Remote sensing of water depths in shallow waters via artificial neural networks
Remote sensing of water depths in shallow waters via artificial neural networks
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DOI：
10.1016/j.ecss.2010.05.015
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发表时间：
2010-09-01
影响因子：
2.8
通讯作者：
Yalcin, Arisoy
中科院分区：
文献类型：
--
作者：
Ceyhun, Oezcelik;Yalcin, Arisoy
确定海岸带水深是大多数海岸工程和海岸科学应用的共同要求。然而，制作高质量的测深地图需要昂贵的实地调查、高科技设备和专家人员。遥感图像可以方便地用于降低水深测量所需的成本和劳动力，并克服空间和时间深度提供的困难。本研究引入人工神经网络（ANN）方法，通过遥感图像和样本深度测量得出浅水区的测深图。该方法将遥感图像的时间和空间能力与人工神经网络的建模灵活性相结合，为浅水区的深度估计提供了快速实用的解决方案。它在实践中的主要优点是，它能够在深度估计中直接使用图像反射率值，而无需细化其他环境因素（例如底部材料和植被）引起的深度散射。其无函数结构允许评估多波段图像和原位深度测量之间的非线性关系，因此比经典回归方法更可靠的深度估计。土耳其伊兹密尔福卡西海岸被用作试验台。 Aster 前三波段图像和 Quickbird 全色锐化图像用于导出该研究区域基于 ANN 的测深图。现场深度测量由土耳其测绘总司令部 (HGK) 提供。设置了两种模型，一种用于 Aster，一种用于 Quickbird 图像输入。仅依赖于原位深度测量的测深图被用来评估所得的测深图。本文最后讨论了该方法的效率。结论是，所提出的方法可以减少测深测绘中的空间和重复深度测量要求，特别是对于初步工程应用。 (C) 2010 Elsevier Ltd. 保留所有权利。
Determination of the water depths in coastal zones is a common requirement for the majority of coastal engineering and coastal science applications. However, production of high quality bathymetric maps requires expensive field survey, high technology equipment and expert personnel. Remotely sensed images can be conveniently used to reduce the cost and labor needed for bathymetric measurements and to overcome the difficulties in spatial and temporal depth provision. An Artificial Neural Network (ANN) methodology is introduced in this study to derive bathymetric maps in shallow waters via remote sensing images and sample depth measurements. This methodology provides fast and practical solution for depth estimation in shallow waters, coupling temporal and spatial capabilities of remote sensing imagery with modeling flexibility of ANN. Its main advantage in practice is that it enables to directly use image reflectance values in depth estimations, without refining depth-caused scatterings from other environmental factors (e.g. bottom material and vegetation). Its function-free structure allows evaluating nonlinear relationships between multi-band images and in-situ depth measurements, therefore leads more reliable depth estimations than classical regressive approaches. The west coast of the Foca, Izmir/Turkey was used as a test bed. Aster first three band images and Quickbird pan-sharpened images were used to derive ANN based bathymetric maps of this study area. In-situ depth measurements were supplied from the General Command of Mapping, Turkey (HGK). Two models were set, one for Aster and one for Quickbird image inputs. Bathymetric maps relying solely on in-situ depth measurements were used to evaluate resultant derived bathymetric maps. The efficiency of the methodology was discussed at the end of the paper. It is concluded that the proposed methodology could decrease spatial and repetitive depth measurement requirements in bathymetric mapping especially for preliminary engineering application. (C) 2010 Elsevier Ltd. All rights reserved.