计算机工程与应用2019，Vol.55Issue(5)：181-186,6.DOI:10.3778/j.issn.1002-8331.1805-0134
深度迁移学习在高光谱图像分类中的运用
Application of Deep Transfer Learning in Hyperspectral Image Classification
摘要
Abstract
In the field of hyperspectral image classification, the potential of spatial features is just taken into consider-ation in recent years and yet still not fully exploited. In this work, it generalizes the deep residual network to hyperspectral image classification as a feature extractor which is pre-trained on large-scale common image datasets, the discriminability of extracted features is verified on real data experiments and showed to be very promising. Moreover, under the super-vised learning setting, aiming at the problem of overfitting due to insufficient label samples, a model-based transfer learning strategy is proposed. Through pre-training the deep residual network in another related hyperspectral data set, it then fixes the shallow convolution kernel parameters, and uses a small number of labeled samples of the target data set to fine-tune the network top-level convolution kernel parameters. The ability of generalization on new data set is also proved.关键词
高光谱/深层残差网络/迁移学习Key words
hyperspectral/ deep residual network/ transfer learning分类
信息技术与安全科学引用本文复制引用
王立伟,李吉明,周国民,杨东勇..深度迁移学习在高光谱图像分类中的运用[J].计算机工程与应用,2019,55(5):181-186,6.基金项目
陕西省国际科技合作基地项目(No.2017GHJD-009). （No.2017GHJD-009）
