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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Automated Detection of Arbitrarily Shaped Buildings in Complex Environments From Monocular VHR Optical Satellite Imagery
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Automated Detection of Arbitrarily Shaped Buildings in Complex Environments From Monocular VHR Optical Satellite Imagery

机译：从单目VHR光学卫星图像中自动检测复杂环境中的任意形状的建筑物

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This paper introduces a new approach for the automated detection of buildings from monocular very high resolution (VHR) optical satellite images. First, we investigate the shadow evidence to focus on building regions. To do that, we propose a new fuzzy landscape generation approach to model the directional spatial relationship between buildings and their shadows. Once all landscapes are collected, a pruning process is developed to eliminate the landscapes that may occur due to non-building objects. The final building regions are detected by GrabCut partitioning approach. In this paper, the input requirements of the GrabCut partitioning are automatically extracted from the previously determined shadow and landscape regions, so that the approach gained an efficient fully automated behavior for the detection of buildings. Extensive experiments performed on 20 test sites selected from a set of QuickBird and Geoeye-1 VHR images showed that the proposed approach accurately detects buildings with arbitrary shapes and sizes in complex environments. The tests also revealed that even under challenging environmental and illumination conditions, reasonable building detection performances could be achieved by the proposed approach.
机译：本文介绍了一种从单眼超高分辨率（VHR）光学卫星图像中自动检测建筑物的新方法。首先，我们调查影子证据，重点关注建筑区域。为此，我们提出了一种新的模糊景观生成方法，以对建筑物及其阴影之间的定向空间关系建模。收集所有景观后，将开发修剪过程以消除由于非建筑物体而可能发生的景观。通过GrabCut分区方法检测最终建筑物区域。在本文中，从先前确定的阴影和景观区域中自动提取了GrabCut分区的输入要求，因此该方法获得了用于建筑物检测的高效，全自动行为。在从一组QuickBird和Geoeye-1 VHR图像中选择的20个测试站点上进行的广泛实验表明，该方法可以准确地检测复杂环境中具有任意形状和大小的建筑物。测试还表明，即使在具有挑战性的环境和光照条件下，所提出的方法也可以实现合理的建筑物检测性能。

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