Semantic Information-Aided Geometric Correction of High Resolution Satellite Images

Haoyu Guo, Yi Wan, Yongjun Zhang

Abstract: With the rapid development of deep learning technology, the automatic classification and semantic segmentation of remote sensing imagery become more and more accurate. Meanwhile, with the improvement of the generalization, this technology is more and more widely used in the industry [1] . This paper proposes a new framework of sematic-information-aided geometric correction of high-resolution satellite images (HRSIs) to achieve higher accuracy and automation.