GA BASED DIMENSIONALITY REDUCTION IN HYPERSPECTRAL IMAGE SEGMENTATION FRAMEWORK

Hyperspectral imaging system contains stack of images collected from the sensor with different wavelengths representing the same scene on the earth. This paper presents GA based dimensionality reduction method in framework for hyperspectral image segmentation. The framework consists of four stages in segmenting a hyperspectral data set. In the first stage, filtering is done to remove noise in image bands. Second stage consists of dimensionality reduction algorithms, in which the bands that convey less information or redundant data will be removed. This deletion will decrease the storage requirement, computational load etc in processing the hyperspectral data. In the third stage, the informative bands which are selected in the second stage are merged into a single image using averaging method of fusion technique. The main goal of image fusion is to merge all the features from the selected image bands to form a single image. This single image is segmented using Fuzzy cmeans clustering algorithm. The experimental results show that this framework will segment the data set more accurately by combining all the features in the image bands after dimensionality reduction using proposed technique.