Enhanced Subpixel Mapping with Spatial Distribution Patterns of Geographical Objects

This paper proposes spatial distribution pattern-based subpixel mapping (SPM_S) as a novel subpixel mapping (SPM) strategy. It separately considers spatial distribution patterns of different types of geographical objects. Initially, it classifies geographical objects into areal, linear, and point patterns according to their spatially geometric characteristics. For the different patterns, SPM_S uses the vectorial boundary-based SPM algorithm with the spatial dependence assumption to deal with areal objects, the linear template matching-based SPM algorithm for linear objects, and the spatial pattern consistency matching-based SPM algorithm for point objects. The three patterns are integrated to generate a subpixel map. An artificially created image and two remotely sensed images were used to evaluate the performance of SPM_S. The results were compared with a traditional hard classifier and seven existing SPM methods. The experimental results demonstrated that SPM_S performed better than the hard classification and traditional SPM methods, particularly when dealing with linear and point objects.