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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks
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Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks

机译：使用ImageNet预训练网络进行深度学习地球观测分类

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摘要

Deep learning methods such as convolutional neural networks (CNNs) can deliver highly accurate classification results when provided with large enough data sets and respective labels. However, using CNNs along with limited labeled data can be problematic, as this leads to extensive overfitting. In this letter, we propose a novel method by considering a pretrained CNN designed for tackling an entirely different classification problem, namely, the ImageNet challenge, and exploit it to extract an initial set of representations. The derived representations are then transferred into a supervised CNN classifier, along with their class labels, effectively training the system. Through this two-stage framework, we successfully deal with the limited-data problem in an end-to-end processing scheme. Comparative results over the UC Merced Land Use benchmark prove that our method significantly outperforms the previously best stated results, improving the overall accuracy from 83.1% up to 92.4%. Apart from statistical improvements, our method introduces a novel feature fusion algorithm that effectively tackles the large data dimensionality by using a simple and computationally efficient approach.
机译：如果提供足够大的数据集和相应的标签，则深度学习方法（例如卷积神经网络（CNN））可以提供高度准确的分类结果。但是，将CNN与有限的标记数据一起使用可能会出现问题，因为这会导致过度拟合。在这封信中，我们通过考虑为解决完全不同的分类问题（即ImageNet挑战）而设计的经过预训练的CNN，提出一种新颖的方法，并利用它来提取一组初始表示。然后将派生的表示形式及其类标签一起转移到受监督的CNN分类器中，从而有效地训练系统。通过此两阶段框架，我们成功地以端到端处理方案处理了有限数据问题。与UC Merced土地使用基准相比的比较结果证明，我们的方法大大优于以前最好的陈述结果，将总体准确度从83.1％提高到92.4％。除了统计上的改进外，我们的方法还引入了一种新颖的特征融合算法，该算法通过使用一种简单且计算效率高的方法有效地解决了大数据维度。

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