海洋区域受云雾和气流影响导致其光学遥感图像局部对比度低、能见度差,给舰船监视带来困难。针对这一问题,首先利用多尺度相位谱对低可观测图像进行重构生成舰船目标的显著图,然后采用全局阈值快速提取具有较高显著度的感兴趣区域(ROI)。对各个ROI,首先利用其外部环状窗口各区间平均亮度的有序统计量来估计阈值对其进行二值分割,然后分别提取平均显著度、形状复杂度和空间扩展度特征,训练最小距离分类器对ROI进行鉴别,得到最终的检测结果。用大量受云雾影响的SPOT4全色影像进行实验,结果表明该算法能够满足应用要求。

Local cloud and fog can cause low contrast and poor visibility in optical remote sensing images of certain ocean regions, which hinders ship surveillance. To overcome this, a multi-scale phase spectrum is used to reconstruct the low observable image to form a saliency map in the first step. Then, a global threshold is used to extract the regions of interest (ROI), which has higher saliency. The order statistic of mean intensities from the sub regions of a circular window around each ROI is used to estimate the local threshold for target pixel segmentation. The mean saliency, shape complexity, and spatial extent are extracted from the target pixels to form a feature vector. Then a minimum distance classifier on the extracted feature vector is trained to discard the false alarms. Results on many cloudy SPOT-4 panchromatic images show the effectiveness of the proposed algorithm.