Abstract
A significant challenge in hyperspectral remote sensing image analysis is the presence of noise, which has a negative impact on various data analysis methods such as image classification, target detection, unmixing etc. In order to address this issue, hyperspectral image denoising is used as a preprocessing step prior to classification. This paper presents an effective, fast and reliable method for denoising hyperspectral images followed by classification based on sparse representation of hyperspectral data. The use of Legendre-Fenchel transform for denoising is an effective spatial preprocessing step to improve the classification accuracy. The main advantage of Legendre-Fenchel transform is that it removes the noise in the image while preserving the sharp edges. The sparsity based algorithm namely, Orthogonal Matching Pursuit (OMP) is used for classification. The experiment is done on Indian Pines data set acquired by Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor. It is inferred that the denoising of hyperspectral images before classification improves the Overall Accuracy of classification. The effect of preprocessing using Legendre Fenchel transformation is shown by comparing the classification results with Total Variation (TV) based denoising. A statistical comparison of the accuracies obtained on standard hyperspectral data before and after denoising is also analysed to show the effectiveness of the proposed method. The experimental result analysis shows that for 10\(\%\) training set the proposed method leads to the improvement in Overall Accuracy from 83.18\(\%\) to 91.06\(\%\), Average Accuracy from 86.17\(\%\) to 92.78\(\%\) and Kappa coefficient from 0.8079 to 0.8981.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bioucas-Dias, J., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., Chanussot, J.: Hyperspectral remote sensing data analysis and future challenges. IEEE Geoscience and Remote Sensing Magazine 1(2), 6–36 (2013)
Zelinski, A.C., Goyal, V.K.: Denoising hyperspectral imagery and recovering junk bands using wavelets and sparse approximation. In: IEEE International Conference on Geoscience and Remote Sensing Symposium, IGARSS 2006, pp. 387–390. IEEE (2006)
Yuan, Q., Zhang, L., Shen, H.: Hyperspectral image denoising employing a spectral-spatial adaptive total variation model. IEEE Transactions on Geoscience and Remote Sensing 50(10), 3660–3677 (2012)
Soman, K.P., Kavitha, B., Sowmya, V.: Spatial preprocessing for improved sparsity based hyperspectral image classification. International Journal of Engineering Research and Technology 1. ESRSA, July 2012
Chen, Y., Nasrabadi, N.M., Tran, T.D.: Hyperspectral image classification using dictionary-based sparse representation. IEEE Transactions on Geoscience and Remote Sensing 49(10), 3973–3985 (2011)
Santhosh, S., Abinaya, N., Rashmi, G., Sowmya, V., Soman, K.P.: A novel approach for denoising coloured remote sensing image using legendre fenchel transformation. In: 2014 International Conference on Recent Trends in Information Technology (ICRTIT), pp. 1–6. IEEE (2014)
Handa, A., Newcombe, R.A., Angeli, A., Davison, A.J.: Applications of legendre-fenchel transformation to computer vision problems, Tech. Rep., Tech. Rep. DTR11-7, Department of Computing at Imperial College London (2011)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60(1), 259–268 (1992)
Suchithra, M., Sukanya, P., Prabha, P., Sikha, O.K., Sowmya, V., Soman, K.P.: An experimental study on application of orthogonal matching pursuit algorithm for image denoising. In: 2013 International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), pp. 729–736. IEEE (2013)
Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory 53(12), 4655–4666 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Haridas, N., Aswathy, C., Sowmya, V., Soman, K.P. (2016). Hyperspectral Image Denoising Using Legendre-Fenchel Transform for Improved Sparsity Based Classification. In: Berretti, S., Thampi, S., Srivastava, P. (eds) Intelligent Systems Technologies and Applications. Advances in Intelligent Systems and Computing, vol 384. Springer, Cham. https://doi.org/10.1007/978-3-319-23036-8_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-23036-8_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23035-1
Online ISBN: 978-3-319-23036-8
eBook Packages: EngineeringEngineering (R0)