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 ell^{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 ell^{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.
Automatic Area-Based Registration of Optical and SAR Images Through Generative Adversarial Networks and a Correlation-Type Metric
Maggiolo L.;Solarna D.;Moser G.;Serpico S. B.
2020-01-01
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 ell^{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 ell^{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.File | Dimensione | Formato | |
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