The Hopfield neural network as a tool for feature tracking and recognition from satellite sensor images
The Hopfield neural network as a tool for feature tracking and recognition from satellite sensor images
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DOI：
10.1080/014311697218809
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发表时间：
1997-03-10
影响因子：
3.4
通讯作者：
Tatnall, ARL
中科院分区：
文献类型：
--
作者：
Cote, S;Tatnall, ARL
提出了一种基于神经网络的序列卫星传感器图像特征跟踪与识别方法。特征跟踪被认为在冰测绘、云运动风、洋流和短期预报等应用中具有重要意义。特征识别在图像自动导航中得到了应用。本文探讨了Hopfield神经网络执行特征跟踪或识别的潜力，并给出了其在三种不同应用中的实现示例。结果表明，与现有的跟踪技术相比，这种新方法的优点是精度高、速度快、对变形的敏感性低、能够检测旋转运动，并直接提供位移矢量的横截和顺截分量。Hopfield神经网络可以为通过海岸线识别实现图像自动导航提供有价值的工具。
A new approach for feature tracking and recognition on sequential satellite sensor images using neural networks has been developed. Feature tracking is recognized as being of importance in applications such as ice-mapping, cloud motion winds, ocean currents, and short-term forecasting. Feature recognition finds application in automatic image navigation. This paper explores the potential of a Hopfield neural network to perform feature tracking or recognition, and gives examples of its implementation to three different applications. It is shown that the net can provide superior performance to existing techniques for tracking, the advantages of this new approach being its precision, speed, low sensitivity to deformation, its capacity to detect rotational motion, and to provide directly the cross- and along-isopycnal components of displacement vectors. It is also shown that the Hopfield neural network can provide a valuable tool for automatic image navigation through coastline recognition.