基于高分辨率遥感影像的内河航标自动检测方法

提出了一种高分辨率遥感影像中的水运航标提取算法.首先应用单类支持向量机分类器实现水陆分割,确定水陆边界.然后将水域中的小目标作为候选目标,基于目标几何和灰度统计特性进行初步筛选,获得疑似航标目标.再利用影像中航标窗口间的相关性,提出一种基于相关系数编组的航标判定方法.最后提出一种基于在线学习原理的漏检航标检测算法,即首先依据已经检测得到的航标的空间分布对漏检航标的可能位置进行估计,再依据已检测到的航标的先验知识在估计位置进行精确检测.利用QuickBird影像进行的实验结果表明了该方法的有效性.

Detection of Navigation Marks in High resolution Remote Sensing Imagery

A novel method for extracting navigation mark using high resolution remote sensing imagery is proposed in this paper. The one class support vector machine(OCSVM) is used to segment the land and the water to derive the shoreline. Then the small targets within the water regions are found out and regarded as the candidate ones. The statistics of pixel intensity and the geometric feature of the candidate targets are used to remove a portion of false targets. Then the rest of the candidate targets are categorized into several groups according to the relationship coefficient between them and others. The group having most targets is the one that consists of navigation marks. At last, an online learning algorithm is proposed to decrease the miss rate. The spatial distribution of the extracted navigation marks are used to estimate the positions where the missing targets are likely to exist. The intensity distribution of the extracted navigation marks then are used as the prior knowledge to detect the missing target in the estimated positions. The experiments using QuickBird imagery show that the proposed method is effective.