Name
Title | ||
|---|---|---|
Automatic Area-Based Registration Of Optical And Sar Images Through Generative Adversarial Networks And A Correlation-Type Metric |
Abstract | ||
|---|---|---|
The automatic registration of multisensor remote sensing images is a highly challenging task due to the inherently different physical, statistical, and textural properties of the input data. In the present paper, this problem is addressed in the case of optical-SAR images by proposing a novel method based on deep learning and area-based registration concepts. The method integrates a conditional generative adversarial network (cGAN), an area-based cross-correlation-type l(2) similarity metric, and the COBYLA constrained maximization algorithm. Whereas correlation-type metrics are typically ineffective in the application to multisensor registration, the proposed approach allows exploiting the image translation capabilities of cGAN architectures to enable the use of an l(2) similarity metric, which favors high computational efficiency. Experiments with Sentinel-1 and Sentinel-2 data suggest the effectiveness of this strategy and the capability of the proposed method to achieve accurate registration. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IGARSS39084.2020.9323235 | IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM |
Keywords | DocType | Citations |
Multisensor image registration, conditional generative adversarial network, l(2) similarity, COBYLA | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Maggiolo | 1 | 0 | 0.34 |
| David Solarna | 2 | 0 | 0.34 |
| Gabriele Moser | 3 | 919 | 76.92 |
| Sebastiano B. Serpico | 4 | 0 | 0.34 |