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- Title
Combined Spatial-Spectral Hyperspectral Image Classification Based on Adaptive Guided Filtering.
- Authors
Liang Huang; Shenkai Nong; Xiaofeng Wang; Xiaohang Zhao; Chaoran Wen; Ting Nie
- Abstract
Hyperspectral image classification has a low accuracy in the face of a small training set. To solve the problem, this paper proposes a combined spatial-spectral hyperspectral image classification approach based on adaptive guided filtering. From coarse to fine classification, the local binary pattern (LBP) histogram features were improved, the spatial contrast description was enhanced, and enhanced spatial-spectral features were prepared through Gabor transform of different scales and directions, combined with super pixel blocks. Then, the pre-classification was completed by the support vector machine (SVM) classifier. To reduce noise interference, the pre-classification results were filtered again by a guided filter based on the adaptive regularization factor. To verify its effectiveness, the proposed approach was compared with the state-of-the-arts approaches through repeated experiments. The comparison shows that our approach achieved a high classification accuracy, while suppressing noise interference. This research provides a new tool for hyperspectral image classification with a small training set.
- Subjects
ADAPTIVE filters; SPECTRAL imaging; FILTERS &; filtration; GABOR transforms; MULTISPECTRAL imaging; SUPPORT vector machines
- Publication
Traitement du Signal, 2022, Vol 39, Issue 2, p745
- ISSN
0765-0019
- Publication type
Article
- DOI
10.18280/ts.390240