Residual information to estimate uncertainty and improve the spectral linear mixing model solution

• Published in: 2012 IEEE International Geoscience and Remote Sensing Symposium pp. 3471 - 3473
• Main Authors: Zanotta, D. C., Haertel, V., Shimabukuro, Y. E., Renno, C. D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-07-2012
• Subjects: endmember extraction; Estimation; Image segmentation; Indexes; Remote sensing; residual term; Soil; Spectral mixture analysis; Uncertainty; Vegetation mapping
• DOI: 10.1109/IGARSS.2012.6350673
• EISSN: 2153-7003
• ISBN: 9781467311601; 146731160X
• ISSN: 2153-6996

This paper proposes an analysis on the residual term resulting from the Linear Spectral Mixing Model (SLMM) solution in order to access model uncertainty. The framework employed here is based on analysis of data produced initially by unmixing of vegetation, bare soil and shade/water, whose are commonly used as standard endmembers. We suggest procedures to identify missing components in the mixture problem and automatically compute the spectral endmember values for these components directly from image data and residual information. The techniques proposed have been tested on real TM-Landsat. The results obtained promises and confirm the validity of the proposed approach.