A super resolution approach for spectral unmixing of remote sensing images

Abstract

It is well known that coarse spatial resolution is an important factor for the occurrence of mixed pixels in remote sensing images, and conventional approaches for spectral unmixing adopt various techniques on spectral dimension only in a fixed spatial resolution. In this article, a super resolution (SR) approach for spectral unmixing is proposed, based on the assumption that increasing the spatial resolution helps to retrieve the composition of a pixel. Firstly, a remote sensing image is downscaled into an SR image using example-based kernel ridge regression (EBKRR). Secondly, the SR image is classified using supervised hard classification, and then the class map is decomposed into thematic class layers. Thirdly, the thematic class layers are upscaled into the original spatial resolution with an averaging operation, and the abundance maps are finally derived. In two simulated data-based experiments and one ground data-based experiment, this approach was compared with linear spectral mixture analysis (LSMA) and artificial neural network (ANN)-based spectral unmixing methods. The accuracy assessment indicated that the SR approach outperformed LSMA and ANN under measurements of mean absolute error and absolute bias in the three experiments.

Acknowledgements

This work was funded by the 973 Programme (grant no. 2006CB701300), the NNSFC (grant no. 40721001) and the S&T Programme of Wuhan (200970634267). The author would like to thank Dr. Kwang In Kim, from the Max Plank Institute for Biological Cybernetics, Germany for his sharing of the SR programme and his communication with the author. The author would also like to thank the two anonymous referees who have given very helpful comments.