Journal of Geo-information Science ›› 2016, Vol. 18 ›› Issue (2): 263-271.doi: 10.3724/SP.J.1047.2016.00263

• Orginal Article • Previous Articles     Next Articles

Classification of Hyperspectral Images with Spectral-Spatial Sparse Representation

ZHU Yong(), WU Bo*()   

  1. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350002, China
  • Received:2015-05-11 Revised:2015-10-27 Online:2016-02-10 Published:2016-02-04
  • Contact: WU Bo E-mail:zhuyongfz@126.com;wavelet778@sohu.com

Abstract:

A novel sparse representation classification model with spectral-spatial sparsity properties is presented to improve the classification accuracy of hyperspectral images. Firstly, this method uses the wavelet dictionary as the core dictionary to extract spectral domain sparse information, and then the spectral dimension sparse representation classification is transformed into the wavelet domain (WSRC) by inverse wavelet transformation. After that, we actually extract the sparse spectral features of the hyperspectral images and increase the recognition of the original dictionary. Secondly, considering the unity and diversity of the spatial adjacent object, we realize the sparse coding of the neighborhood pixels, and then accumulate the sparse codes. At the same time, we classify the hyperspectral images using a linear classifier that is based on the accumulated sparse codes. This method ensures that we extract the main sparse signal of the neighborhood pixels on the basis of the personality features of sparse encoding, and it performs better than the joint sparse representation model (JSRC) which is directly based on the neighborhood pixels. Finally, two commonly used hyperspectral images are utilized to validate the proposed model. The experimental results demonstrate that the proposed algorithm outperforms other models in terms of overall accuracy and kappa coefficient measurements.

Key words: wavelet transform, double sparse representation, sparse code, hyperspectral image