Abstract
Dimensionality reduction strategies can be broadly categorized as band selection and feature extraction. Researchers and analysts from the remote sensing community give greater preference to band selection over feature extraction as the latter modifies the original reflectance values of hyperspectral data, making it difficult to understand the behavior of the materials in terms of their reflectance values. However, feature extraction strategies have their own advantages which cannot be ignored. Thus, a two-level, PCA-based band selection framework is proposed to unify the two dimensionality reduction strategies so that benefits of both the strategies can be derived. The proposed approach selects bands based on their relationship with a given set of principal components explained in terms of component loadings, thus keeping the original bands intact. Additionally, contrary to the popular notion that the complete information of all bands is adequately coalesced in the top principal components, middle principal components play a far stronger discriminative role when the competing classes are spectrally confusing to each other. Thus, for each level of classification, a different range of principal components is used to select the bands, on the basis of the level of spectral similarity expected between the classes at each level. Experimental results indicate that the proposed two-level band selection algorithm can select bands with varying levels of discriminative capabilities to effectively classify hyperspectral images consisting of classes spectrally very similar in nature.
Similar content being viewed by others
Data Availability
The datasets used in the current study are publicly archived at the official website of the computational Intelligence Group of the University of the Basque Country.
References
Allegrini, A., Bajocco, S., Merola, P., & Mandrone, S. (2005). Hydrological studies using hyperspectral remote-sensed data: monitoring thermal anomalies and census of internal water bodies. In Proceedings of international symposium and exhibition on geoinformation.
Artigas, F. J., & Yang, J. S. (2005). Hyperspectral remote sensing of marsh species and plant vigour gradient in the New Jersey Meadowlands. Photogrammetric Engineering and Remote Sensing, 26(23), 5209–5220.
Arzuaga-Cruz, E., Jimenez-Rodriguez, L. O., & Velez-Reyes, M. (2003). Unsupervised feature extraction and band subset selection techniques based on relative entropy criteria for hyperspectral data analysis. AeroSense, 2003, 462–473.
Baisantry, M., Sao, A. K., & Shukla, D. P. (2018) Two-level feature extraction framework for hyperspectral image classification. In Proceedings of 9th workshop on hyperspectral image and signal processing: Evolution in remote sensing (WHISPERS) (pp. 1–5).
Brusco, M. J., & Köhn, H. F. (2008). Clustering by passing messages between data points. Science, 319(5864), 972–976.
Cadima, J., & Jolliffe, I. T. (1995). Loading and correlations in the interpretation of principle compenents. Journal of Applied Statistics, 22(2), 203–214.
Cai, Y., Liu, X., & Cai, Z. (2019). BS-nets: An end-to-end framework for band selection of hyperspectral image. IEEE Transactions on Geoscience and Remote Sensing, 58(3), 1969–1984. https://doi.org/10.1109/TGRS.2019.2951433.
Cathcart, J. M. (2008) Detection and sensing of mines, explosive objects, and obscured targets. In Proceedings of SPIE disturbed soil characterization workshop.
Chang, C. I., Du, Q., Sun, T. L., & Althouse, M. L. G. (1999). A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 37(6), 2631–2641.
Cheriyadat, A., & Bruce, L. M. (2003) Why principal component analysis is not an appropriate feature extraction method for hyperspectral data. In Proceedings of IEEE international geoscience and remote sensing symposium (Vol. 6, pp. 3420–3422).
Crósta, A., De Roberto, C., & Souza, Filho. (2000). Hyperspectral remote sensing for mineral mapping: a case-study at alto Paraíso de Goías, central Brazil. Revista Brasileira de Geociências, 30(3), 551–554.
Crosta, A. P., & Moore, J. M. (1989). Enhancement of landsat themetic mapper imagery for residual soil mapping in SW Minas Gerais State, Brazil: A prospecting case history in Greenstone Belt Terrain. In Proceedings of the 7th thematic conference on remote sensing for exploration geology (pp. 1173–1187).
Davies, D., & Bouldin, D. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224–227.
Du, H., Qi, H., Wang, X., Ramanath, R., & Snyder, W. E. (2003). Band selection using independent components analysis for hyperspectral image processing. In Proceedings of 32nd Applied Imagery Pattern Recognition Workshop (pp. 93–98).
Dunteman, G. H. (1969). Using principal components to select a subset of variables. principal component analysis. Sage University Papers Series.
Green, A., Berman, M., Switzer, P., & Craig, M. (1988). A transform for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 26(1), 65–74.
Group CI. (2011). Hyperspectral remote sensing scenes [online]. http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes. Accessed 9 Feb 2018.
Guo, B., et al. (2006). Band selection for hyperspectral image classification using mutual information. IEEE Geoscience and Remote Sensing Letters, 3(4), 522–526.
Herve, A., & Williams Lynne, J. (2010). Principal component analysis. WIREs Computational Statistics, 2, 433–459.
