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Framework for Hyperspectral Image Segmentation using Unsupervised Algorithm
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. 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 hierarchical fusion technique. The main goal of image fusion is to combine all the information from the selected image bands to form a single image. This single image is segmented using Fuzzy C-means (FCM) algorithm. The qualitative and quantitative analysis shows that this framework will segment the data set more accurately by combining all the features in the image bands.
Dimensionality Reduction, Empirical Mode Decomposition, FCM, Hyperspectral Imaging.
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