Signal & Image Center, Royal Military Academy, B⁻1000 Brussels, Belgium.
Sensors (Basel). 2018 Oct 14;18(10):3454. doi: 10.3390/s18103454.
The presence of border noise in Sentinel-1 Ground Range Detected (GRD) products is an undesired processing artifact that limits their full exploitation in a number of applications. All of the Sentinel-1 GRD products generated before March 2018-more than 800,000-are affected by this particular type of noise. In March 2018, an official fix was deployed that solved the problem for a large portion of the newly generated products, but it did not cover the entire range of products, hence the need for an operational tool that is able to effectively and consistently remove border noise in an automated way. Currently, a few solutions have been proposed that try to address the problem, but all of them have limitations. The scope of this paper is therefore to present a new method based on mathematical morphology for the automatic detection and masking of border noise in Sentinel-1 GRD products that is able to overcome the existing limitations. To evaluate the performance of the method, a detailed numerical assessment was carried out, using, as a benchmark, the 'Remove GRD Border Noise' module integrated in ESA's Sentinel Application Platform. The results showed that the proposed method is capable of very accurately removing the undesired noisy pixels from GRD images, regardless of their acquisition mode, polarization, or resolution and can cope with challenging features within the image scenes that typically affect other approaches.
哨兵 1 地面距向探测（GRD）产品中存在边界噪声是一种不期望的处理伪影，限制了它们在许多应用中的充分利用。在 2018 年 3 月之前生成的所有 Sentinel-1 GRD 产品——超过 80 万——都受到这种特殊类型噪声的影响。2018 年 3 月，部署了一个官方修复程序，解决了大部分新生成产品的问题，但它并没有涵盖所有产品范围，因此需要一种能够以自动化方式有效且一致地去除边界噪声的运行工具。目前，已经提出了一些解决方案来尝试解决这个问题，但它们都存在局限性。因此，本文的范围是提出一种新的基于数学形态学的方法，用于自动检测和屏蔽 Sentinel-1 GRD 产品中的边界噪声，该方法能够克服现有的局限性。为了评估该方法的性能，使用 ESA 的 Sentinel 应用平台中集成的“Remove GRD Border Noise”模块作为基准，进行了详细的数值评估。结果表明，该方法能够非常准确地从 GRD 图像中去除不需要的噪声像素，无论其采集模式、极化或分辨率如何，并且能够处理图像场景中通常影响其他方法的具有挑战性的特征。
