{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["23(2)"],"submitter":["Islam MR"],"pubmed_abstract":["A hyperspectral image (HSI), which contains a number of contiguous and narrow spectral wavelength bands, is a valuable source of data for ground cover examinations. Classification using the entire original HSI suffers from the \"curse of dimensionality\" problem because (i) the image bands are highly correlated both spectrally and spatially, (ii) not every band can carry equal information, (iii) there is a lack of enough training samples for some classes, and (iv) the overall computational cost is high. Therefore, effective feature (band) reduction is necessary through feature extraction (FE) and/or feature selection (FS) for improving the classification in a cost-effective manner. Principal component analysis (PCA) is a frequently adopted unsupervised FE method in HSI classification. Nevert"],"journal":["Sensors (Basel, Switzerland)"],"pagination":["657"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9864404"],"repository":["biostudies-literature"],"pubmed_title":["Mutual Information-Driven Feature Reduction for Hyperspectral Image Classification."],"pmcid":["PMC9864404"],"pubmed_authors":["Islam MR","Hossain MA","Uddin MP","Ahmed B"],"additional_accession":[]},"is_claimable":false,"name":"Mutual Information-Driven Feature Reduction for Hyperspectral Image Classification.","description":"A hyperspectral image (HSI), which contains a number of contiguous and narrow spectral wavelength bands, is a valuable source of data for ground cover examinations. Classification using the entire original HSI suffers from the \"curse of dimensionality\" problem because (i) the image bands are highly correlated both spectrally and spatially, (ii) not every band can carry equal information, (iii) there is a lack of enough training samples for some classes, and (iv) the overall computational cost is high. Therefore, effective feature (band) reduction is necessary through feature extraction (FE) and/or feature selection (FS) for improving the classification in a cost-effective manner. Principal component analysis (PCA) is a frequently adopted unsupervised FE method in HSI classification. Nevert","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2025-04-04T14:27:02.486Z","creation":"2025-04-04T14:27:02.486Z"},"accession":"S-EPMC9864404","cross_references":{"pubmed":["36679453"],"doi":["10.3390/s23020657"]}}