Iterative Spectral Distancing: A Novel Approach for Extracting Endmembers in Complex Urban Image Scenes

Optical remote sensing images of cities exhibit strong spectral variability, and characterising it remains a key challenge. Imaging spectroscopy is useful for this purpose, yet traditional endmember extraction algorithms are poorly suited for this type of imagery. Important issues include the non-spatiality, randomness and poor computational efficiency of existing methods, as well as the need for predefining the number of endmembers. We propose a novel algorithm, called Iterative Spectral Distancing addressing each of these issues. We show that ISD outperforms three established methods on a synthetic image, indicating its potential.

ISBN:9781665403696

Publication year:2021

Accessibility:Closed