Improving execution time for supervised sparse representation classification of hyperspectral images using the Moore–Penrose pseudoinverse

2019 ◽  
Vol 13 (02) ◽  
pp. 1
Author(s):  
Fernando X. Arias ◽  
Heidy Sierra ◽  
Emmanuel Arzuaga
Keyword(s):  
Sparse Representation ◽  
Execution Time ◽  
Hyperspectral Images ◽  
Penrose Pseudoinverse ◽  
Sparse Representation Classification

scholarly journals Superpixel based Feature Specific Sparse Representation for Spectral-Spatial Classification of Hyperspectral Images

2019 ◽  
Vol 11 (5) ◽  
pp. 536 ◽  
Author(s):  
He Sun ◽  
Jinchang Ren ◽  
Huimin Zhao ◽  
Yijun Yan ◽  
Jaime Zabalza ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Learning Strategy ◽  
Metric Learning ◽  
Hyperspectral Images ◽  
Multiple Features ◽  
Spatial Classification ◽  
Class A ◽  
Sparse Representation Classification ◽  
Shape And Size

To improve the performance of the sparse representation classification (SRC), we propose a superpixel-based feature specific sparse representation framework (SPFS-SRC) for spectral-spatial classification of hyperspectral images (HSI) at superpixel level. First, the HSI is divided into different spatial regions, each region is shape- and size-adapted and considered as a superpixel. For each superpixel, it contains a number of pixels with similar spectral characteristic. Since the utilization of multiple features in HSI classification has been proved to be an effective strategy, we have generated both spatial and spectral features for each superpixel. By assuming that all the pixels in a superpixel belongs to one certain class, a kernel SRC is introduced to the classification of HSI. In the SRC framework, we have employed a metric learning strategy to exploit the commonalities of different features. Experimental results on two popular HSI datasets have demonstrated the efficacy of our proposed methodology.

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