An Adaptive Dimensionality Reduction Approach for Hyperspectral Imagery Semantic Interpretation
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.
[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).
[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).
@article{"International Journal of Information, Control and Computer Sciences:74875", author = "Akrem Sellami and Imed Riadh Farah and Basel Solaiman", title = "An Adaptive Dimensionality Reduction Approach for Hyperspectral Imagery Semantic Interpretation", 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.", volume = "11", number = "2", pages = "213-8", }
