Two-step registration of near-space remote sensing images via deep neural networks

Xiaohan Li; Meng An; Haopeng Zhang; Fengying Xie

doi:10.1117/12.2636244

26 October 2022 Two-step registration of near-space remote sensing images via deep neural networks

Xiaohan Li, Meng An, Haopeng Zhang, Fengying Xie

Proceedings Volume 12267, Image and Signal Processing for Remote Sensing XXVIII; 1226704 (2022) https://doi.org/10.1117/12.2636244
Event: SPIE Remote Sensing, 2022, Berlin, Germany

Abstract

Near-space remote sensing image registration is an important foundation of near-space image processing. For large image jitter distortion, geometric and atmospheric distortion of its image, we propose a two-step method based on deep neural networks, which includes a coarse-to-fine registration process. We construct a near-space image registration dataset which is captured from Google Maps and hot air balloon platforms, etc. For obtaining candidates, the coarse alignment stage applies classical geometric validation methods to a corresponding set of pre-trained deep features. The fine alignment network is based on pyramidal feature extraction and optical flow estimation to realize local flow field inference from coarse to fine. We construct a regularization layer for each level to ensure smoothness. Applying our method to our synthetic dataset, the experimental result shows that it has a competitive result that is evaluated based on the root mean square error, peak signal to noise ratio and structural similarity.

Conference Presentation

Citation Download Citation

Xiaohan Li, Meng An, Haopeng Zhang, and Fengying Xie "Two-step registration of near-space remote sensing images via deep neural networks", Proc. SPIE 12267, Image and Signal Processing for Remote Sensing XXVIII, 1226704 (26 October 2022); https://doi.org/10.1117/12.2636244

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available