SPATIAL INTERPOLATION AS A TOOL FOR SPECTRAL UNMIXING OF REMOTELY SENSED IMAGES

Abstract

Super resolution-based spectral unmixing (SRSU) is a recently developed method for spectral unmixing of remotely sensed imagery, but it is too complex to implement for common users who are interested in land cover mapping. This study makes use of spatial interpolation as an alternative approach to achieve super resolution reconstruction in SRSU. An ASTER image with three spectral bands was used as the test data. The algorithm is evaluated using root mean square error (RMSE) compared with linear spectral unmixing and hard classification. The result shows that the proposed algorithm has higher unmixing accuracy than those of the other comparative algorithms, and it is proved as an efficient and convenient spectral unmixing tool of remotely sensed imagery.