Sub-Pixel Mapping Based on MAP Model and Spatial Attraction Theory for Remotely Sensed Image

Abstract

There are a lot of mixed pixels in the remotely sensedimagery, which can seriously limit the utility of classification. Sub-pixel mapping (SPM) is a promising techniqueto solve this problem. It can generate a fine resolution land covermap from coarse resolution fractional images by predicting the spatiallocations of different land cover classes at sub-pixel scale.However, the accuracy and detail are always limited. Especiallywhen the scale factor is large among sub-pixels per pixel, thedata volumes are amplified and the sub-pixel distribution becomescomplex. The traditional methods are carried out only by the fractionsof land cover and the spatial dependence theory, which cannot satisfythe requirement of the SPM. For avoiding the above flaw, a newSPM method based on maximum a posteriori (MAP) model withsub-pixel/pixel spatial attraction theory aimed at the largescale factor is proposed. First, MAP is proposed to improve theresolution of the fractional images and obtain the initial sub-pixellocations; after that, the pixel swapping algorithm is used tooptimize and produce the final SPM result. In this paper, theproposed model is tested by a simple simulated font image and real remotelysensed imagery, which can both demonstrate that it can outperformtraditional algorithm with a more accurate sub-pixel scale land covermap.