Dimensionality reduction and hierarchical clustering in framework for hyperspectral image segmentation

Abstract

The hyperspectral data contains hundreds of narrows bands representing the same scene on earth, with each pixel has a continuous reflectance spectrum. The first attempts to analysehyperspectral images were based on techniques that were developed for multispectral images by randomly selecting few spectral channels, usually less than seven. This random selection of bands degrades the performance of segmentation algorithm on hyperspectraldatain terms of accuracies. In this paper, a new framework is designed for the analysis of hyperspectral image by taking the information from all the data channels with dimensionality reduction method using subset selection and hierarchical clustering. A methodology based on subset construction is used for selecting k informative bands from d bands dataset. In this selection, similarity metrics such as Average Pixel Intensity [API], Histogram Similarity [HS], Mutual Information [MI] and Correlation Similarity [CS] are used to create k distinct subsets and from each subset, a single band is selected. The informative bands which are selected are merged into a single image using hierarchical fusion technique. After getting fused image, Hierarchical clustering algorithm is used for segmentation of image. The qualitative and quantitative analysis shows that CS similarity metric in dimensionality reduction algorithm gets high quality segmented image.