Two New Indices for Unsupervised Dimensionality Reduction of Hyperspectral Data

Document Type : Original Article


1 Ms.c of Remote Sensing, Department of Photogrammetry and Remote Sensing, K.N Toosi University of Technology, Tehran, Iran

2 Associate Professor, Department of Photogrammetry and Remote Sensing, K.N Toosi University of Technology, Tehran, Iran


Hyperspectral imaging, with hundreds of spectral bands provide a rich data source for thematic mapping. However, the high dimensionality of these images is a challenge which is commonly treated by dimensionality reduction techniques. In this paper, a pixel-based hyperspectral dimensionality reduction technique is proposed in which the spectral signature curve (SSC) is divided into segments with equal lengths. Afterwards two new mathematical indices, which are based on integral and L2-norm, are proposed to transform each SSC segment into a new feature of the reduced feature vector. The proposed method considers the ordinance of SSC and, unlike PCA, can be applied individually to all pixels independently and simultaneously which means the method is applicable in parallel processing. The proposed data reduction technique was applied to two well-known agricultural hyperspectral scenes and was compared to wavelet and PCA data reduction techniques. The obtained results proved the efficiency of the proposed method from both classification accuracy and processing time aspects.


Lim, S., Sohn, K. H., & Lee, C. (2002). Principal component analysis for compression of hyperspectral images. IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217). IEEE 2001 International Geoscience and Remote Sensing Symposium, Sydney, NSW, Australia. doi:10.1109/igarss.2001.976068
Yang, C., Everitt, J. H., & Johnson, H. B. (2009). Applying image transformation and classification techniques to airborne hyperspectral imagery for mapping Ashe juniper infestations. International Journal of Remote Sensing, 30(11), 2741–2758. doi:10.1080/01431160802555812
Landgrebe, D. A. (2008). Signal theory methods in multispectral remote sensing. Newy York: Wiley-Interscience.
Kuo, B. C., & Landgrebe, D. A. (2004). Nonparametric weighted feature extraction for classification. IEEE Transactions on Geoscience and Remote Sensing, 42(5), 1096-1105.
Lee, C., & Landgrebe, D. A. (1993). Feature extraction based on decision boundaries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(4), 388-400.
Ghosh, J. K., & Somvanshi, A. (2008). Fractal-based dimensionality reduction of hyperspectral images. Journal of the Indian Society of Remote Sensing, 36(3), 235–241. doi:10.1007/s12524-008-0024-0
Hosseini, A., & Ghassemian, H. (2012, Μάιος). Classification of hyperspectral and multispectral images by using fractal dimension of spectral response curve. 20th Iranian Conference on Electrical Engineering (ICEE2012, Tehran, Iran. doi:10.1109/iraniancee.2012.6292587
Hosseini, S. A., & Ghassemian, H. (2013, Μάιος). A new hyperspectral image classification approach using fractal dimension of spectral response curve. 2013 21st Iranian Conference on Electrical Engineering (ICEE). Mashhad, Iran. doi:10.1109/iraniancee.2013.6599552
Hosseini, S. A., & Ghassemian, H. (2016). Rational function approximation for feature reduction in hyperspectral data. Remote sensing letters, 7(2), 101–110. doi:10.1080/2150704x.2015.1101180
Kaewpijit, S., Le Moigne, J., & El-Ghazawi, T. (2003). Automatic reduction of hyperspectral imagery using wavelet spectral analysis. IEEE transactions on geoscience and remote sensing: a publication of the IEEE Geoscience and Remote Sensing Society, 41(4), 863–871. doi:10.1109/tgrs.2003.810712