Distinguishable keypoint detection and matching for optical satellite images with deep convolutional neural networks

DOI码：10.1016/j.jag.2022.102795

发表刊物：International Journal of Applied Earth Observation and Geoinformation

关键字：Image Matching,Keypoint Detector,Convolutional Neural Networks,Remote Sensing

摘要：As a required step in optical remote sensing applications, image matching identifies correspondences to estimate the relationship between two images. To address this task, feature-based algorithms, such as Scale-Invariant Feature Transform (SIFT), use detectors to identify keypoints and then apply descriptors to represent these keypoints as feature vectors. Thereby, these vectors from different images are matched by Euclidean distance to produce matching points as correspondences. Deep learning networks are widely used in the design of detectors and descriptors due to their powerful representation capabilities. However, previous methods oriented to repeatability tend to be of low localization accuracy. To solve this problem, we define optimal keypoints as "detected, repeatable, reasonable, and distinguishable" (DRRD). Based on this idea, we introduce a DRRD framework for remote sensing optical image matching. Specifically, we designed a deep-learning-based descriptor network which is trained in a self-supervised manner with a novel adaptive mix content triplet (AMCT) loss function. We established a training procedure for remote sensing images using a multi-spectral image dataset. The proposed method obtained 1694 correct correspondences from 8192 detected keypoints in multi-temporal image matching tasks and a sub-pixel accuracy in the image rectification experiments. Comparison experiments show that the proposed DRRD framework outperforms other state-of-the-art methods.

合写作者：Puyun Liao,Guanzhou Chen,Kun Zhu,Xiaoliang Tan,Tong Wang,Xianwei Li,Hongyu Wu

论文类型：期刊论文

文献类型：J

卷号：109

ISSN号：1569-8432

是否译文：否

发表时间：2022-04-28

收录刊物：SCI