Abstract
This paper describes the importance of performing preprocessing techniques namely, denoising and dimensionality reduction to the hyperspectral data before classification. The two main problems faced in hyperspectral image processing are noise and higher dimension. Legendre–Fenchel transformation denoises each band in the data while preserving the edge information. To overcome the issue of high data volume, inter-band block correlation coefficient technique followed by singular value decomposition and QR decomposition is utilized to reduce the dimension of hyperspectral image without affecting the critical information. The preprocessed data are classified using kernel-based libraries, namely GURLS and LibSVM. Performance of these techniques is evaluated with accuracy assessment measures. The experiment was performed on five datasets. Experimental analysis shows that the proposed denoising technique increases the classification accuracy. In the case of Indian Pines data, with 10% of the training data, the classification accuracy is improved from 83.5 to 97.3%. And also, dimensionality reduction technique gives good classification accuracy even with 50% reduction in the number of bands. The classification accuracy of the Salinas-A and Pavia University data is 99.4 and 94.6% with the 50% dimensionally reduced (100 and 50 bands, respectively) number of bands. The bands extracted by the dimensionality reduction technique using the denoised hyperspectral data differ from that of the hyperspectral data without denoising. This emphasizes the importance of denoising the dataset before applying dimensionality reduction technique. In case of Pavia University, the band numbers above 50 (out of 100 bands) which were not informative bands before denoising are selected as informative bands by dimension reduction technique after denoising .
Similar content being viewed by others
References
Zhao Y-Q, Yang J (2015) Hyperspectral image denoising via sparse representation and low-rank constraint. IEEE Trans Geosci Remote Sens 53(1):296–308
Zelinski A, Goyal V (2006) Denoising hyperspectral imagery and recovering junk bands using wavelets and sparse approximation. In IEEE international symposium on geoscience and remote sensing, IEEE, pp 387–390
Chen G, Qian S-E (2011) Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage. IEEE Trans Geosci Remote Sens 49(3):973–980
Yuan Q, Zhang L, Shen H (2012) Hyperspectral image denoising employing a spectral-spatial adaptive total variation model. IEEE Trans Geosci Remote Sens 50(10):3660–3677
Santhosh S, Abinaya N, Rashmi G, Sowmya V, Soman K (2014) A novel approach for denoising coloured remote sensing image using Legendre Fenchel transformation. In International Conference on Recent Trends in Information Technology (ICRTIT), IEEE, pp 1–6
Handa A, Newcombe RA, Angeli A, Davison AJ (2011) Applications of Legendre–Fenchel transformation to computer vision problems, vol. 45. Department of Computing at Imperial College London. DTR11-7
Harsanyi JC, Chang C-I (1994) Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach. IEEE Trans Geosci Remote Sens 32(4):779–785
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. Int J Mach Learn Comput 2(3):248
Reshma R, Sowmya V, Soman K (2016) Dimensionality reduction using band selection technique for kernel based hyperspectral image classification. In 6th international conference on advances in computing and communications (ICACC)
Wang J, Chang C-I (2006) Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis. IEEE Trans Geosci Remote Sens 44(6):1586–1600
Bruce LM, Koger CH, Li J (2002) Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction. IEEE Trans Geosci Remote Sens 40(10):2331–2338
Renard N, Bourennane S, Blanc-Talon J (2008) Denoising and dimensionality reduction using multilinear tools for hyperspectral images. IEEE Geosci Remote Sens Lett 5(2):138–142
Zabalza J, Ren J, Zheng J, Zhao H, Qing C, Yang Z, Du P, Marshall S (2016) Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing 185:1–10
Bhushan DB, Sowmya V, Manikandan MS, Soman K (2011) An effective pre-processing algorithm for detecting noisy spectral bands in hyperspectral imagery. In International symposium on ocean electronics (SYMPOL), IEEE, pp 34–39
Soman K, Loganathan R, Ajay V (2009) Machine Learning with SVM and other Kernel methods. PHI Learning Pvt. Ltd., New Delhi
Hsu C-W, Lin C-J (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425
Li J, Bioucas-Dias JM, Plaza A (2010) Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Trans Geosci Remote Sens 48(11):4085–4098
Bernard K, Tarabalka Y, Angulo J, Chanussot J, Benediktsson JA (2012) Spectral-spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach. IEEE Trans Image Process 21(4):2008–2021
Cai TT, Wang L (2011) Orthogonal matching pursuit for sparse signal recovery with noise. IEEE Trans Inf Theory 57(7):4680–4688
Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press, Cambridge
Kotsiantis SB, Zaharakis I, Pintelas P (2007) Supervised machine learning: a review of classification techniques. Informatica 31(1):249–268
Tacchetti A, Mallapragada PK, Santoro M, Rosasco L (2013) Gurls: a least squares library for supervised learning. J Mach Learn Res 14(1):3201–3205
Chang C-C, Lin C-J (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27
Haridas N, Sowmya V, Soman K (2015) Gurls vs libsvm: performance comparison of kernel methods for hyperspectral image classification. Indian J Sci Technol 8(24):1–10
Aswathy C, Sowmya V, Soman K (2015) ADMM based hyperspectral image classification improved by denoising using Legendre Fenchel transformation. Indian J Sci Technol 8(24):1–9
Chen Y, Nasrabadi NM, Tran TD (2011) Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans Geosci Remote Sens 49(10):3973–3985
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest:
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Reshma, R., Sowmya, V. & Soman, K.P. Effect of Legendre–Fenchel denoising and SVD-based dimensionality reduction algorithm on hyperspectral image classification. Neural Comput & Applic 29, 301–310 (2018). https://doi.org/10.1007/s00521-017-3145-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-017-3145-y