A. Kuzmin, L. Grekov, Georgii Veriuzhskyi, Oleksii Petrov
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引用次数: 0
摘要
本文探讨了利用合成孔径雷达卫星图像识别海船的问题。它描述了自动监测的软件和技术综合体的主要功能。该系统利用 SAR 卫星 Sentinel 1A (B) 的空间图像运行。详细介绍了在海面上检测与船舶有关的标记的算法部分。为减少斑点噪声的影响,使用改进的李氏滤波器对图像进行预处理。进一步的处理在于使用自适应阈值算法,为图像的每个局部背景片段提供异常亮像素的检测,同时该算法提供恒定的错误概率。通过求解一个非线性方程,算法会为背景窗口的每个位置找到阈值亮度值,然后将所有高于该值的像素视为船只。事先要对背景窗口每个位置的像素亮度统计分布参数进行评估。K-mean 值用于这种分布。将选定的亮像素组合成紧凑的组,并确定其大小和坐标。将获得的结果与船舶自动识别系统(AIS)的数据进行比较,并将结果显示在地图上。
Software and technological complex of identification of sea vessels based on the use of radar space images Sentinel 1
The paper considers the problem of using images from SAR satellites for the identification of seagoing vessels. It describes the main functions of software and technological complex of the automated monitoring. The system is operated with utilizing space images of SAR satellites Sentinel 1A (B). The algorithmic part, which implements the detection on the sea surface the marks associated with ships, is described in details. To reduce the impact of speckle-noise, the image is pre-processed with the improved Lee-filter. Further processing lies in using an adaptive threshold algorithm that provides detection for each local background fragment of the image the unusually bright pixels, at the same time the algorithm provides a constant probability of error. By solving a nonlinear equation, for each position of the background window the algorithm finds the threshold brightness value and then all pixels above this value are considered vessels. In advance the evaluation of parameters of statistical distribution of pixels’ brightness is performed for each position of the background window. K-mean is used for such distribution. The selected bright pixels are combined into compact groups and their size and coordinates are being determined. The obtained results are compared with the data of the AIS, Automatic Identification System of ships, and the results are displayed on a cartographic basis.