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首页> 外文期刊>Journal of Environmental Geography >Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas
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Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

机译：用于临时淹没区域的地图光学卫星图像的土地利用/土地覆盖机的机器学习技术

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Classification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.
机译：使用机器学习技术进行多光谱光学卫星数据的分类，从而导出土地使用/ Land Cover主题数据对于许多应用很重要。比较最新的算法，我们的研究旨在确定分类土地使用/陆地覆盖的最佳选择，特别关注匈牙利南部的临时淹没的土地。这些洪水破坏了农业实践，并可能导致大量财务损失。 Hentinel 2具有高时和中等空间分辨率的数据使用随机林的开源实现来分类，支持向量机和人工神经网络。每个分类模型应用于相同的数据集，并且定性和定量地比较结果。所有方法的结果的准确性很高，并且没有显示出大的总体差异。定量空间比较表明神经网络提供了最佳结果，但所有模型都受到图像中大气干扰的强烈影响。

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