Optimized Hyperspectral Image Classification Using Tabu Search for Band Selection and Hyper3DNet Lite Classifier.

Hyperspectral imaging (HSI) is essential for capturing images across various spectral bands and providing indepth spectral information. However, the high dimensionality of HSI data presents challenges, such as increased computational complexity and the curse of dimensionality. Band selection is a critical pre-processing step that addresses these challenges by identifying the most informative bands. This paper introduces a novel band selection method utilizing the Tabu Search algorithm (TSA), aimed at optimizing the selection of spectral bands to enhance classification performance. The proposed method assesses bands using a fitness function that maximizes variance and minimizes correlation among the chosen bands. Experimental results on Indian pine dataset and Pavia University Scenes, along with the K-Nearest Neighbor and Support Vector machine improves the classification accuracy while reducing computational demands. [ABSTRACT FROM AUTHOR]