Huber-Lerner, M., Hadar, O., Rotman, S. R., & Huber-Shalem, R. (2016). Hyperspectral band selection for anomaly detection: The role of data gaussianity. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(2), 732–743.
Hughes, G. (1968). On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory, 14(1), 55–63.
Ifarraguerri, A., & Prairie, M. W. (2004). Visual method for spectral band selection. IEEE Geoscience and Remote Sensing Letters, 1(2), 101–106.
Jia, X., Kuo, B. C., & Crawford, M. M. (2013) Feature mining for hyperspectral image classification. In Proceedings of IEEE.
Jia, S., Qian, Y., & Ji, Z. (2008) Band selection for hyperspectral imagery using affinity propagation. In Proceedings of DICTA’08. digital image computing: techniques and applications (pp. 137–141).
Jia, X., & Richards, J. A. (1999). Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification. IEEE Transactions on Geoscience and Remote Sensing, 37(1), 538–542.
Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374, 20150202.
Kohavi, R. (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the fourteenth international joint conference on artificial intelligence (pp. 1137–1143).
Koonsanit, K., Jaruskulchai, C., & Eiumnoh, A. (2012). Band selection for dimension reduction in hyper spectral image using integrated information gain and principal components analysis technique. International Journal of Machine Learning and Computing, 2(3), 248–251.
Li, S., & Qi, H. (2011). Sparse representation based band selection for hyperspectral images. In Proceedings of International Conference on Image Processing, ICIP (pp. 2693–2696).
Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. Journal of Applied Statistics International Journal of Remote Sensing, 28(5), 823–870.
Mausel, P., Kramber, W., & Lee, J. (1990). Optimum band selection for supervised classification of multispectral data. Photogrammetric Engineering & Remote Sensing, 56, 55–60.
O’Toole, A. J., Abdi, H., Deffenbacher, K. A., & Valentin, D. (1993). Low-dimensional representation of faces in higher dimensions of the face space. Journal of the Optical Society of America, 10(3), 405–411.
O’Toole, A., Roark, D., & Abdi, H. (2002). Recognizing moving faces: A psychological and neural synthesis. Trends in cognitive sciences, 6, 261–266.
Ramakrishnan, D., & Bharti, R. (2015). Hyperspectral remote sensing and geological applications. Current Science, 108, 879–891.
Sharma, P., Abrol, V., Dileep, A. D., & Sao, A. K. (2018). Sparse coding based features for speech units classification. Computer Speech Language, 47, 333–350.
Shejin, T., & Sao, A. K. (2012). Significance of dictionary for sparse coding based face recognition. In Proceedings of the international conference of biometrics special interest group (BIOSIG), Darmstadt (pp. 1–6).
Sun, W., Zhang, L., Du, B., Li, W., & Mark, L. Y. (2015). Band selection using improved sparse subspace clustering for hyperspectral imagery classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), 2784–2797.
Thenkabail, P. S., Gumma, M. K., Teluguntla, P., & Mohammed, I. A. (2014). Hyperspectral remote sensing of vegetation and agricultural crops. Photogrammetric Engineering and Remote Sensing, 80(4), 697–709.
Theodoridis, S., & Koutroumbas, K. (2009). Pattern recognition (4th ed.). Cambridge: Academic Press.
Torbick, N., & Becker, B. (2009). Evaluating principal components analysis for identifying optimal bands using wetland hyperspectral measurements from the Great Lakes, USA. Remote Sensing, 1, 408–417.
Valentin, D., Abdi, H., & O’Toole, A. J. (1994). Categorization and identification of human face images by neural networks: A review of the linear autoassociative and principal component approaches. Journal of Biological Systems, 2(3), 413–429.
Wang, Q., Li, Q., & Li, X. (2019). Hyperspectral band selection via adaptive subspace partition strategy. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
Yingzi, D., Chang, C., Ren, H., Chang, C., Jensen, J., & D’Amico, F. (2004). New hyperspectral discrimination measure for spectral characterization. Optical Engineering, 43, 1777–1786.
Zaatour, R., Bouzidi, S., & Zagrouba, E. (2017) Independent component analysis-based band selection techniques for hyperspectral images analysis. Journal of Applied Remote Sensing, 11(2), 026006.
Zhai, H., Zhang, H., Zhang, L., & Li, P. (2019). Laplacian-Regularized Low-Rank Subspace Clustering For Hyperspectral Image Band Selection. IEEE Transactions on Geoscience and Remote Sensing, 57(3), 1723–1740.
Zou, H., & Xue, L. (2018). A selective overview of sparse principal component analysis. Proceedings of the IEEE, 106(8), 1311–1320.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Baisantry, M., Sao, A.K. & Shukla, D.P. Two-Level Band Selection Framework for Hyperspectral Image Classification. J Indian Soc Remote Sens 49, 843–856 (2021). https://doi.org/10.1007/s12524-020-01262-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-020-01262-w