Defect Detection in Remote Sensing Satellite Images: A New Dataset and Algorithm

Abstract

Satellite observation is an important way to understand the earth. However, due to problems such as satellite aging, cloud obstruction, and other interferences during the imaging and transmission process, remote sensing images inevitably produce various defects. Hence, it is necessary to quickly detect defects to calibrate the imaging system and avoid the waste of satellite resources. Current research on defect detection in remote sensing images is not comprehensive and only focuses on partial defect categories, such as cloud and stripe. To this end, we construct the first large-scale high-resolution remote sensing image defect detection dataset (HRSD). The proposed dataset contains more than 1.2 million manually annotated patches from eight different satellites, covering various common defect categories and including multiple image modalities (i.e., panchromatic and multispectral). The dataset also has rich diversity which covers different landforms in multiple regions. Furthermore, to realize the detection of multiple defect categories simultaneously, we design a feature aggregation graph network (FAGN) based on the position correlation and semantic similarity among image patches, which fully utilizes the distribution characteristics of defects to achieve accurate defect detection. Extensive experiments on the HRSD dataset demonstrated the effectiveness of FAGN. We will release the HRSD dataset and FAGN model later.