...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Lakes & Reservoirs >Lake bathymetry from Indian Remote Sensing (P6-LISS Ⅲ) satellite imagery using artificial neural network model
【24h】

Lake bathymetry from Indian Remote Sensing (P6-LISS Ⅲ) satellite imagery using artificial neural network model

机译：基于人工神经网络模型的印度遥感影像（P6-LISSⅢ）的湖泊测深

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The remote sensing technique provides a rapid and relatively inexpensive means of identifying silted areas in large water bodies, in order that desilting activities can be effectively conducted. This study developed lake bathymetry for a selected lake system (Akkulam-Veli Lake, Kerala, India) from the Indian Remote Sensing (IRS P6-LISS Ⅲ) satellite imagery, using an artificial neural network (ANN) model. The water depth was measured for 17 months at different points in the lake on the same date of overpass of the IRS satellite. The satellite imageries obtained for 12 December 2007 and 16 February 2009 were identified as cloud-free images. ANN models were developed with the four input series of radiance values from green, red, NIR and MIR bands observed for the satellite imagery obtained on 12 December 2007 at the sampling sites, with actual water depth measurements also being taken on the same date. A three-layered feed forward neural network with back propagation training algorithm was developed for this study. To train the model, it was run several times by changing the number of neurons, learning rate and the momentum constants until the mean square error was minimum. When the number of neurons is increased to 35, and the logsig function is used as ANN transfer function, the error becomes minimum. To test the model, the developed ANN was run for a new set of input from the satellite imagery taken on 16 February 2009. Comparing the predicted and measured values for the same sites for the same day, it was found that the model is best suited for predicting water depth using ANN and the radiance values for four bands of IRS satellite imagery. The results of this study indicated that, for the shallow lake with lower depth, the difference between the actual and predicted value was considerable. In contrast, this was not the case where the lake water depth was greater, indicating an increased prediction accuracy with ANN with increasing depths for shallow lakes. A bathymetry map prepared with ANN indicated only the lake shoreline, as well as the shallow littoral zones. The approach used in this study requires further refinement, including further of the model based on using more field measurements to obtain a better bathymetry map.
机译：遥感技术提供了一种快速且相对便宜的方法来识别大型水体中的淤积区域，从而可以有效地进行淤积活动。这项研究使用人工神经网络（ANN）模型，从印度遥感（IRS P6-LISSⅢ）卫星图像中为选定的湖泊系统（印度喀拉拉邦阿库库拉姆-韦利湖）开发了湖泊测深法。在IRS卫星越过同一天，在湖中不同位置测量了17个月的水深。 2007年12月12日和2009年2月16日获得的卫星图像被确定为无云图像。使用2007年12月12日在采样地点获得的卫星图像观测到的绿色，红色，NIR和MIR波段的辐射值的四个输入序列，开发了ANN模型，并且在同一日期也进行了实际水深测量。这项研究的三层前馈神经网络与反向传播训练算法。为了训练该模型，通过更改神经元的数量，学习率和动量常数来运行几次，直到均方误差最小为止。当神经元数增加到35，并且将logsig函数用作ANN传递函数时，误差变为最小。为了测试该模型，运行了开发的人工神经网络，以获取2009年2月16日的卫星图像的一组新输入。比较同一天同一天的预测值和测量值，发现该模型最适合使用ANN和IRS卫星图像四个波段的辐射值预测水深。研究结果表明，对于深度较浅的浅水湖泊，其实际值与预测值之间存在较大差异。相反，湖水深度更大的情况并非如此，这表明随着浅水湖泊深度的增加，人工神经网络的预测精度会提高。使用ANN编制的测深图仅显示了湖岸线以及浅海沿岸带。本研究中使用的方法需要进一步完善，包括基于使用更多现场测量值以获得更好的测深图的模型的进一步改进。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号