A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification

By Bor-Chen Kuo, Hsin-Hua Ho, Cheng-Hsaun Li, Chih-Cheng Hung, Jin-Shiuh Taur. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014

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Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review (2020, positive): “Feature selection for complex HSI and MSI data, with a huge amount of attributes, is two-fold; With classical machine learning classifiers, such as SVM, the feature selection is crucial as defining the hyper-parameters for massive input types is too complex and impractical [71,72].”
Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review (2020, methodology): “approaches, including automatic kernel selection [83], [87], [89], and multiple kernel learning [90], were proposed to address this problem.”
Hyperspectral Band Selection: A Review (2019, methodology): “In [125], the RFE-SVM was modified into the kernel-based feature-selection algorithm, which used the magnitude of the SVM coefficients as a ranking criterion to select the important bands and better train the SVM classifier.”
CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification (2020, methodology): “To alleviate this issue, kernel tricks, such as the kernel support vector machine (KSVM) [7] and multiple kernel learning [8], were exploited to improve the linear separability.”