An Adaptive Dimensionality Reduction Approach for Hyperspectral Imagery Semantic Interpretation

Authors: Akrem Sellami, Imed Riadh Farah, Basel Solaiman

Abstract:

With the development of HyperSpectral Imagery (HSI) technology, the spectral resolution of HSI became denser, which resulted in large number of spectral bands, high correlation between neighboring, and high data redundancy. However, the semantic interpretation is a challenging task for HSI analysis due to the high dimensionality and the high correlation of the different spectral bands. In fact, this work presents a dimensionality reduction approach that allows to overcome the different issues improving the semantic interpretation of HSI. Therefore, in order to preserve the spatial information, the Tensor Locality Preserving Projection (TLPP) has been applied to transform the original HSI. In the second step, knowledge has been extracted based on the adjacency graph to describe the different pixels. Based on the transformation matrix using TLPP, a weighted matrix has been constructed to rank the different spectral bands based on their contribution score. Thus, the relevant bands have been adaptively selected based on the weighted matrix. The performance of the presented approach has been validated by implementing several experiments, and the obtained results demonstrate the efficiency of this approach compared to various existing dimensionality reduction techniques. Also, according to the experimental results, we can conclude that this approach can adaptively select the relevant spectral improving the semantic interpretation of HSI.

Keywords: Band selection, dimensionality reduction, feature extraction, hyperspectral imagery, semantic interpretation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1128851

References:

[1] H. Huang, J. Li, and J. Liu, “Enhanced semi-supervised local fisher discriminant analysis for face recognition,” Future Generation Computer Systems, vol. 28, no. 1, pp. 244–253, 2012.
[2] S. Chen and D. Zhang, “Semisupervised dimensionality reduction with pairwise constraints for hyperspectral image classification,” Geoscience and Remote Sensing Letters, IEEE, vol. 8, no. 2, pp. 369–373, 2011.
[3] H. Huang and M. Yang, “Dimensionality reduction of hyperspectral images with sparse discriminant embedding,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 53, no. 9, pp. 5160–5169, 2015.
[4] A. Radoi, R. Tanase, and M. Datcu, “Semantic interpretation of multi-level change detection in multi-temporal satellite images,” in Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International. IEEE, 2015, pp. 4157–4160.
[5] M. Ivaˇsi´c-Kos, M. Pavli´c, and M. Mateti´c, “Data preparation for semantic image interpretation,” in Information Technology Interfaces (ITI), 2010 32nd International Conference on. IEEE, 2010, pp. 181–186.
[6] W. Li, S. Prasad, J. E. Fowler, and L. M. Bruce, “Locality-preserving dimensionality reduction and classification for hyperspectral image analysis,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 50, no. 4, pp. 1185–1198, 2012.
[7] J. Khoder, R. Younes, and F. B. Ouezdou, “Stability of dimensionality reduction methods applied on artificial hyperspectral images,” in Computer Vision and Graphics. Springer, 2012, pp. 465–474.
[8] J. Feng, L. Jiao, F. Liu, T. Sun, and X. Zhang, “Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images,” Pattern Recognition, vol. 51, pp. 295–309, 2016.
[9] J. M. Sotoca and F. Pla, “Supervised feature selection by clustering using conditional mutual information-based distances,” Pattern Recognition, vol. 43, no. 6, pp. 2068–2081, 2010.
[10] B. Guo, R. I. Damper, S. R. Gunn, and J. D. Nelson, “A fast separability-based feature-selection method for high-dimensional remotely sensed image classification,” Pattern Recognition, vol. 41, no. 5, pp. 1653–1662, 2008.
[11] L. Zhang, C. Chen, J. Bu, and X. He, “A unified feature and instance selection framework using optimum experimental design,” Image Processing, IEEE Transactions on, vol. 21, no. 5, pp. 2379–2388, 2012.
[12] S. B. Kim and P. Rattakorn, “Unsupervised feature selection using weighted principal components,” Expert systems with applications, vol. 38, no. 5, pp. 5704–5710, 2011.
[13] C.-I. Chang and S. Wang, “Constrained band selection for hyperspectral imagery,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 44, no. 6, pp. 1575–1585, 2006.
[14] W. Jian, “Unsupervised intrusion feature selection based on genetic algorithm and fcm,” in Information Engineering and Applications. Springer, 2012, pp. 1005–1012.
[15] M. Breaban and H. Luchian, “A unifying criterion for unsupervised clustering and feature selection,” Pattern Recognition, vol. 44, no. 4, pp. 854–865, 2011.
[16] P. Mitra, C. Murthy, and S. K. Pal, “Unsupervised feature selection using feature similarity,” IEEE transactions on pattern analysis and machine intelligence, vol. 24, no. 3, pp. 301–312, 2002.
[17] A. MartI´nez-UsO´Martinez-Uso, F. Pla, J. M. Sotoca, and P. Garc´ıa-Sevilla, “Clustering-based hyperspectral band selection using information measures,” IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 12, pp. 4158–4171, 2007.
[18] Y. Qian, F. Yao, and S. Jia, “Band selection for hyperspectral imagery using affinity propagation,” IET Computer Vision, vol. 3, no. 4, pp. 213–222, 2009.
[19] B.-C. Kuo, C.-H. Li, and J.-M. Yang, “Kernel nonparametric weighted feature extraction for hyperspectral image classification,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 47, no. 4, pp. 1139–1155, 2009.
[20] M. Sugiyama, “Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis,” The Journal of Machine Learning Research, vol. 8, pp. 1027–1061, 2007.
[21] P. Deepa and K. Thilagavathi, “Feature extraction of hyperspectral image using principal component analysis and folded-principal component analysis,” in Electronics and Communication Systems (ICECS), 2015 2nd International Conference on. IEEE, 2015, pp. 656–660.
[22] L. Ding, P. Tang, and H. Li, “Isomap-based subspace analysis for the classification of hyperspectral data,” in Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International. IEEE, 2013, pp. 429–432.
[23] S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang, and S. Lin, “Graph embedding and extensions: a general framework for dimensionality reduction,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 29, no. 1, pp. 40–51, 2007.
[24] R. Ji, Y. Gao, R. Hong, Q. Liu, D. Tao, and X. Li, “Spectral-spatial constraint hyperspectral image classification,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 52, no. 3, pp. 1811–1824, 2014.
[25] H. Yuan and Y. Y. Tang, “Learning with hypergraph for hyperspectral image feature extraction,” Geoscience and Remote Sensing Letters, IEEE, vol. 12, no. 8, pp. 1695–1699, 2015.
[26] N. Renard and S. Bourennane, “Dimensionality reduction based on tensor modeling for classification methods,” IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 4, pp. 1123–1131, 2009.
[27] A. Sellami and I. R. Farah, “High-level hyperspectral image classification based on spectro-spatial dimensionality reduction,” Spatial Statistics, vol. 16, pp. 103–117, 2016.
[28] R. Clark, “Spectral library,” https://speclab.cr.usgs.gov/spectral-lib. html/, 2007, (Online; accessed 19-September-2007).