A novel method for feature channel selection of the hyperspectral images obtained in remote-sensing, was presented in IEEE Symposium Series on Computational Intelligence, held at Singapore Management University during Dec. 4-7, 2022.
U. A. Md. Ehsan Ali and Keisuke Kameyama, “Informative Band Subset Selection for Hyperspectral Image Classification using Joint and Conditional Mutual Information,” IEEE Symposium on Computational Intelligence in Remote Sensing (IEEE SSCI 2022), (Singapore), pp. 573-580, Dec. 2022.
The paper proposes a novel feature selection method (JCIF) based on joint-conditional mutual information between the hyperspectral channels.The method was applied to toy pattern recognition problems and the segmentation of real hyperspectral remote sensing data, and it was shown to select the feature channels which enable a superior classification ratio in comparison with the other known feature selection methods.