Label Noise Robust Classification Of Hyperspectral Data
Label Noise Robust Classification Of Hyperspectral Data
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DOI：
10.1109/whispers.2018.8747035
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发表时间：
2018-09
期刊：
影响因子：
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通讯作者：
Alina E. Maas;Behnood Rasti;M. Ulfarsson
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文献类型：
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作者：
Alina E. Maas;Behnood Rasti;M. Ulfarsson
遥感数据监督分类是自动目标检测和地形数据更新的经典方法。为了训练分类器，需要已经标记的图像数据。训练标签通常是针对给定数据的部分手动创建的，这是耗时的，因此成本高昂。另一种方法是使用来自旧的过时数据集（称为map）的标签，以避免手动操作。由于随着时间的推移发生了变化，一些标签可能是错误的，因此所使用的分类器必须能够处理这一点。在本文中，我们应用标签噪声鲁棒分类两个高光谱数据集使用模拟过时的地图进行训练。
Supervised classification on remotely sensed data is a classical method to detect objects automatically and to update topo-graphical datasets. To train the classifier, already labeled image data are needed. The training labels are typically created manually for parts of the given data, which is time-consuming and thus costly. Another approach would be to use the labels from an old and outdated dataset, called map, to avoid manual effort. Due to changes over time some of the labels might be wrong, and thus the used classifier has to be able to deal with that. In this paper, we apply label noise robust classification on two hyperspectral datasets using a simulated outdated map for training.