基于视觉显著性和图分割的高分辨率遥感影像中人工目标区域提取

A Man-made Object Area Extraction Method Based on Visual Saliency Detection and Graph-cut Segmentation for High Resolution Remote Sensing Imagery

目标检测与提取是遥感影像处理与解译的重要研究内容。提出一种基于显著性检测和图像分割的面向对象高精度目标提取方法。首先,给出一种融合"基于图论的视觉显著性"和"基于边线密度的视觉显著性"的显著性计算模型。通过引入线密度,可以在复杂背景图像下有效提取目标区域,用于高分辨率遥感图像无监督的快速场景分析。然后,利用图论分割方法获取特征相似的图像区域。同一区域中的像素具有相似的显著度值和特征。以图块为对象分析其显著性大小,可以提取精细的目标轮廓。相对于基于像素点的显著性目标提取方法,本文所用面向对象的分析方法能够在保证较高检测精度的同时有效降低冗余检测率。在高分辨率遥感影像上的试验证实对人工目标(如建筑物)的检测更准确并且所得轮廓更精确。

Object detection and extraction are very important research topic in remote sensing processing and analysis. An object-oriented based accurate object extraction method was proposed by combining saliency detection and image segmentation. Firstly, a new saliency detection method which is adequate for high resolution remote sensing image analysis is presented by fusing graph-based visual saliency detection and line density visual saliency detec- tion. By introducing line density, the proposed method can detect building regions under very complex background remote sensing images in an unsupervised manner. Then, graph-cut based segmentation is used to obtain image regions. Pixels in each region have similar saliency scores and features. Accurate boundaries of objects can be extracted by analyzing saliency of these regions. Compared with pixel based salient objects detection methods, our method has high true detection rate as well as low false detection rate by using object-oriented idea. Experi- mental results also demonstrate that our method can detect human buildings accurate target boundary.