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首页> 外文期刊>Ecological informatics: an international journal on ecoinformatics and computational ecology >Sea water chlorophyll-a estimation using hyperspectral images and supervised Artificial Neural Network
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Sea water chlorophyll-a estimation using hyperspectral images and supervised Artificial Neural Network

机译：利用高光谱图像和监督人工神经网络估算海水中的叶绿素

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The use of satellite hyperspectral images has improved the extraction of information compared to multispectral images. Although designed as a technical demonstration for land applications, Hyperion satellite hyperspectral images are used to estimate sea water parameters in the coastal area. A combination of turbid river inputs, as well as the open sea flushing, determines the quality of the sea water in the coastal area and the status of its environment. In addition, the existence of different source of pollution adds to the complexity of the coastal sea water analysis. The field campaigns to retrieve sea water parameters provided by the past completed projects were coincident with acquisition of the Hyperion image covering the pilot area. A robust method based on a supervised Feed-Forward Back-Propagation Artificial Neural Network (ANN-BP) algorithm is applied to retrieve the concentration of chlorophyll-a from hyperspectral image. In addition, Hyperion images are used to show the variation of chlorophyll-a during two different periods of time. The variation is due to many manmade environmental disasters such as oil spill and continuous discharge of chemical and solid wastes. The research proves that the new method based on ANN has improved the mathematical regression methods to a coefficient of determination almost equal 1 compared to about 0.4 for the methods not based on ANN-BP. (C) 2014 Elsevier B.V. All rights reserved.
机译：与多光谱图像相比，卫星高光谱图像的使用改善了信息的提取。尽管被设计为土地应用的技术演示，但Hyperion卫星高光谱图像用于估算沿海地区的海水参数。浑浊的河水投入以及公海的冲刷共同决定了沿海地区海水的质量及其环境状况。此外，不同污染源的存在增加了沿海海水分析的复杂性。检索过去完成的项目提供的海水参数的野外活动与采集覆盖试验区域的Hyperion图像相吻合。基于监督前馈反向传播人工神经网络（ANN-BP）算法的鲁棒方法被用于从高光谱图像中检索叶绿素a的浓度。此外，Hyperion图像用于显示两个不同时间段内叶绿素-a的变化。这种变化是由于许多人为的环境灾难造成的，例如漏油以及化学和固体废物的不断排放。研究证明，基于ANN的新方法将数学回归方法的测定系数提高到几乎等于1，而基于ANN-BP的方法的测定系数约为0.4。 （C）2014 Elsevier B.V.保留所有权利。

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