’. 2017 Int. Conf. on Intelligent Computing and Control Systems, Madurai, 2017, pp. 538–542, doi: 10.1109/ICCONS.2017.8250520.
-
53)
-
26. Zhan, K., Wang, H., Huang, H., et al: ‘Large margin distribution machine for hyperspectral image classification’, J. Electron. Imaging, 2016, 25, p. 063024, doi: 10.1117/1.JEI.25.6.063024.
-
54)
-
59. Sawant, S., Manoharan, P.: ‘A hybrid optimization approach for hyperspectral band selection based on wind driven optimization and modified cuckoo search optimization’, Multimed. Tools. Appl., 2020, 79, pp. 1–24, .
-
55)
-
35. Aptoula, E., Ozdemir, M.C., Yanikoglu, B.: ‘Deep learning with attribute profiles for hyperspectral image classification’, IEEE Geosci. Remote Sens. Lett., 2016, 13, (12), pp. 1970–1974.
-
56)
-
19. Qian, Y., Ye, M., Zhou, J.: ‘Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features’, IEEE Trans. Geosci. Remote Sens., 2013, 51, (4), pp. 2276–2291.
-
57)
-
20. Prabukumar, M., Sawant, S., Samiappan, S., et al: ‘Three-dimensional discrete cosine transform-based feature extraction for hyperspectral image classification’, J. Appl. Remote Sens., 2018, 12, (4), pp. 1–19.
-
58)
-
8. Vaddi, R., Prabukumar, M.: ‘Comparative study of feature extraction techniques for hyper spectral remote sensing image classification: a survey’. 2017 Int. Conf. on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2017.
-
59)
-
23. Gastal, E.S.L., Oliveira, M.M.: ‘Domain transform for edge-aware image and video processing’, ACM Trans. Graph., 2011, 30, (4), pp. 1–12. .
-
60)
-
38. Yu, C., Zhao, M., Song, M., et al: ‘Hyperspectral image classification method based on CNN architecture embedding with hashing semantic feature’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2019, 12, (6), pp. 1866–1881, .
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2020.0728
Related content
content/journals/10.1049/iet-ipr.2020.0728
pub_keyword,iet_inspecKeyword,pub_concept
6
6