2026-05-19T22:55:33Z https://ipsj.ixsq.nii.ac.jp/oai

oai:ipsj.ixsq.nii.ac.jp:00218592 2025-01-19T15:06:58Z 1164:2735:10865:10962

Joint-Conditional Mutual Information based Feature Subset Selection for Remotely Sensed Hyperspectral Image Classification Joint-Conditional Mutual Information based Feature Subset Selection for Remotely Sensed Hyperspectral Image Classification U., A. Md Ehsan Ali Keisuke, Kameyama U., A. Md Ehsan Ali Keisuke, Kameyama Hundreds of contiguous bands of remotely sensed hyperspectral image (HSI) capture the spectral signatures of observed objects or materials on the earth’s surface. Although the HSI data is able to provide huge information with great details, it poses challenges to image analysis because of the high computational cost due to the large dimensionality of the feature space, redundancy in information, and curse of dimensionality. To overcome these difficulties, feature reduction techniques are used to extract informative features from hyperspectral images. This paper proposes an information-theoretic feature selection approach for selecting an informative feature subset considering the maximum of the minimum approach. The minimum of conditional mutual information based estimation is used to select a feature among the selected feature subset. This selected feature with the corresponding candidate feature is then exploited using the mutual information and joint mutual information based valuation to find the maximum relevance of the candidate features with the target classes. The effectiveness of the proposed approach, called joint-conditional mutual information for selecting informative feature (JCIF), is assessed by implementing it in some synthetic data and two remotely sensed HSI data. Several known feature selection algorithms are also used for comparison purposes. The results of the experiments with K-Nearest Neighbors and Support Vector Machine classifiers reveal that JCIF performs better in selecting informative features. Hundreds of contiguous bands of remotely sensed hyperspectral image (HSI) capture the spectral signatures of observed objects or materials on the earth’s surface. Although the HSI data is able to provide huge information with great details, it poses challenges to image analysis because of the high computational cost due to the large dimensionality of the feature space, redundancy in information, and curse of dimensionality. To overcome these difficulties, feature reduction techniques are used to extract informative features from hyperspectral images. This paper proposes an information-theoretic feature selection approach for selecting an informative feature subset considering the maximum of the minimum approach. The minimum of conditional mutual information based estimation is used to select a feature among the selected feature subset. This selected feature with the corresponding candidate feature is then exploited using the mutual information and joint mutual information based valuation to find the maximum relevance of the candidate features with the target classes. The effectiveness of the proposed approach, called joint-conditional mutual information for selecting informative feature (JCIF), is assessed by implementing it in some synthetic data and two remotely sensed HSI data. Several known feature selection algorithms are also used for comparison purposes. The results of the experiments with K-Nearest Neighbors and Support Vector Machine classifiers reveal that JCIF performs better in selecting informative features 情報処理学会 2022-06-20 eng technical report https://ipsj.ixsq.nii.ac.jp/records/218592 2188-8833 AN10505667 研究報告数理モデル化と問題解決（MPS） 2022-MPS-138 22 1 8 https://ipsj.ixsq.nii.ac.jp/record/218592/files/IPSJ-MPS22138022.pdf application/pdf 1.3 MB