Automatic Adaptive Signature Generalization in R

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.

自动自适应签名泛化（AASG）算法克服了多时相影像分类所存在的诸多局限。该算法通过定位两幅影像间的稳定地物，将高质量参考分类结果中的类别光谱特征适配至新影像中，可有效缓解影像间辐射度与物候差异带来的影响，并保障两次分类的类别定义保持一致。本文提供了AASG算法的源代码（基于R编程语言环境）以及完整的用户指南。有关该算法的详细技术细节，请参阅Dannenberg、Hakkenberg与Song（2016）的相关研究。

Dannenberg, MP、CR Hakkenberg 及 C Song（2016），《基于改进自动自适应签名泛化算法的Landsat时间序列一致性分类》，遥感（Remote Sensing），8卷8期：691。