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ABSTRACT
In recent years, methods of spatial-spectral feature extraction have been widely used in hyperspectral image (HSI) classification and have achieved good performance. However, the ways are often unable to distinguish effectively the boundary of the adjacent ground objects. Multi-scale feature fusion alleviates the problems to a certain extent, but in the case of limited samples, the previous methods often fail to achieve the desired effect. In this paper, we propose a multi-scale hyperspectral image classification method based on weakened Laplacian pyramid and guided filtering to nicely distinguish the boundary of the different ground objects. This structure first obtains multi-scale information by constructing a weakened Laplacian pyramid, and weakens the magnification of Gaussian blur in the upsampling process. While obtaining multi-scale images, it can not only retain enough image information to expand feature samples but also emphasize edge information in the edge extraction of the Laplacian pyramid. Then, the weakened Laplacian pyramid feature map is used as the guiding image of the guiding filter, which can better preserve the edge features and further improve the classification performance of HSI. Extensive experiments are carried out on four HSI data sets. The results show that our method is superior in classification accuracy and computational cost.
Acknowledgements
This work was supported in part by the Funds of the National Natural Science Foundation of China under Grant 42076189 and 52075057, in part by the Fundamental Research Funds for the Central Universities under Grant 22CX01004A-8, and in part by China High-Resolution Earth Observation System Program under Grant 41-Y30F07-9001-20/22.
Disclosure statement
No potential conflict of interest was reported by the author(s).