面向星上的云层探测与规避算法

为避免在遥感卫星成像过程中,因云层遮挡影响光学卫星得到有效的地面目标信息,提出面向星上的云层探测和规避算法。首先利用自适应阈值分割目标和背景,并对分割后的二值图像进行标记,然后提出形状复杂度的概念并计算各连通域的形状复杂度,设定阈值后提取云层区域。建立了卫星轨道空间坐标系和规避云层的模型,推导了敏捷卫星姿态角调整公式,设计了卫星组网通过星间通讯在可见窗口观测目标的方案。通过实验与仿真得出该算法检测云层时间在168～281ms范围内,正确率为89%,卫星数据利用率在两组轨道参数下分别提高18.15%和22.21%。基本能够将准确度和实时性指标有效结合,并缓解了遥感图像海量数据对传输通道的压力。

On-board cloud detection and avoidance algorithms for optical remote sensing satellite

In order to avoid the influence of cloud obstruction on the optical satellites to obtain effective ground target information during the remote sensing satellite imaging process, this paper proposes an on-board cloud detection and avoidance algorithm. Firstly, the adaptive threshold is used to segment the target and background, and the segmented binary image is marked. Then the concept of shape complexity is proposed and the shape complexity of each connected component is calculated. After setting the threshold, the cloud region is extracted. Then, the satellite orbital space coordinate system and the model of avoiding clouds are established. The formulas for adjusting the attitude of agile satellites are deduced, and the satellite network is designed to observe the targets through the inter satellite communication in the visible window. Through experiments and simulations, the time for detecting clouds by utilizing the algorithm is in the range of 168-281 ms, and the correct rate is 89%. The satellite data utilization rate is increased by 18.15% and 22.21% respectively under the two sets of orbital parameters. Basically, it can effectively combine the accuracy and real time indicators, and alleviate the pressure on the transmission channel of the massive data of remote sensing images.