计算机科学
过度拟合
人工智能
卷积神经网络
高光谱成像
学习迁移
模式识别(心理学)
上下文图像分类
数据建模
深度学习
RGB颜色模型
人工神经网络
图像(数学)
数据库
作者
Haokui Zhang,Ying Li,Yenan Jiang,Peng Wang,Qiang Shen,Chunhua Shen
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2019-07-22
卷期号:57 (8): 5813-5828
被引量:168
标识
DOI:10.1109/tgrs.2019.2902568
摘要
Recently, hyperspectral image (HSI) classification approaches based on deep learning (DL) models have been proposed and shown promising performance. However, because of very limited available training samples and massive model parameters, DL methods may suffer from overfitting. In this paper, we propose an end-to-end 3-D lightweight convolutional neural network (CNN) (abbreviated as 3-D-LWNet) for limited samples-based HSI classification. Compared with conventional 3-D-CNN models, the proposed 3-D-LWNet has a deeper network structure, less parameters, and lower computation cost, resulting in better classification performance. To further alleviate the small sample problem, we also propose two transfer learning strategies: 1) cross-sensor strategy, in which we pretrain a 3-D model in the source HSI data sets containing a greater number of labeled samples and then transfer it to the target HSI data sets and 2) cross-modal strategy, in which we pretrain a 3-D model in the 2-D RGB image data sets containing a large number of samples and then transfer it to the target HSI data sets. In contrast to previous approaches, we do not impose restrictions over the source data sets, in which they do not have to be collected by the same sensors as the target data sets. Experiments on three public HSI data sets captured by different sensors demonstrate that our model achieves competitive performance for HSI classification compared to several state-of-the-art methods.
科研通智能强力驱动
Strongly Powered by AbleSci AI