Feature extraction of hyperspectral images with a matting model

ABSTRACT

Owing to the limitations of the imaging sensor and theoretical aspect, hyperspectral images (HSIs) are contaminated with some unwanted components such as noise and a lack of spatial information. This article proposes a spatial–spectral feature enhancement model to eliminate interference, modify spectral distortion, and increase the useful features. The framework firstly proposes an effective spatial feature-based strategy for selecting a band with the most edge information to serve as alpha channel. Given the alpha channel, the continuous foreground and background are estimated by the closed form solution. Finally, feature-enhanced HSI is obtained by linearly combining the selected band, hyper foreground and background. Experimental results of the ground-based data and remotely sensed data indicate that the proposed feature enhancement algorithm provides effective performance in enhancing spatial–spectral features and reducing noise. Especially, the feature-enhanced data have positive influence on both unmixing and classification.

Acknowledgements

The authors would like to thank the handling editors and the reviewers for providing valuable comments. The authors also would like to thank Yi Chen for providing the software of SOMP method.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. https://engineering.purdue.edu/biehl/MultiSpec/hyperspectral.html/ .

2. http://www.ehu.es/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.

Additional information

Funding

This work was partially supported by the National Natural Science Foundation of China (nos. 61222101, 61272120, 61301287, 61301291 and 61350110239) and the 111 project (B08038).