Abstract
In hyperspectral image classification, it is important to make use of the rich spectral information efficiently and to use the neighborhood information appropriately to alleviate the ‘salt and pepper noise pixel’. This paper presents a new hyperspectral image classification method based on Sparse Feature and Neighborhood Homogeneity (SF-NH). The core idea of SF-NH is to use sparse feature to express the hyperspectral image, and then the classification results preliminarily obtained by the Support Vector Machine (SVM) are revised by the neighborhood homogeneity. Experimental results on two classical hyperspectral data (i.e., Indian Pines, Saunas data) show that the proposed SF-NH method can greatly improve the classification accuracy.
Similar content being viewed by others
References
Aharon, M., Elad, M., & Bruckstein, A. (2006). K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(1), 4311–4322.
Camps-Valls, G., & Gomez-Chova, L. (2006). Composite kernels for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 3(1), 93–97.
Charles, A. S., Olshausen, B. A., & Rozell, C. J. (2011). Learning sparse codes for hyperspectral imagery. IEEE Journal of Selected Topics in Signal Processing, 5(5), 963–978.
Chen, Y., Nasrabadi, N. M., & Tran, T. D. (2013). Hyperspectral image classification via kernel sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 51(1), 217–231.
Elad, M. (2010). Sparse and redundant representations: From theory to applications in signal and image processing. New York: Springer.
Engan, K., Aase, S. O., & Husey, J. H. (1999). Method of optimal directions for frame design. IEEE International Conference on Acoustics, Speech, and Signal Processing, 5, 2443–2446.
Fauvel, M., Tarabalka, Y., & Benediktsson, J. A. (2013). Advances in Spectral–Spatial Classification of Hyperspectral Images. Proceedings of the IEEE, 101(3), 652–675.
Gualtieri, J. A., & Cromp, R. F. (1998). Support vector machines for hyperspectral remote sensing classification. Proceedings of the SPIE, 3584, 221–232.
Gurram, P., & Kwon, H. (2013). Sparse kernel-based ensemble learning with fully optimized kernel parameters for hyperspectral classification problems. IEEE Transactions on Geoscience and Remote Sensing, 51(2), 787–802.
Haq, Q. S., Tao, L. M., Sun, F. C., & Yang, S. Q. (2012). A fast and robust sparse approach for hyperspectral data classification using a few labeled samples. IEEE Transactions on Geoscience and Remote Sensing, 50(6), 2287–2302.
Hebiri, M., & Lederer, J. (2013). How correlations influence lasso prediction. IEEE Transactions on Information Theory, 59(3), 1846–1854.
Iordache, D., Bioucas-Dias, J. M., & Plaza, A. (2012). Total variation spatial regularization for sparse hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 50(11), 4482–4502.
Jang, J. H., Kim, S. D., & Ra, J. B. (2011). Enhancement of optical remote sensing images by subband-decomposed multiscale retinex with hybrid intensity transfer function. IEEE Geoscience and Remote Sensing Letters, 8(5), 983–987.
Jiménez, L. O., Rivera-Medina, J. L., & Rodrigues-Diaz, E. (2005). Integration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 43(4), 844–851.
Jong, S. M., Hornstra, T., & Maas, H. G. (2001). An integrated spatial and spectral approach to the classification of mediterranean land cover types: The SSC method. Journal of Applied Geosciences, 3(2), 176–183.
Kettig, R. L., & Landgrebe, D. A. (1976). Classification of multispectral image data by extraction and classification of homogeneous objects. IEEE Transaction on Geoscience Electronies, 14(1), 19–26.
Kuo, B. C., Ho, H. H., & Li, C. H. (2014). A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1), 317–326.
Li, S. S., Zhang, B., & Li, A. (2013). Hyperspectral imagery clustering with neighborhood constraints. IEEE Geoscience and Remote Sensing Letters, 10(3), 588–592.
Liu, J. J., Wu, Z. B., & Wei, Z. H. (2013). Spatial-spectral kernel sparse representation for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(6), 2462–2471.
Mairal, J., & Bach, F. (2010). Online learning for matrix factorization and sparse coding. Journal of Machine Learning Research, 11(1), 19–60.
Melgani, F., & Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(8), 1778–1790.
Pasolli, E., Melgani, F., & Tuia, D. (2014). SVM active learning approach for image classification using spatial information. IEEE Transactions on Geoscience and Remote Sensing, 52(4), 2217–2233.
