Abstract :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