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Hyperspectral image classification via recursive filtering and KNN |
| Bing TU1,2,3, Xiaofei ZHANG1,3, Guoyun ZHANG1,2,3, Jinping WANG1,3, Yao ZHOU1,3 |
1.School of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang 414006,China
2.Key Laboratory of Optimization and Control for Complex Systems of Hunan Province, Hunan Institute of Science and Technology, Yueyang 414006, China
3.Laboratory of Intelligent-Image Information Processing, Hunan Institute of Science and Technology, Yueyang 414006, China |
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Abstract In order to remove the noise in the hyperspectral image effectively, strengthen the spatial structure, make full use of the spatial context information of the object, and improve the classification accuracy of hyperspectral image, the authors put forward recursive filtering and k-nearest neighbor (KNN) method for hyperspectral image classification. The main steps are as follows: Firstly, the principal component analysis (PCA) is used to perform feature dimension reduction of hyperspectral images. Next, the recursive filtering is used to filter the principal component image. Then, the Euclidean distance between the test sample and the different training samples is calculated by the KNN algorithm. Finally, according to the comparison of average values of k minimum Euclidean distances, the classification of test samples is achieved. Experimental results are based on several real-world hyperspectral data sets, and the influence of different parameters on the classification accuracy is analyzed. Experimental results show that, with recursive filtering, the noise can be effectively removed, and the image outline can be strengthened. Compared with other hyperspectral image classification methods, the proposed method is outstanding in classification accuracy.
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| Keywords
hyperspectral images
recursive filtering
k-nearest neighbor
principal component analysis
Euclidean distance
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Issue Date: 15 March 2019
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2019,
Vol. 31
