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| Classification of Hyperspectral Remote Sensing Image Based on Sparse Representation and Spectral Information |
| Song Xiang-fa①② Jiao Li-cheng① |
①(Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an 710071, China)
②(School of Computer and Information Engineering, Henan University, Kaifeng 475004, China) |
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Abstract This paper presents a novel classification algorithm of hyperspectral remote sensing image based on sparse representation and spectral information. First, a learning dictionary is obtained based on hyperspectral remote sensing image data set, and then the sparse coefficient of each pixel is calculated according to the learning dictionary. As a result, sparse representation feature is obtained. Finally, random forests are respectively constructed based on sparse representation feature and spectral information, and the classification result is decided by voting strategy. Experiments on AVIRIS hyperspectral remote sensing image justify the effectiveness of the algorithm. The experimental results indicate that the proposed method has better performance than methods based on spectral and sparse representation respectively, and has a higher overall accuracy and Kappa coefficient.
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Received: 02 June 2011
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Corresponding Authors:
Song Xiang-fa
E-mail: xiangfasong@163.com
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2012, Vol. 34 
