A Novel Sub-Pixel Mapping Model Based on Pixel Aggregation Degree for Small-Sized Land-Cover

Abstract

To further improve the accuracy of remote sensing classification and land-cover recognition at sub-pixel level, a novel sub-pixel mapping (SPM) model was first proposed by introducing the concept of pixel aggregation degree (PAD) which could simulate the spatial distribution of small-sized land-cover. In the proposed novel SPM model, based on the distribution of sub-pixel random initialization, PAD algorithm was optimized sub-pixel distribution to obtain final SPM results. Using a Sentinel-2 remote sensing data, related SPM experiments were performed to verify both accuracy and effect of PAD SPM model. The experimental results indicated that the SPM accuracy based on PAD model were superior to the classification results of the K-mean and the SPM results of traditional spatial attraction model. It was shown that the PAD model had certain feasibility and applicability which provided a new idea to better break the limitations of remote sensing image spatial resolution, and was beneficial to the subsequent research and application of remote sensing image.