Qian, Y. T., Ye, M. C., & Zhou, J. (2012). Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features. IEEE Transactions on Geoscience and Remote Sensing, 51(4), 2276–2291.
Rabbani, H. T., & Ghaoui, L. E. (2013). Online homotopy algorithm for a generalization of the LASSO. IEEE Transactions on Automatic Control, 58(12), 3175–3179.
Rubinstein, R., Zibulevsky, M., & Elad, M. (2010). Double sparsity. Learning sparse dictionaries for sparse signal approximation. IEEE Transactions on Signal Processing, 58(3), 1553–1564.
Santos, J. A., Gosselin, P. H., & Philipp-Foliguet, S. (2012). Multiscale classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 50(10), 3764–3775.
Santos, J. A., Gosselin, P. H., & Philipp-Foliguet, S. (2013). Interactive multiscale classification of high-resolution remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(4), 2020–2034.
Smith, L. N., & Elad, M. (2013). Improving dictionary learning:multiple dictionary updates and coefficient reuse. IEEE Signal Processing Letters, 20(1), 79–82.
Song, X. F., Jiao, L. C., Yang, S. Y., Zhang, X. R., & Shang, F. H. (2013). Sparse coding and classifier ensemble based multi-instance learning for image categorization. Signal Processing, 93(1), 1–11.
Tarabalka, Y., Benediktsson, J. A., & Chanussot, J. (2010). Multiple spectral–spatial classification approach for hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 48(11), 4122–4132.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B (Methodological), 58(1), 267–288.
Wang, Q. M., & Shi, W. Z. (2013). Unsupervised classification based on fuzzy c-means with uncertainty analysis. Remote Sensing Letters, 4(11), 1087–1096.
Wang, L. G., Liu, D. F., & Wang, Q. M. (2013a). Spectral unmixing model based on least squares support vector machine with unmixing residue constraints. IEEE Geoscience and Remote Sensing Letters, 10(6), 1592–1596.
Wang, L. G., Liu, D. F., & Zhao, L. (2013b). A color visualization method based on sparse representation of hyperspectral imagery. Applied Geophysics, 10(2), 210–221.
Wright, J., Yang, A. Y., & Ganesh, A. (2009). Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), 210–227.
Wright, J., Ma, Y., & Mairal, J. (2010). Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE, 98(6), 1031–1044.
Xu, L. L., & Li, J. T. (2014). Bayesian classification of hyperspectral imagery based on probabilistic sparse representation and markov random field. IEEE Geoscience and Remote Sensing Letters, 11(4), 823–827.
Yang, L. X., Yang, S. Y., & Jin, P. L. (2014a). Semi-supervised hyperspectral image classification using spatio-spectral laplacian support vector machine. IEEE Geoscience and Remote Sensing Letters, 11(3), 651–655.
Yang, S. Y., Jin, H. H., & Wang, M. (2014b). Data-driven compressive sampling and learning sparse coding for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 11(2), 479–483.
Yang, S. Y., Jin, H. H., & Yang, L. X. (2014c). Compressive sensing-inspired dual-sparse SLFNN for hyperspectral imagery classification. IEEE Geoscience and Remote Sensing Letters, 11(1), 220–224.
Zhou, M. Y., Chen, H. J., & Paisley, J. (2012). Nonparametric bayesian. Dictionary learning for analysis of noisy and incomplete images. IEEE Transactions on Image Processing, 21(1), 130–144.
Acknowledgements
The work was supported by the National Natural Science Foundation of China (Grant No. 61275010), Ph.D. Programs Foundation of Ministry of Education of China (Grant No. 20132304110007), and the Heilongjiang Natural Science Foundation (Grant No. F201409), and the Fundamental Research Funds for the Central Universities (Grant No. HEUCFD1410). We would like to thank Qunming Wang of the Hong Kong Polytechnic University for his careful proofreading, good suggestions and helpful discussions. The authors also would like to thank the handling editor and the reviewers for providing valuable comments.
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Yang, J., Wang, L. & Qian, J. Hyperspectral Imagery Classification Based on Sparse Feature and Neighborhood Homogeneity. J Indian Soc Remote Sens 43, 445–457 (2015). https://doi.org/10.1007/s12524-014-0420-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-014-0420-6