Band selection for remotely sensed image data is an effective means to mitigate the curse of dimensionality. Many criteria have been suggested in the past for optimal band selection. In this paper, a joint band-prioritization and band-decorrelation approach to band selection is considered for hyperspectral image classification. The proposed band prioritization is a method based on the eigen (sp...

common spatial pattern (csp) is a method commonly used to enhance the effects of event‑related desynchronization and event‑related synchronization present in multichannel electroencephalogram‑based brain‑computer interface (bci) systems. in the present study, a novel csp sub‑band feature selection has been proposed based on the discriminative information of the features. besides, a distinction ...

While hyperspectral data are very rich in information, processing the hyperspectral data poses several challenges regarding computational requirements, information redundancy removal, relevant information identification, and modeling accuracy. In this paper we present a new methodology for combining unsupervised and supervised methods under classification accuracy and computational requirement ...

Biometrics based on palmprint has been developing fast in the past ten years. The newly proposed hyperspectral imaging can provide high accuracy and abundant information about the palms and the tissues, veins underneath. However, due to the limitations of computation speed and storage, we have to select the most representative bands for palmprint recognition. This paper proposes a band selectio...

Hyperspectral band image selection is a fundamental problem for hyperspectral remote sensing data processing. Accepting its importance, several information-based band selection methods have been proposed, which apply Shannon entropy to measure image information. However, the Shannon entropy is not accurate in measuring image information since it neglects the spatial distribution of pixels and i...