River-flow boundary delineation from digital aerial photography and ancillary images using Support Vector Machines
River-flow boundary delineation from digital aerial photography and ancillary images using Support Vector Machines
复制标题
使用支持向量机根据数字航空摄影和辅助图像描绘河流边界
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
B. Hales
中科院分区：
文献类型：
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作者：
Inci Güneralp;A. Filippi;B. Hales
河流流量边界的划定是各种河流相关研究的重要一步，包括河流水力模拟，流量宽度估计，河流和洪泛区栖息地制图和评估。通过降低劳动力成本，提高从天气遥感图像中划分流量边界的自动化水平提供了巨大的潜力，特别是对于专注于长河段的研究和那些检查流量随时间变化的研究。本文研究了利用支持向量机（SVM）和图像辅助数据层从航空照片中划定河道水流边界的方法。它还包括对划界准确性的定量评价。研究结果表明，支持向量机执行令人满意的划定的边界，和辅助数据层产生的边缘检测器和空间域纹理统计，特别是提高划定精度。此外，一个多尺度的评价方案允许检查整个河段的SVM的性能，以及为不同的地貌和环境条件的子河段。
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.