A New Methodology of Hierarchical Image Fusion in Framework for Hyperspectral Image Segmentation

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 a framework for hyperspectral image segmentation using a clustering algorithm. 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. In the third stage, the informative bands which are selected in the second stage are merged into a single image using hierarchical fusion technique. In the hierarchical image fusion, the images are grouped such that each group has equal number of images. This methodology leads to group of images having much varied information, thus decreasing the quality of fused image. This paper presents a new methodology of hierarchical image fusion in which similarity metrics are used to create image groups for merging the selected image bands. This single image is segmented using Fuzzy c-means 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.