Multi-Modal Remote Sensing Image Matching Method Based on Deep Learning Technology

Abstract

Remote sensing is a scientific technology that uses sensors to detect the reflection, radiation or scattering of electromagnetic wave signals from ground objects in a non-contact and long-distance manner. The images are classified by the extracted image feature information Recognition is a further study of obtaining target feature information, which is of great significance to urban planning, disaster monitoring, and ecological environment evaluation. The image matching framework proposed in this paper matches the depth feature maps, and reversely pushes the geometric deformation between the depth feature maps to between the original reference image and the target image, and eliminates the geometric deformation between the original images. Finally, through feature extraction of the corrected image, the extracted local feature image blocks are input into the trained multi-modal feature matching network to complete the entire matching process. Experiments show that the negative sample set construction strategy that takes into account the sample distance proposed in this experiment can effectively deal with the problem of neighboring point interference in RSI matching, and improve the matching performance of the network model.