Water Body Super-Resolution Mapping Based on Multiple Endmember Spectral Mixture Analysis and Multiscale Spatio-Temporal Dependence

Abstract

Water body mapping is an effective way to monitor dynamic changes in surface water, which is of great significance for water resource management. Super-resolution mapping is a valid method to generate high-resolution dynamic water body maps from low-spatial-resolution images. However, the accuracy of existing super-resolution mapping methods is not high due to the low accuracy of fraction images and the insufficiency of spatial pattern information. To solve this problem, this paper proposes a spectral similarity scale-based multiple-endmember spectral mixture analysis (SSS-based MESMA) and a multiscale spatio-temporal dependence method based on super-resolution mapping (MESMA_MST_SRM) for water bodies. SSS-based MESMA allows different coarse pixels to have different endmember combinations, which can effectively improve the accuracy of spectral unmixing and then improve the accuracy of fraction images. Multiscale spatio-temporal dependence adopts both pixel-based and subpixel-based spatial dependence. In this study, eight different types of water body mappings derived from the Landsat 8 Operational Land Imager (OLI) and Google Earth images were employed to test the performance of the MESMA_MST_SRM method. The results of the eight experiments showed that compared with the other four tested methods, the overall accuracy (OA) value, as well as the overall distribution and detailed information of the water map generated by the MESMA_MST_SRM method, were the best, indicating the great potential and efficiency of the proposed method in water body mapping.

Keywords

• water body mapping
• super-resolution mapping
• data fusion