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Open AccessArticle

TCANet for Domain Adaptation of Hyperspectral Images

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Centro Singular de Investigación en Tecnologías Inteligentes, Universidade de Santiago de Compostela, Santiago de Compostela 15782, Spain
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Departamento de Electrónica y Computación, Universidade de Santiago de Compostela, Santiago de Compostela 15782, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2289; https://doi.org/10.3390/rs11192289
Received: 30 July 2019 / Revised: 18 September 2019 / Accepted: 25 September 2019 / Published: 30 September 2019
(This article belongs to the Special Issue Hyperspectral Imagery for Urban Environment)
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. View Full-Text
Keywords: domain adaptation; TCA; hyperspectral; correlation alignment; classification domain adaptation; TCA; hyperspectral; correlation alignment; classification
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MDPI and ACS Style

S. Garea, A.; Heras, D.B.; Argüello, F. TCANet for Domain Adaptation of Hyperspectral Images. Remote Sens. 2019, 11, 2289.

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