论文标题
Landsat中的机器学习7数据
Forestry digital twin with machine learning in Landsat 7 data
论文作者
论文摘要
使用历史数据对森林进行建模可以更准确地进化分析，从而为其他研究提供了重要的基础。作为公认和有效的工具，遥感在林业分析中起着重要作用。我们可以使用它来得出有关森林的信息，包括树型，覆盖范围和冠层密度。有许多使用统计值的森林时间序列建模研究，但很少使用遥感图像。图像预测数字双胞胎是数字双胞胎的实现，旨在预测历史数据的未来图像基础。在本文中，我们在20年内使用Landsat 7遥感图像提出了一种基于LSTM的数字双胞胎方法，用于森林建模。实验结果表明，本文中的预测双胞胎方法可以有效地预测研究区域的未来图像。
Modeling forests using historical data allows for more accurately evolution analysis, thus providing an important basis for other studies. As a recognized and effective tool, remote sensing plays an important role in forestry analysis. We can use it to derive information about the forest, including tree type, coverage and canopy density. There are many forest time series modeling studies using statistic values, but few using remote sensing images. Image prediction digital twin is an implementation of digital twin, which aims to predict future images bases on historical data. In this paper, we propose an LSTM-based digital twin approach for forest modeling, using Landsat 7 remote sensing image within 20 years. The experimental results show that the prediction twin method in this paper can effectively predict the future images of study area.
