基于直线和区域特征的遥感影像线状目标检测

Knowledge Based High Resolution Remote Sensing Image Segmentation

针对高分辨率航空遥感影像中线状目标的特点,提出一种结合区域和直线特征识别线状目标的方法。在基于标记点分水岭变换进行初始分割的基础上,利用关于目标的知识和区域邻接图(RAG)对感兴趣区域进行合并,得到最终检测结果。实验结果表明,本文方法可以有效地从遥感影像中提取线状目标。

This paper proposes an effective approach to extract linear obiect in high-resolution remote sensing image. The approach integrates the knowledge about the linear object to implement the watershed algorithm and guide the region merging and finally extract the linear object. First, theKalman filter algorithm is used to detect the straight line in the image, the center point of parallel line pairs and the minimum with dynamics larger than predefined threshold are utilized as marker point to modify the morphological gradient of the input image by geodesic reconstruction, the modified gradient image is then segmented by the watershed transform. The initial segmentation result is input to region merging process. This process applies the region adjacency graph （RAG） representation of the segmented regions and knowledge about the road to execute the region merging, which significantly reduce the merging time. The proposed scheme was tested on remote sensing images of 2 m resolution, and the results show that the extraction of road is quite promising.