Abstract
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.
Highlights
With rich spectral information contained in tens or hundreds of spectral bands, hyperspectral images (HSI) has been successfully applied in a wide range of remote sensing applications such as land cover analysis [1,2,3], military surveillance [4,5], object detection [6], and precision agriculture [7,8,9,10,11], etc
The main contributions of this paper can be highlighted as follows: (1) A superpixel-based sparse representation (SR) model is proposed for effective classification of HSIs with insufficient training samples; (2) By introducing the superpixel into the sparse representation classification (SRC) model, the computational cost has been significantly reduced whilst maintaining the classification accuracy; (3) an online metric learning strategy is applied to exploit the discrimination of spatial and spectral features to further improve the classification accuracy
During the last several years, various approaches have been proposed to improve the performance of HSI classification
Summary
With rich spectral information contained in tens or hundreds of spectral bands, hyperspectral images (HSI) has been successfully applied in a wide range of remote sensing applications such as land cover analysis [1,2,3], military surveillance [4,5], object detection [6], and precision agriculture [7,8,9,10,11], etc. Most of current SRC-based methods [32,33,34,35] utilize adaptive strategies to estimate the sparse coefficients and determine the label of the test pixel by the sum of residuals from all extracted features. To improve the efficiency and maintain the classification accuracy under the circumstance of insufficient training samples, a superpixel-based feature specific sparse representation framework (SPFS-SRC) is proposed in this paper for the classification of HSI. The main contributions of this paper can be highlighted as follows: (1) A superpixel-based sparse representation (SR) model is proposed for effective classification of HSIs with insufficient training samples; (2) By introducing the superpixel into the SRC model, the computational cost has been significantly reduced whilst maintaining the classification accuracy; (3) an online metric learning strategy is applied to exploit the discrimination of spatial and spectral features to further improve the classification accuracy. Further discussion and concluding remarks about our work are given in the Section 4
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