Hyperspectral Image Classification Using Dictionary-Based Sparse Representation
Top Cited Papers
- 12 May 2011
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 49 (10), 3973-3985
- https://doi.org/10.1109/tgrs.2011.2129595
Abstract
A new sparsity-based algorithm for the classification of hyperspectral imagery is proposed in this paper. The proposed algorithm relies on the observation that a hyperspectral pixel can be sparsely represented by a linear combination of a few training samples from a structured dictionary. The sparse representation of an unknown pixel is expressed as a sparse vector whose nonzero entries correspond to the weights of the selected training samples. The sparse vector is recovered by solving a sparsity-constrained optimization problem, and it can directly determine the class label of the test sample. Two different approaches are proposed to incorporate the contextual information into the sparse recovery optimization problem in order to improve the classification performance. In the first approach, an explicit smoothing constraint is imposed on the problem formulation by forcing the vector Laplacian of the reconstructed image to become zero. In this approach, the reconstructed pixel of interest has similar spectral characteristics to its four nearest neighbors. The second approach is via a joint sparsity model where hyperspectral pixels in a small neighborhood around the test pixel are simultaneously represented by linear combinations of a few common training samples, which are weighted with a different set of coefficients for each pixel. The proposed sparsity-based algorithm is applied to several real hyperspectral images for classification. Experimental results show that our algorithm outperforms the classical supervised classifier support vector machines in most cases.Keywords
This publication has 56 references indexed in Scilit:
- Local Manifold Learning-Based $k$ -Nearest-Neighbor for Hyperspectral Image ClassificationIEEE Transactions on Geoscience and Remote Sensing, 2010
- Recent advances in techniques for hyperspectral image processingRemote Sensing of Environment, 2009
- Sparse Representation for Classification of Tumors Using Gene Expression DataJournal of Biomedicine and Biotechnology, 2009
- An Interior-Point Method for Large-Scale -Regularized Least SquaresIEEE Journal of Selected Topics in Signal Processing, 2007
- $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse RepresentationIEEE Transactions on Signal Processing, 2006
- Simultaneous approximation by greedy algorithmsAdvances in Computational Mathematics, 2006
- Preprocessing eo-1 hyperion hyperspectral data to support the application of agricultural indexesIEEE Transactions on Geoscience and Remote Sensing, 2003
- Spatially smooth partitioning of hyperspectral imagery using spectral/spatial measures of disparityIEEE Transactions on Geoscience and Remote Sensing, 2003
- Detection algorithms for hyperspectral imaging applicationsIEEE Signal Processing Magazine, 2002
- Anomaly detection from hyperspectral imageryIEEE Signal Processing Magazine, 2002