Sparse representation within disconnected spatial support for target detection in hyperspectral imagery

Target detection (TD) is one of the fundamental tasks in hyperspectral imagery (HSI) processing. Sparse representation (SR) as a novel tool is powerful in accurate detection of target of interest. In this paper, SR approach within disconnected spatial support is proposed for effective TD in HSI. For conventional sparse representation, an HSI pixel is represented as a sparse vector whose non-zero entries correspond to the weights of the selected training atoms from a structured dictionary. For improved sparse representation, spatial correlation and spectral similarity of pixels in the whole image are exploited in this context. The pixels within disconnected spatial are automatically determined using similarity compare strategy. Accordingly, a solution based on greedy pursuit algorithms is also given to solve the extended optimization problem in recovering the desired sparse representation. Comprehensive experiments on two different datasets using both visual inspection and quantitative evaluation are carried out. The results from the two datasets have indicated that the proposed approaches help to generate improved results in terms of efficacy and efficiency.

[1]  Fuchun Sun,et al.  A Fast and Robust Sparse Approach for Hyperspectral Data Classification Using a Few Labeled Samples , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Nasser Kehtarnavaz,et al.  Proceedings of SPIE - The International Society for Optical Engineering , 1991 .

[3]  Nasser M. Nasrabadi,et al.  Automated Hyperspectral Cueing for Civilian Search and Rescue , 2009, Proceedings of the IEEE.

[4]  Daoqiang Zhang,et al.  Semisupervised Dimensionality Reduction With Pairwise Constraints for Hyperspectral Image Classification , 2011, IEEE Geoscience and Remote Sensing Letters.

[5]  Trac D. Tran,et al.  Simultaneous sparse recovery for unsupervised hyperspectral unmixing , 2011, Defense + Commercial Sensing.

[6]  Chunhui Zhao,et al.  Improved sparse representation using adaptive spatial support for effective target detection in hyperspectral imagery , 2013 .

[7]  Laurent Tits,et al.  The Potential and Limitations of a Clustering Approach for the Improved Efficiency of Multiple Endmember Spectral Mixture Analysis in Plant Production System Monitoring , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Dimitris G. Manolakis,et al.  Detection algorithms for hyperspectral imaging applications , 2002, IEEE Signal Process. Mag..

[9]  Trac D. Tran,et al.  Sparse Representation for Target Detection in Hyperspectral Imagery , 2011, IEEE Journal of Selected Topics in Signal Processing.

[10]  Sildomar T. Monteiro,et al.  Evaluating Classification Techniques for Mapping Vertical Geology Using Field-Based Hyperspectral Sensors , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Guillermo Sapiro,et al.  Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.

[12]  Hairong Qi,et al.  Sparse representation based band selection for hyperspectral images , 2011, 2011 18th IEEE International Conference on Image Processing.

[13]  Olgica Milenkovic,et al.  Subspace Pursuit for Compressive Sensing Signal Reconstruction , 2008, IEEE Transactions on Information Theory.

[14]  Trac D. Tran,et al.  Hyperspectral Image Classification Using Dictionary-Based Sparse Representation , 2011, IEEE Transactions on Geoscience and Remote Sensing.