Automatic Adaptive Signature Generalization in R

Published: 20 December 2017| Version 2 | DOI: 10.17632/s7c3vfr84w.2


The automatic adaptive signature generalization (AASG) algorithm overcomes many of the limitations associated with classification of multitemporal imagery. By locating stable sites between two images and using them to adapt class spectral signatures from a high-quality reference classification to a new image, AASG mitigates the impacts of radiometric and phenological differences between images and ensures that class definitions remain consistent between the two classifications. Here, I provide source code (in the R programming environment), as well as a comprehensive user guide, for the AASG algorithm. See Dannenberg, Hakkenberg and Song (2016) for details of the algorithm. Dannenberg, MP, CR Hakkenberg, and C Song (2016), Consistent classification of Landsat time series with an improved automatic adaptive signature generalization algorithm, Remote Sensing 8(8): 691.



University of North Carolina at Chapel Hill


Natural Sciences, Social Sciences, Geography, Remote Sensing, Land Cover Change, Land Cover Analysis, Landsat Satellite