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A Method for Edge Detection in Hyperspectral Images Based on Gradient Clustering

Edge detection in hyperspectral images is an intrinsic difficult problem as the gray value intensity images related to single spectral bands may show different edges. The few existing approaches are either based on a straight forward combining of these individual edge images, or on finding the outliers in a region segmentation. We propose as an alternative a clustering of all image pixels in a feature space constructed by the spatial gradients in the spectral bands. An initial comparative study of various hyperspectral datasets shows the differences and properties of these approaches and makes clear that the proposal has interesting properties that may be studied further.