基于人工神经网络的海洋表面波谱SAR图像反演技术

遥感信息 Pub Date : 1997-08-03 DOI:10.1109/IGARSS.1997.606394

D. Kasilingam, Jian Shi

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引用次数: 7

摘要

提出了一种基于人工神经网络的海洋表面波SAR图像频谱非线性反演技术。在该技术中，使用多层感知器(MLP)来执行反转过程。MLP使用模拟SAR和波谱进行训练。训练过程采用标准误差反向传播技术。结果表明，该方法在很大范围的风浪条件下都能很好地工作。在较高的海况下，反演过程中的误差增大。如果网络在其被训练的范围内使用，该技术效果最好。值得注意的是，该技术可以独立于SAR成像模型，通过使用SAR和波浪光谱的重合和共定位测量来训练网络。

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Artificial neural network-based inversion technique for extracting ocean surface wave spectra from SAR images

An artificial neural network (ANN) based nonlinear technique for inverting the SAR image spectrum of ocean surface waves is developed. In this technique, a multi-layer perceptron (MLP) is used to perform the inversion process. The MLP is trained using simulated SAR and wave spectra. The training process utilizes the standard error-backpropagation technique. The results indicate that the method works well over a large range of wind and wave conditions. The error in the inversion process was found to increase in the higher sea states. The technique works best if the network is used within the range over which it was trained. It is noted that this technique may be used independent of SAR imaging models, by training the network with coincident and co-located measurements of SAR and wave spectra.

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来源期刊

遥感信息

自引率

0.00%

发文量

3984

期刊介绍： Remote Sensing Information is a bimonthly academic journal supervised by the Ministry of Natural Resources of the People's Republic of China and sponsored by China Academy of Surveying and Mapping Science. Since its inception in 1986, it has been one of the authoritative journals in the field of remote sensing in China.In 2014, it was recognised as one of the first batch of national academic journals, and was awarded the honours of Core Journals of China Science Citation Database, Chinese Core Journals, and Core Journals of Science and Technology of China. The journal won the Excellence Award (First Prize) of the National Excellent Surveying, Mapping and Geographic Information Journal Award in 2011 and 2017 respectively. Remote Sensing Information is dedicated to reporting the cutting-edge theoretical and applied results of remote sensing science and technology, promoting academic exchanges at home and abroad, and promoting the application of remote sensing science and technology and industrial development. The journal adheres to the principles of openness, fairness and professionalism, abides by the anonymous review system of peer experts, and has good social credibility. The main columns include Review, Theoretical Research, Innovative Applications, Special Reports, International News, Famous Experts' Forum, Geographic National Condition Monitoring, etc., covering various fields such as surveying and mapping, forestry, agriculture, geology, meteorology, ocean, environment, national defence and so on. Remote Sensing Information aims to provide a high-level academic exchange platform for experts and scholars in the field of remote sensing at home and abroad, to enhance academic influence, and to play a role in promoting and supporting the protection of natural resources, green technology innovation, and the construction of ecological civilisation.