Adaptive Linear Spectral Mixture Analysis

作者：Chang, Chein-I^*

来源：IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(3): 1240-1253.

DOI：10.1109/TGRS.2016.2620494

• Adaptive linear mixing model (ALMM)
• adaptive linear spectral mixture analysis (ALSMA)
• fully constrained least squares (FCLS)
• linear mixing model (LMM)
• linear spectral unmixing (LSU)
• recursive linear spectral mixture analysis (RLSMA)
• virtual dimensionality (VD)

This paper presents a theory of adaptive linear spectral mixture analysis (ALSMA), which can implement LSMA using an adaptive linear mixing model (ALMM) that adjusts and varies with spectral signatures adaptively. In doing so, a recursive LSMA (RLSMA) is developed for ALSMA to allow LSMA to update spectral signature by spectral signature without reprocessing LSMA and also to fuse LSMA results obtained by ALMM using different sets of spectral signatures. To form ALMM, the concept of RLSMA-specified virtual dimensionality is further proposed for ALSMA, which not only can find spectral signatures recursively by RLSMA to adjust ALMM but also can automatically determine the number of spectral signatures via Neyman-Pearson detection theory.

• 出版日期2017-3
• 单位西安电子科技大学; 大连海事大学