Abstract
Designing active learning (AL) techniques to determine informative training samples for hyperspectral image (HSI) classification is an open research issue. In this chapter, several spectral–spatial AL techniques are designed by exploiting both, an interesting approach to fuse spectral and spatial information and different combinations of existing uncertainty and diversity criteria. In order to fuse spectral and spatial information, the dimensionality of HSI is reduced and mean filtering (for incorporation of spatial information) is applied to each component image in the reduced domain of HSI considering multiple windows of different sizes. The filtered images are concatenated with original component images to form an extended spatial profile for the HSI. These spectral–spatial features are used with different combinations of uncertainty and diversity criteria to design several spectral–spatial AL techniques for determining most informative pixels. The experiments carried out on two real HSI data sets show that the spectral–spatial AL methods are more robust than the AL methods based on spectral measurements only.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
References
Benediktsson, J.A., Palmason, J.A., Sveinsson, J.R.: Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans. Geosci. Remote. Sens. 43(3), 480–491 (2005)
Bhardwaj, K., Patra, S.: An unsupervised technique for optimal feature selection in attribute profiles for spectral-spatial classification of hyperspectral images. ISPRS J. Photogramm. Remote. Sens. 138, 139–150 (2018)
Brinker, K.: Incorporating diversity in active learning with support vector machines. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp. 59–66 (2003)
Bruzzone, L., Chi, M., Marconcini, M.: A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Trans. Geosci. Remote. Sens. 44(11), 3363–3373 (2006)
Campbell, C., Cristianini, N., Smola, A. et al.: Query learning with large margin classifiers. In: ICML, pp. 111–118 (2000)
Camps-Valls, G., Marsheva, T.V.B., Zhou, D.: Semi-supervised graph-based hyperspectral image classification. IEEE Trans. Geosci. Remote. Sens. 45(10), 3044–3054 (2007)
Camps-Valls, G., Tuia, D., Bruzzone, L., Benediktsson, J.A.: Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE Signal Process. Mag. 31(1), 45–54 (2014)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)
Demir, B., Persello, C., Bruzzone, L.: Batch-mode active-learning methods for the interactive classification of remote sensing images. IEEE Trans. Geosci. Remote. Sens. 49(3), 1014–1031 (2011)
Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning. IEEE Trans. Geosci. Remote. Sens. 51(2), 844–856 (2013)
Pasolli, E., Melgani, F., Tuia, D., Pacifici, F., Emery, W.J.: SVM active learning approach for image classification using spatial information. IEEE Trans. Geosci. Remote. Sens. 52(4), 2217–2233 (2014)
Patra, S., Bhardwaj, K., Bruzzone, L.: A spectral-spatial multicriteria active learning technique for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 10(12), 5213–5227 (2017)
Patra, S., Bruzzone, L.: A fast cluster-assumption based active-learning technique for classification of remote sensing images. IEEE Trans. Geosci. Remote. Sens. 49(5), 1617–1626 (2011)
Patra, S., Bruzzone, L.: A batch-mode active learning technique based on multiple uncertainty for SVM classifier. IEEE Geosci. Remote. Sens. Lett. 9(3), 497–501 (2012)
Patra, S., Bruzzone, L.: A novel som-svm-based active learning technique for remote sensing image classification. IEEE Trans. Geosci. Remote. Sens. 52(11), 6899–6910 (2014)
Singla, A., Patra, S.: A fast partition-based batch-mode active learning technique using svm classifier. Soft Comput. 22(14), 4627–4637 (2018)
Tuia, D., Ratle, F., Pacifici, F., Kanevski, M.F., Emery, W.J.: Active learning methods for remote sensing image classification. IEEE Trans. Geosci. Remote. Sens. 47(7), 2218–2232 (2009)
Xu, Z., Yu, K., Tresp, V., Xu, X., Wang, J.: Representative sampling for text classification using support vector machines. In: European Conference on Information Retrieval, pp. 393–407. Springer, Berlin (2003)
Acknowledgements
This work is supported in part by Science and Engineering Research Board, Government of India.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rajbanshi, S., Bhardwaj, K., Patra, S. (2020). Spectral–Spatial Active Learning Techniques for Hyperspectral Image Classification. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_30
Download citation
DOI: https://doi.org/10.1007/978-981-13-8676-3_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8675-6
Online ISBN: 978-981-13-8676-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)