Synthetic aperture radar (SAR) and optical sensing are different earth observation methods. Compared to the optical sensors, the SAR has the imaging advantages such as all-weather, all-time, ability to traverse clouds and vegetation, etc. We propose the application of the cycle-consistent generativ...
[5]Zheng Y, Takeuchi W. Quantitative assessment and driving force analysis of mangrove forest changes in China from 1985 to 2018 by integrating optical and radar imagery[J]. ISPRS International Journal of Geo-Information, 2020, 9(9): 513. [6]李露锋...
周玉杉, 李新, 郑东海, 任姗姗, 汪赢政, 李志伟. 亚洲高山区冰川厚度变化光学立体和双基SAR卫星监测数据、方法与展望. 测绘学报[J], 2024, 53(5): 779-800 doi:10.11947/j.AGCS.2024.20230260 ZHOU Yushan. Data, methods and perspec...
Automatic optical-to-SAR image registration is considered as a challenging problem because of the inconsistency of radiometric and geometric properties. Feature-based methods have proven to be effective; however, common features are difficult to extract and match, and the robustness of those methods st...
In optical-to-SAR image registration, the SAR image is accurately aligned with the optical image. The latest satellites are providing the georeferenced remote sensing images which do not have any orientation and scaling differences [1]. The recent technologies are capable of producing the ...
Abstract: The development and characteristics of high-resolution optical/SAR satellites are introduced. Geometric radiometric calibration is an important means to improve the image quality. The basic principles of optical/SAR geometric r...
We propose a new synthetic aperture radar (SAR) despeckling technique based on nonlocal filtering and driven by a coregistered optical image. A preliminary homogeneous versus heterogeneous classification of the image is used to decide where the optical guide can be safely used, thus preventing any...
Feature based registration algorithm has good adaptability to gray difference, rotation and scale change, such as SIFT5 and accelerated robust feature (SURF) calculation6. Among them, SIFT algorithm is widely used in optical image registration7. Yu et al.8 used the combination of spatial feature ...
With this paper, we publish the SEN1-2 dataset to foster deep learning research in SAR-optical data fusion. SEN1-2 comprises 282,384 pairs of corresponding image patches, collected from across the globe and throughout all meteorological seasons. Besides a detailed description of the dataset, ...
(SAR) data and optical imagery. To promote the development of deep learning based SAR-optical fusion approaches, we release the QXS-SAROPT dataset, which contains 20,000 pairs of SAR-optical image patches. We obtain the SAR patches from SAR satellite GaoFen-3 images and the optical patches ...