Remote sensing image registration of disaster-affected areas based on deep learning feature matching

With the rapid development of remote sensing technology, remote sensing registration plays an important role in the assessment of various natural disasters, especially earthquakes. However, multi-temporal remote sensing images for the assessment have some characteristics, e.g. large-scale and rotation, resulting in challenges of remote sensing registration. In order to better register remote sensing images, we propose a new image registration method with a deep learning feature matching strategy. We first extract the pre-match point sets M and S by using SIFT-FLANN (SIFT-Fast Library for Approximate Nearest Neighbors). Second, we filter out the correct matching point pairs from M and S by using a multiscale neighborhood information network and a dual-path ConvNeXt network with self-attention-guided local information enhancement. Thirdly, we register multi-temporal remote sensing images by solve the model parameters of the spatial transformation. Finally, we evaluate our proposed method using a variety of remote sensing images with different phases, including visible light images with different illumination, scale and geometry changes. On the remote sensing image dataset containing images of pre- and post-earthquake, we compare our method to existing state-of-the-art methods and provide the results with the evaluation indexes such as Root Mean Square Error (RMSE). The results show that our method for multi-temporal remote sensing registration has a higher registration accuracy and more robustness.