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STATUS PREDICTION BY 3D FRACTAL NET CNN BASED ON REMOTE SENSING IMAGES
Fractals ( IF 4.7 ) Pub Date : 2020-05-06 , DOI: 10.1142/s0218348x20400186
LI WANG 1 , YUXI WU 1 , JIPING XU 1 , HUIYAN ZHANG 1 , XIAOYI WANG 1 , JIABIN YU 1 , QIAN SUN 1 , ZHIYAO ZHAO 1
Affiliation  

The contradiction between the supply and demand of water resources is becoming increasingly prominent, whose main reason is the eutrophication of rivers and lakes. However, limited and inaccurate data makes it impossible to establish a precise model to successfully predict eutrophication levels. Moreover, it is incompetent to distinguish the degree of eutrophication status of lakes by manual calculation and processing. Focusing on these inconveniences, this study proposes 3D fractal net CNN to extract features in remote sensing images automatically, aiming at achieving scientific forecasting on eutrophication status of lakes. In order to certificate the effectiveness of the proposed method, we predict the state of the water body based on remote sensing images of natural lake. The images in natural lake were accessed by MODIS satellite, cloud-free chlorophyll inversion picture of 2009 was resized into [Formula: see text] patches, which were collected as training and testing samples. In the total of 162 pictures, our study makes three consecutive pictures as a set of data so as to attain 120 group of training and 40 testing data. Taking one set of data as input of the neural network and the next day’s eutrophication level as labels, CNNs act considerable efficiency. Through the experimental results of 2D CNN, 3D CNN and 3D fractal net CNN, 3D fractal net CNN has more outstanding performance than the other two, with the prediction accuracy of 67.5% better than 47.5% and 62.5%, respectively.

中文翻译:

基于遥感图像的3D分形网络CNN状态预测

水资源供需矛盾日益突出,主要原因是江河湖泊富营养化。然而,有限和不准确的数据使得无法建立一个精确的模型来成功预测富营养化水平。此外,人工计算处理也无法区分湖泊的富营养化程度。针对这些不便,本研究提出3D分形网CNN自动提取遥感图像中的特征,旨在实现对湖泊富营养化状况的科学预测。为了验证所提出方法的有效性,我们基于天然湖泊的遥感图像预测水体状态。MODIS卫星获取了天然湖中的图像,将2009年无云叶绿素反转图片大小调整为[公式:见正文]块,作为训练和测试样本收集。在总共162张图片中,我们的研究将三张连续的图片作为一组数据,从而达到120组训练和40组测试数据。以一组数据作为神经网络的输入,以第二天的富营养化程度作为标签,CNNs 的效率相当高。通过 2D CNN、3D CNN 和 3D 分形网 CNN 的实验结果,3D 分形网 CNN 的性能比其他两种更为突出,预测准确率分别优于 47.5% 和 62.5%,分别为 67.5% 和 62.5%。我们的研究将三张连续的图片作为一组数据,从而得到120组训练数据和40组测试数据。以一组数据作为神经网络的输入,以第二天的富营养化程度作为标签,CNNs 的效率相当高。通过 2D CNN、3D CNN 和 3D 分形网 CNN 的实验结果,3D 分形网 CNN 的性能比其他两种更为突出,预测准确率分别优于 47.5% 和 62.5%,分别为 67.5% 和 62.5%。我们的研究将三张连续的图片作为一组数据,从而得到120组训练数据和40组测试数据。以一组数据作为神经网络的输入,以第二天的富营养化程度作为标签,CNNs 的效率相当高。通过 2D CNN、3D CNN 和 3D 分形网 CNN 的实验结果,3D 分形网 CNN 的性能比其他两种更为突出,预测准确率分别优于 47.5% 和 62.5%,分别为 67.5% 和 62.5%。
更新日期:2020-05-06
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