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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Automatic Target Detection in High-Resolution Remote Sensing Images Using a Contour-Based Spatial Model
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Automatic Target Detection in High-Resolution Remote Sensing Images Using a Contour-Based Spatial Model

机译：使用基于轮廓的空间模型在高分辨率遥感图像中进行自动目标检测

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摘要

In this letter, we propose a contour-based spatial model which can detect geospatial targets accurately in high-resolution remote sensing images. To detect the geospatial targets with complex structures, each image was partitioned into pieces as target candidate regions using multiple segmentations at first. Then, the automatic identification of target seed regions is achieved by computing the similarity of the contour information with the target template using dynamic programming. Finally, the contour-based similarity was further updated and combined with spatial relationships to figure out the missing parts. In this way, a more accurate target detection result can be achieved. The precision, robustness, and effectiveness of the proposed method were demonstrated by the experimental results.
机译：在这封信中，我们提出了一种基于轮廓的空间模型，该模型可以在高分辨率遥感影像中准确检测地理空间目标。为了检测具有复杂结构的地理空间目标，首先使用多个分割将每幅图像划分为多个部分作为目标候选区域。然后，通过使用动态编程计算轮廓信息与目标模板的相似度来实现目标种子区域的自动识别。最后，基于轮廓的相似度被进一步更新，并与空间关系相结合以找出缺失的部分。这样，可以实现更准确的目标检测结果。实验结果证明了该方法的准确性，鲁棒性和有效性。

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