Dimension Reduction in Hyperspectral Image Using Single Layer Perceptron Neural Network

Hundreds of continuous bands make up a hyperspectral image. All the bands are not equal important. Some of the bands are significant and others are redundant. Band reduction is a typical step before further processing. Instead of attempting to handle the complete information set without losing crucial data, it is essential to select the most valuable bands. Using traditional band selection techniques, the predetermined number of dimensions are selected from the hyperspectral image. In this article, we propose a novel single-layer neural network and a genetic evolutionary approach to reduce a hyperspectral image’s high dimension. The process involves selecting the two bands with the lowest correlation in each iteration and eliminating two redundant bands. The suggested framework eliminates the unnecessary bands from a hyperspectral image and then chooses the ideal number of the most crucial bands.