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River-flow boundary delineation from digital aerial photography and ancillary images using Support Vector Machines

机译:使用支持向量机从数字航拍和辅助图像划定河流边界

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摘要

Delineation of river-flow boundaries constitutes an important step in various river-related studies, including river hydraulic modeling, flow-width estimations, and river and floodplain habitat mapping and assessment. Increasing the level of automation of . delineation of flow boundaries from synoptic remote-sensing images provides great potential, by reducing the labor cost, especially for studies focusing on long river reaches and those examining flow changes over time. This article investigates the boundary delineation of river channel flow from aerial photographs using Support Vector Machine (SVM) and image-derived ancillary data layers. It also includes a quantitative evaluation of delineation accuracy. The findings show that SVM performs satisfactory delineations of the boundaries, and the ancillary data layers generated using edge detectors and spatial domain texture statistics particularly increase delineation accuracy. Moreover, a multiscale evaluation scheme allows for examining the performance of SVM for the whole river reach, as well as that for the subriver sections with varied geomorphic and environmental conditions.
机译:河流边界的划定是与河流有关的各种研究的重要步骤,包括河流水力模型,流量宽度估计以及河流和洪泛区生境制图和评估。提高自动化程度。通过减少人工成本,从天气遥感图像划定流动边界提供了巨大的潜力,特别是对于着眼于长河沿流和研究随时间变化的研究而言。本文研究了使用支持向量机(SVM)和图像辅助数据层从航拍照片中划定河道水流的边界的方法。它还包括对划界准确性的定量评估。研究结果表明,SVM对边界进行了令人满意的描绘,并且使用边缘检测器和空间域纹理统计生成的辅助数据层特别提高了描绘的准确性。此外,多尺度评估方案允许检查支持向量机在整个河段以及具有不同地貌和环境条件的子河段的性能。

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