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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Edge-Guided Image Object Detection in Multiscale Segmentation for High-Resolution Remotely Sensed Imagery
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Edge-Guided Image Object Detection in Multiscale Segmentation for High-Resolution Remotely Sensed Imagery

机译：高分辨率遥感影像多尺度分割中的边缘引导图像对象检测

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摘要

A new segmentation approach for high-resolution remotely sensed imagery that combines the global edge and region information is developed from a new scheme to monitor the best conditions for each growing object to obtain the corresponding meaningful image object during multiscale analysis. The approach, which is an extension of the image object detection approach, includes new algorithms for determination of region-growing criteria, edge-guided image object detection, and assessment of edges. The method consists of two stages: In the first stage, edges are acquired from edge detection with embedded confidence and stored in an R-tree, and initial objects are segmented by eCognition and organized in the region adjacency graph; in the second stage, meaningful image objects are obtained by incorporating multiscale segmentation and analyzing the edge completeness curve. The evaluation results of edge completeness are obtained within the process of multiscale segmentation, and the assessment for the segmentation results shows its merit in coastal remote sensing. Images containing plenty of weak edges or distributing scene objects with various sizes and shapes can fully embody the strength of this method.
机译：从一种新方案开发了一种结合了全局边缘和区域信息的高分辨率遥感影像的新分割方法，该方案可以监视每个生长物体的最佳条件，从而在多尺度分析过程中获得相应的有意义的图像物体。该方法是图像对象检测方法的扩展，包括用于确定区域增长标准，边缘引导的图像对象检测和边缘评估的新算法。该方法包括两个阶段：在第一阶段，从边缘检测中以嵌入的置信度获取边缘并将其存储在R树中，然后通过eCognition对初始对象进行分割并将其组织在区域邻接图中。在第二阶段，通过合并多尺度分割并分析边缘完整性曲线来获得有意义的图像对象。在多尺度分割过程中获得了边缘完整性的评价结果​​，对分割结果的评价表明了其在沿海遥感中的优势。包含大量弱边缘或分布各种大小和形状的场景对象的图像可以充分体现此方法的优势。

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