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

Qiang Chen; Fei Song; Xianyuan Liu; Sanxing Zhang; Tao Lei; Ping Jiang

doi:10.1117/12.2673374

3 April 2023 Remote sensing image registration of disaster-affected areas based on deep learning feature matching

Qiang Chen, Fei Song, Xianyuan Liu, Sanxing Zhang, Tao Lei, Ping Jiang

Proceedings Volume 12599, Second International Conference on Digital Society and Intelligent Systems (DSInS 2022); 125992F (2023) https://doi.org/10.1117/12.2673374
Event: 2nd International Conference on Digital Society and Intelligent Systems (DSInS 2022), 2022, Chendgu, China

Abstract

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.

Citation Download Citation

Qiang Chen, Fei Song, Xianyuan Liu, Sanxing Zhang, Tao Lei, and Ping Jiang "Remote sensing image registration of disaster-affected areas based on deep learning feature matching", Proc. SPIE 12599, Second International Conference on Digital Society and Intelligent Systems (DSInS 2022), 125992F (3 April 2023); https://doi.org/10.1117/12.2673374

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KEYWORDS

Image registration

Remote sensing

Education and training

Feature extraction

Unmanned aerial vehicles

Image enhancement

Deep learning

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