Recognition-Driven Two-Dimensional Competing Priors Toward Automatic and Accurate Building Detection
Recognition-Driven Two-Dimensional Competing Priors Toward Automatic and Accurate Building Detection
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DOI：
10.1109/tgrs.2008.2002027
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发表时间：
2009
影响因子：
8.2
通讯作者：
K. Karantzalos;N. Paragios
中科院分区：
文献类型：
--
作者：
K. Karantzalos;N. Paragios
本文提出了一种新的基于投影驱动的变分框架，用于从航空和卫星图像中提取多个建筑物。为此，竞争形状先验被认为是，并通过图像分割方法，涉及到使用数据驱动的条款约束从以前的模型，建筑物提取解决。所提出的框架扩展了以前的方法对多个形状先验的水平集分割的集成。特别是，它估计建筑物的数量以及它们的姿势从观察到的数据。因此，它可以解决多个建筑物提取从一个单一的光学图像，在各种地球科学和遥感应用中具有根本重要性的高要求的任务。此外，它可以很容易地扩展到处理其他遥感数据通过一个简单的修改的图像项。非常有希望的实验结果和执行的定性和定量评价表明，我们的方法的潜力。
In this paper, a novel recognition-driven variational framework, toward multiple building extraction from aerial and satellite images, is introduced. To this end, competing shape priors are considered, and building extraction is addressed through an image segmentation approach that involves the use of a data-driven term constrained from the prior models. The proposed framework extends previous approaches toward the integration of multiple shape priors into the level-set segmentation. In particular, it estimates the number of buildings as well as their pose from the observed data. Therefore, it can address multiple building extraction from a single optical image, a highly demanding task of fundamental importance in various geoscience and remote-sensing applications. Furthermore, it can be easily extended to deal with other remote-sensing data through a simple modification of the image term. Very promising experimental results and the performed qualitative and quantitative evaluation demonstrate the potential of our approach.