4.7 Article

TCANet for Domain Adaptation of Hyperspectral Images

期刊

REMOTE SENSING
卷 11, 期 19, 页码 -

出版社

MDPI
DOI: 10.3390/rs11192289

关键词

domain adaptation; TCA; hyperspectral; correlation alignment; classification

资金

  1. Conselleria de Educacion, Universidade e Formacion Profesional [GRC2014/008, ED431C 2018/19, ED431G/08]
  2. Ministerio de Economia y Empresa, Government of Spain [TIN2016-76373-P]
  3. European Regional Development Fund (ERDF)
  4. Xunta de Galicia
  5. European Union (European Social Fund - ESF)

向作者/读者索取更多资源

The use of Convolutional Neural Networks (CNNs) to solve Domain Adaptation (DA) image classification problems in the context of remote sensing has proven to provide good results but at high computational cost. To avoid this problem, a deep learning network for DA in remote sensing hyperspectral images called TCANet is proposed. As a standard CNN, TCANet consists of several stages built based on convolutional filters that operate on patches of the hyperspectral image. Unlike the former, the coefficients of the filter are obtained through Transfer Component Analysis (TCA). This approach has two advantages: firstly, TCANet does not require training based on backpropagation, since TCA is itself a learning method that obtains the filter coefficients directly from the input data. Second, DA is performed on the fly since TCA, in addition to performing dimensional reduction, obtains components that minimize the difference in distributions of data in the different domains corresponding to the source and target images. To build an operating scheme, TCANet includes an initial stage that exploits the spatial information by providing patches around each sample as input data to the network. An output stage performing feature extraction that introduces sufficient invariance and robustness in the final features is also included. Since TCA is sensitive to normalization, to reduce the difference between source and target domains, a previous unsupervised domain shift minimization algorithm consisting of applying conditional correlation alignment (CCA) is conditionally applied. The results of a classification scheme based on CCA and TCANet show that the DA technique proposed outperforms other more complex DA techniques.

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。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据