Improving Geological Remote Sensing Interpretation via Optimal Transport-Based Point–Surface Data Fusion

Abstract

High-quality geological remote sensing interpretation (GRSI) products play a vital role in a wide range of fields, including the military, meteorology, agriculture, the environment, mapping, etc. Due to the importance of GRSI products, this research aimed to improve their accuracy. Although deep-learning (DL)-based GRSI has reduced dependence on manual interpretation, the limited accuracy of multiple geological element interpretation still poses a challenge. This issue can be attributed to small inter-class differences, the uneven distribution of geological elements, sensor limitations, and the complexity of the environment. Therefore, this paper proposes a point–surface data optimal fusion method (PSDOF) to improve the accuracy of GRSI products based on optimal transport (OT) theory. PSDOF combines geological survey data (which has spatial location and geological element information called point data) with a geological remote sensing DL interpretation product (which has limited accuracy and is called surface data) to improve the quality of the resulting output. The method performs several steps to enhance accuracy. First, it calculates the gray-scale correlation feature information for the pixels adjacent to the geological survey points. Next, it determines the distribution of the feature information for geological elements in the vicinity of the point data. Finally, it incorporates complementary information from the survey points into the geological elements’ interpretation boundary, as well as calculates the optimal energy loss for point–surface fusion, thus resulting in an optimal boundary. The experiments conducted in this study demonstrated the superiority of the proposed model in addressing the problem of the limited accuracy of GRSI products.