The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 609–615, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-609-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 609–615, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-609-2022
 
30 May 2022
30 May 2022

A SELF-SUPERVISED KEYPOINT DETECTION NETWORK FOR MULTIMODAL REMOTE SENSING IMAGES

L. Li1, L. Han2, H. Cao1, and M. Liu2 L. Li et al.
  • 1College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710064, China
  • 2School of Land Engineering, Chang’an University, Xi’an 710064, China

Keywords: Keypoint detection, Self-supervision, Deep learning, SAR and optical image, DCN

Abstract. Currently, multimodal remote sensing images have complex geometric and radiometric distortions, which are beyond the reach of classical hand-crafted feature-based matching. Although keypoint matching methods have been developed in recent decades, most manual and deep learning-based techniques cannot effectively extract highly repeatable keypoints. To address that, we design a Siamese network with self-supervised training to generate similar keypoint feature maps between multimodal images, and detect highly repeatable keypoints by computing local spatial- and channel-domain peaks of the feature maps. We exploit the confidence level of keypoints to enable the detection network to evaluate potential keypoints with end-to-end trainability. Unlike most trainable detectors, it does not require the generation of pseudo-ground truth points. In the experiments, the proposed method is evaluated using various SAR and optical images covering different scenes. The results prove its superior keypoint detection performance compared with current state-of-art matching methods based on keypoints.