计算机科学
人工智能
深度学习
卷积神经网络
模式识别(心理学)
机器学习
上下文图像分类
遥感
土地覆盖
人工神经网络
特征(语言学)
封面(代数)
作者
Thorsten Wilhelm,Dominik Kossmann
出处
期刊:International Geoscience and Remote Sensing Symposium
日期:2021-07-11
卷期号:: 2496-2499
标识
DOI:10.1109/igarss47720.2021.9553364
摘要
Land cover classification is often only looked at from a classification perspective or either coarse or only local maps are used to teach automated approaches to map orbital images. In this work we complement a large remote sensing archive used for multi-label classification with pixel-synchronous land cover maps. The complementary annotations uncover a significant amount of wrongly labelled samples and yield novel insights into the shortcomings of multi-label based approaches. Further, it is now possible to train deep networks for land cover classification with pixel-wise supervision on a large scale.
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