Training Deep Convolutional Neural Networks for Land-Cover Classification of High-Resolution Imagery
Training Deep Convolutional Neural Networks for Land-Cover Classification of High-Resolution Imagery
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DOI：
10.1109/lgrs.2017.2657778
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发表时间：
2017-04-01
影响因子：
4.8
通讯作者：
Davis, Curt H.
中科院分区：
文献类型：
--
作者：
Scott, Grant J.;England, Matthew R.;Davis, Curt H.
深度卷积神经网络（DCNN）最近已经成为各种领域机器学习的主导范式。然而，获取用于训练DCNN的适当大的数据集通常是一个重大挑战。这是遥感领域的一个主要问题，我们有非常大的卫星和航空图像的集合，但缺乏丰富的标签信息，往往是容易获得的其他图像模式。在这封信中，我们调查使用DCNN的土地覆盖分类在高分辨率遥感图像。为了克服缺乏大量标记的遥感图像数据集的问题，我们结合DCNN采用了两种技术：转移学习（TL），以及专门为遥感图像量身定制的微调和数据增强。TL允许引导DCNN，同时保留在来自不同图像域的图像语料库上学习的深度视觉特征提取。数据增强利用遥感图像的各个方面来显着扩展小的训练图像数据集，并提高DCNN对遥感图像数据的鲁棒性。在这里，我们将这些技术应用于著名的UC默塞德数据集，分别使用CaffeNet、GoogLeNet和ResNet实现了97.8 +/-2.3%、97.6 +/-2.6%和98.5 +/- 1.4%的土地覆盖分类精度。
Deep convolutional neural networks (DCNNs) have recently emerged as a dominant paradigm for machine learning in a variety of domains. However, acquiring a suitably large data set for training DCNN is often a significant challenge. This is a major issue in the remote sensing domain, where we have extremely large collections of satellite and aerial imagery, but lack the rich label information that is often readily available for other image modalities. In this letter, we investigate the use of DCNN for land-cover classification in high-resolution remote sensing imagery. To overcome the lack of massive labeled remote-sensing image data sets, we employ two techniques in conjunction with DCNN: transfer learning (TL) with fine-tuning and data augmentation tailored specifically for remote sensing imagery. TL allows one to bootstrap a DCNN while preserving the deep visual feature extraction learned over an image corpus from a different image domain. Data augmentation exploits various aspects of remote sensing imagery to dramatically expand small training image data sets and improve DCNN robustness for remote sensing image data. Here, we apply these techniques to the well-known UC Merced data set to achieve the land-cover classification accuracies of 97.8 +/- 2.3%, 97.6 +/- 2.6%, and 98.5 +/- 1.4% with CaffeNet, GoogLeNet, and ResNet, respectively.