How Can Geostatistics Help Us Understand Deep Learning?

Convolutional neural networks have shown excellent performance in image processing. However, due to the black-box property of deep networks in applications, people still doubt its credibility, which has led to the birth of many interpretation methods for computer vision networks, but their interpretation effects can only be subjectively assessed by human vision on the heatmaps generated by these interpretation methods. In this paper, we propose a novel Geospatial Analysis for eXplainable Artificial Intelligence (GeoSeXAI) method, which for the first time introduces the spatial autocorrelation analysis method in geostatistics combined with the XAI algorithm and the SAR images to provide an accurate and objective quantitative assessment of the spatial distribution of the XAI attribution, as well as a global and objective assessment of the XAI attribution. global objective quantitative assessment, and provides a local qualitative explanation that is easier for human visual understanding.