基于最大熵阈值分割的SAR图像溢油检测

SAR Image Oil Spill Detection Based on Maximum Entropy  Threshold Segmentation

  • 摘要: 近年来,海上溢油事故频发,使用(合成孔径雷达)SAR遥感图像进行溢油检测有着十分重要的意义。本文提出了一种基于最大熵阈值分割的SAR图像溢油检测算法,算法运算简单,适用于星载平台,可实现高效准确的检测。由于SAR成像存在固有的相干斑噪声,首先需要进行滤波对噪声进行抑制。图像中存在的陆地区域会对溢油检测产生影响,通过先验知识利用经纬度信息对其进行掩模处理,之后采用滑动窗口的方法,在窗口内部基于最大熵选取最佳的分割阈值,最后对分割产生的小块区域进行滤除,并依据距离信息合并相邻的区域。算法使用GF-3卫星图像进行验证测试,并与其他算法对比表明,本算法可满足遥感图像检测实时性、准确性的要求。

     

    Abstract: In recent years, marine oil spills have occurred frequently, and the use of SAR remote sensing images for oil spill detection is of great significance. In this paper, a SAR image oil spill detection algorithm based on maximum entropy threshold segmentation was proposed. The algorithm is simple and suitable for on-board platform, which can achieve efficient and accurate detection. Since SAR imaging has inherent speckle noise, filtering is first required to suppress noise. The land area existing in the image will affect the oil spill detection. Depend on latitude and longitude information, we mask the land area before performing a sliding window operation. Inside the window, we determine the optimal segmentation threshold for background and oil spill based on maximum entropy. Finally, The small block area generated by the segmentation is filtered, and the adjacent regions are merged according to the distance information. The GF-3 satellite images were used for verification test, and compared with other algorithms, the algorithm can meet the requirements of real-time and accuracy of remote sensing image detection.

     

/

返回文章
返回