基于多层感知器神经网络的多光谱图像识别与水体地籍登记

E. Dianderas, K. Rojas, G. Kemper
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引用次数: 3

摘要

本文开发了一种技术,该技术可以通过多层感知器神经网络的多光谱图像处理来计算植被、冰川和水体的存在,并提供了区分湖泊存在的选项,从而生成这些水体的地籍登记。所实现的监督分类具有很高的鲁棒性和可靠性,因为在地理位置级别上获得的数据的验证误差为0%,面积和周长参数的误差约为10%。
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Identification and cadastral registration of water bodies through multispectral image processing with multi-layer Perceptron Neural Network
In this article is developed a technique that allows to calculate the presence of vegetation, glaciers and water bodies through multispectral image processing employing a Multi-layer Perceptron Neural Netwok, giving the option to discriminate the presence of lakes to generate the cadastral registration of these. The supervised classification that was implemented has a high level of robustness and reliability, since the validation of the data obtained at a geolocation level have a 0% of error and the parameters of the area and perimeter an approximate error of 10%.
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