A Review of Statistical Approaches for Band Reduction Techniques for Hyperspectral Image Classification
Keywords:
Hyperspectral Imaging, Band Selection, Statistical Methods, Feature Selection, Classification, Remote SensingAbstract
Hyperspectral imaging (HSI) captures a wide spectrum of light, divided into countless, closely spaced bands. This is very useful to record more information in a data pixel, enabling precise identification of materials and objects. The vast number of spectral bands in HSI data can introduce challenges like redundant information and higher computational requirements. Numerous methods exist to reduce HSI dimensionality by selecting informative bands. Band selection is a critical step, focusing on identifying meaningful bands and eliminating redundant or irrelevant ones. This study compares major statistical approaches in band selection methods: Information-Theoretic approaches, PCA (Principal Component Analysis), ICA (Independent Component Analysis), and Clustering-Based Method and evaluate the performance and methodology of these approaches in the field of hyperspectral image classification.
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