Subpixel Mapping for Remote Sensing Imagery Based on Spatial Adaptive Attraction Model and Conditional Random Fields

Abstract

For subpixel mapping (SPM), ensuring the operational efficiency of the algorithm and mitigating the effect of abundance errors often cannot be achieved simultaneously. To solve the problem, we propose a new SPM method based on the spatial adaptive attraction model (SAAM) and conditional random fields (CRFs). First, the proposed SAAM obtains the spatial adaptive attraction value by adaptively adjusting the spatial attraction value obtained using the traditional spatial attraction model, thereby turning the display form of the abundance constraints in the SPM into an implicit form for expression, to perform the physical significance of the abundance constraints with the relative size of the attraction value of each subpixel. Second, the spatial adaptive attraction value of the implicitly represented abundance constraints and the local spatial smoothing prior are modeled in the CRFs, and the model makes full use of the spatial information in the label field while considering the abundance constraint. Third, Graph-cut is used to optimize the model, the proposed SPM can not only guarantee the operational efficiency, but also extinguish the influence of abundance error and decrease the noise artifact on the results of SPM. Experiments on three remote sensing images show that the proposed SPM accuracy is considerably better than the previously available SPM methods and is the least time-consuming. This study provides a new solution for the SPM of remote-sensing images.

Keywords

• Conditional Random Fields (CRFs)
• graph-cut
• remote sensing imagery
• spatial adaptive attraction model (SAAM)
• subpixel mapping (SPM)