The QXS-SAROPT Dataset for Deep Learning in SAR-Optical Data Fusion
This provides a new dataset for researchers in remote sensing and deep learning, addressing a specific data bottleneck, but it is incremental as it builds on existing data fusion efforts.
The authors tackled the lack of aligned multi-sensor image data for SAR-optical fusion by releasing the QXS-SAROPT dataset, which contains 20,000 pairs of SAR-optical image patches covering three port cities to promote deep learning approaches in this area.
Deep learning techniques have made an increasing impact on the field of remote sensing. However, deep neural networks based fusion of multimodal data from different remote sensors with heterogenous characteristics has not been fully explored, due to the lack of availability of big amounts of perfectly aligned multi-sensor image data with diverse scenes of high resolutions, especially for synthetic aperture radar (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 from Google Earth images. These images cover three port cities: San Diego, Shanghai and Qingdao. Here, we present a detailed introduction of the construction of the dataset, and show its two representative exemplary applications, namely SAR-optical image matching and SAR ship detection boosted by cross-modal information from optical images. As a large open SAR-optical dataset with multiple scenes of a high resolution, we believe QXS-SAROPT will be of potential value for further research in SAR-optical data fusion technology based on deep learning.