Stationary marine targets, such as oil rigs and offshore wind turbines, usually show up like bright spots without or with trivial position shifts in multitemporal synthetic aperture radar (SAR) imagery. They will trigger considerable false alarms in target detection applications tasked with ship detection, if they are not identified. An algorithm for stationary marine target detection, developed based on the assumption that the apparent positions of a stationary target in multitemporal imagery are also stationary or nearly so, is proposed in this article. This algorithm requires a strict time-series of SAR images in temporal sequence as input. For each input SAR image, all targets on sea surface are initially detected with an iterative cell-averaging constant false-alarm rate detection algorithm, and their longitude and latitude positions are then used to identify whether they are stationary targets. Under this algorithm, a stationariness index with five levels (“unknown target,” “suspected new stationary target,” “stationary target,” “suspected removed stationary target,” and “removed target”) is defined for each target and must be iteratively updated with the latest level of identification. The proposed algorithm is promising for monitoring the status of stationary marine targets over a large sea area, because the processing of all input SAR images follows the same procedure and meanwhile the stationariness index is generated and kept updated. Two examples with GF-3 and RADARSAT-2 images are presented to illustrate the effectiveness of the proposed algorithm in detecting both offshore wind turbines and oil rigs.

中文翻译：

---

使用时间序列 SAR 图像进行静止海洋目标检测

在多时相合成孔径雷达 (SAR) 图像中，石油钻井平台和海上风力涡轮机等固定海洋目标通常显示为亮点，没有或有微不足道的位置偏移。如果它们未被识别，它们将在负责船舶检测的目标检测应用程序中触发相当多的误报。本文提出了一种基于静止目标在多时相图像中的表观位置也是静止或接近静止的假设而开发的静止海洋目标检测算法。该算法需要时间序列中严格时间序列的 SAR 图像作为输入。对于每幅输入的 SAR 图像，首先使用迭代单元平均恒定误报率检测算法检测海面上的所有目标，然后使用它们的经纬度位置来识别它们是否是静止目标。在该算法下，为每个目标定义了一个具有五个级别（“未知目标”、“疑似新静止目标”、“静止目标”、“疑似已移除静止目标”和“已移除目标”）的平稳性指数，并且必须迭代更新了最新级别的标识。由于所有输入SAR图像的处理遵循相同的程序，同时生成并保持更新的平稳性指数，所提出的算法有望用于监测大海域静止海洋目标的状态。给出了 GF-3 和 RADARSAT-2 图像的两个示例，以说明所提出的算法在检测海上风力涡轮机和石油钻井平台方面的有效性。