期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 58, 期 7, 页码 4604-4616出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.2964627
关键词
Training; Deep learning; Feature extraction; Labeling; Contracts; Hyperspectral imaging; Active learning (AL); convolutional neural network (CNN); deep learning; hyperspectral image (HSI) classification; Markov random field (MRF)
类别
资金
- China Postdoctoral Science Foundation [2018M643655]
- Fundamental Research Funds for the Central Universities
- National Key Research and Development Program of China [2018YFB1004300]
- MoE-CMCC Artifical Intelligence Project [MCM20190701]
- China NSFC [61906151, 11690011, 61603292, 61721002, U1811461, 91860125]
Deep neural network has been extensively applied to hyperspectral image (HSI) classification recently. However, its success is greatly attributed to numerous labeled samples, whose acquisition costs a large amount of time and money. In order to improve the classification performance while reducing the labeling cost, this article presents an active deep learning approach for HSI classification, which integrates both active learning and deep learning into a unified framework. First, we train a convolutional neural network (CNN) with a limited number of labeled pixels. Next, we actively select the most informative pixels from the candidate pool for labeling. Then, the CNN is fine-tuned with the new training set constructed by incorporating the newly labeled pixels. This step together with the previous step is iteratively conducted. Finally, Markov random field (MRF) is utilized to enforce class label smoothness to further boost the classification performance. Compared with the other state-of-the-art traditional and deep learning-based HSI classification methods, our proposed approach achieves better performance on three benchmark HSI data sets with significantly fewer labeled samples.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据