Feature extraction method based on spectral dimensional edge preservation filtering for hyperspectral image classification

ABSTRACT

A large amount of spectral and spatial information contained in hyperspectral imagery has provided a great opportunity to effectively characterize and identify the surface materials of interest. Feature extraction plays a very important role for hyperspectral data classification, which can reduce noise from the original data and improve the separability of land classes. A novel feature extraction technique based on spectral dimensional edge preserving filter is proposed in this paper. A series of Gaussian filters are applied in the spatial domain of the hyperspectral image to produce the guidance image, then, the edge preserving filter which is guided by the guidance image is adopted and applied in the spectral domain of the hyperspectral data to get the feature. For the feature is produced by filtering in the spectral domain, the spectral curves of the feature are more continues, which avoids the spectral discontinuity problems result from the traditional two-dimensional spatial filter. The guidance image is obtained by filtering the original image in the spatial domain, so, the spatial and the spectral information are integrated together in the following spectral edge preserving filtering process. We carefully adjusted the parameters of the filter and applied it to different real hyperspectral remote sensing images, with the support vector machine, multinomial logistic regression, and random forest serving as the classifier, by comparing with other feature extraction methods presented in recent literature, the results indicate that the proposed methodology always has a great performance in different kinds of cases.

Acknowledgements

The authors would like to acknowledge the editors and the anonymous reviewers for their detailed and highly constructive comments, which greatly helped us to improve the technical quality and presentation of our paper.

Contributors

Zhijian Li: write and test the software, author initial text of the manuscript.

Qing Zhu: make substantial contributions to the development of ideas, analysis and interpretation the experiment.

Yaonan Wang: participate in critically revisiting the manuscript.

Zhenjun Zhang: help to get some important data.

Xianen Zhou, Anping Lin and Jingmin Fan: help to take some experiments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [11304094, 61433016, 61501181, 61573134, 61733004]; National Key Technology Research and Development Program of the Ministry of Science and Technology of China [2015BAF13B00]; Hunan Provincial Natural Science Foundation of China [12JJB001,2017JJ2097];