Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 13, Issue -, Pages 4550-4563Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2020.3014492
Keywords
Kernel; Feature extraction; Support vector machines; Hyperspectral imaging; Image segmentation; Hyperspectral image (HSI); local binary mode (LBP); multiple kernels (MK); superpixel; support vector machine (SVM)
Categories
Funding
- National Natural Science Foundation of China [61602423, 61605175]
- Henan Province Science and Technology Breakthrough Project [182102210611]
- Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Land and Resources
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The superpixel-based multiple kernels model uses the average value of all pixels within superpixel as the spatial feature, which results in inaccurate extraction of edge pixels. To solve this problem, a local binary patterns and superpixel-based multiple kernels method is proposed for hyperspectral image (HSI) classification. First, the original HSI is segmented into multiple superpixels by using the entropy rate superpixel segmentation algorithm. On the HSI with superpixel index, the spectral kernel is second obtained by combining the spectral feature map with the radial basis kernel (RBF). By introducing local binary pattern (LBP) and weighted average filtering into RBF, the spatial kernels are obtained within and among superpixels. Finally, the combined kernel containing the abovementioned three kernels is inputted into the support vector machine classifier to generate a classification map. The experimental procedure in this article uses LBP to extract the information in superpixels, which effectively prevents the loss of edge features in superpixels. The experimental results show that the proposed method is superior to the state-of-the-art classifiers for HSI classification.
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