Multisensor Coupled Spectral Unmixing for Time-Series Analysis

Abstract

We present a new framework, called multisensor coupled spectral unmixing (MuCSUn), that solves unmixing problems involving a set of multisensor time-series spectral images in order to understand dynamic changes of the surface at a subpixel scale. The proposed methodology couples multiple unmixing problems based on regularization on graphs between the time-series data to obtain robust and stable unmixing solutions beyond data modalities due to different sensor characteristics and the effects of nonoptimal atmospheric correction. Atmospheric normalization and cross calibration of spectral response functions are integrated into the framework as a preprocessing step. The proposed methodology is quantitatively validated using a synthetic data set that includes seasonal and trend changes on the surface and the residuals of nonoptimal atmospheric correction. The experiments on the synthetic data set clearly demonstrate the efficacy of MuCSUn and the importance of the preprocessing step. We further apply our methodology to a real time-series data set composed of 11 Hyperion and 22 Landsat-8 images taken over Fukushima, Japan, from 2011 to 2015. The proposed methodology successfully obtains robust and stable unmixing results and clearly visualizes class-specific changes at a subpixel scale in the considered study area.