人工智能
模式识别(心理学)
计算机科学
高光谱成像
卷积神经网络
分类器(UML)
深度学习
上下文图像分类
公制(单位)
人工神经网络
残余物
机器学习
图像(数学)
算法
运营管理
经济
作者
Bing Liu,Xi Yu,Anzhu Yu,Pengqiang Zhang,Gang Wan,Ruirui Wang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2019-04-01
卷期号:57 (4): 2290-2304
被引量:225
标识
DOI:10.1109/tgrs.2018.2872830
摘要
Deep learning methods have recently been successfully explored for hyperspectral image (HSI) classification. However, training a deep-learning classifier notoriously requires hundreds or thousands of labeled samples. In this paper, a deep few-shot learning method is proposed to address the small sample size problem of HSI classification. There are three novel strategies in the proposed algorithm. First, spectral–spatial features are extracted to reduce the labeling uncertainty via a deep residual 3-D convolutional neural network. Second, the network is trained by episodes to learn a metric space where samples from the same class are close and those from different classes are far. Finally, the testing samples are classified by a nearest neighbor classifier in the learned metric space. The key idea is that the designed network learns a metric space from the training data set. Furthermore, such metric space could generalize to the classes of the testing data set. Note that the classes of the testing data set are not seen in the training data set. Four widely used HSI data sets were used to assess the performance of the proposed algorithm. The experimental results indicate that the proposed method can achieve better classification accuracy than the conventional semisupervised methods with only a few labeled samples.
科研通智能强力驱动
Strongly Powered by AbleSci AI