2015-2020年南极和格陵兰冰盖冰裂隙数据

基于sentinel-1超分宽幅SAR数据，利用提出的U-net冰裂隙探测方法，形成了南北极冰盖冰裂隙高程数据。首先对sentinel-1超分宽幅SAR数据预处理，主要包括辐射定标、冰盖范围确定和斑点噪声去除。其中，为抑制SAR数据的斑点噪声，同时为了保证冰裂隙特征，我们采用了去除乘性噪声的PPB方法。该方法既能有效去除斑点，还能保留冰裂隙的特征。其次，我们利用提出的基于U-net的冰裂隙探测算法进行冰裂隙提取。为了获取正确冰裂隙SAR数据样本，我们通过比对冰裂隙高分辨率光学数据来对SAR样板进行选取，从而形成冰裂隙SAR数据样本。基于冰裂隙区域和非冰裂隙区域SAR数据样本，我们利用U-net方法对冰裂隙进行提取。最后，我们对探测出的冰裂隙数据进行地理编码形成南北极冰盖冰裂隙产品。

Based on super-resolution wide-swath SAR data from Sentinel-1, we developed ice crevasse elevation datasets for the Arctic and Antarctic polar ice sheets using the proposed U-net-based ice crevasse detection method. First, preprocessing was conducted on the Sentinel-1 super-resolution wide-swath SAR data, including radiometric calibration, ice sheet extent delineation, and speckle noise mitigation. To suppress speckle noise in SAR data while preserving the distinctive features of ice crevasses, we adopted the PPB method for multiplicative noise removal. This approach can effectively eliminate speckle noise while retaining the critical characteristics of ice crevasses. Subsequently, we utilized the proposed U-net-based ice crevasse detection algorithm to extract ice crevasses. To acquire accurate SAR data samples of ice crevasses, we selected valid SAR samples by cross-referencing with high-resolution optical data of ice crevasses, thus establishing the ice crevasse SAR sample dataset. Based on the SAR samples from both ice crevasse and non-ice crevasse regions, we applied the U-net method to extract ice crevasses. Finally, we performed geocoding on the detected ice crevasse data to generate finalized ice crevasse products for the polar ice sheets of the Arctic and Antarctic.