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Dictionary Learning-Based Feature-Level Domain Adaptation for Cross-Scene Hyperspectral Image Classification
Ye, Minchao1; Qian, Yuntao1; Zhou, Jun2; Tang, Yuan Yan3
2017-03
Source PublicationIEEE Transactions on Geoscience and Remote Sensing
ISSN0196-2892
Volume55Issue:3Pages:1544-1562
Abstract

A big challenge of hyperspectral image (HSI) classification is the small size of labeled pixels for training classifier. In real remote sensing applications, we always face the situation that an HSI scene is not labeled at all, or is with very limited number of labeled pixels, but we have sufficient labeled pixels in another HSI scene with the similar land cover classes. In this paper, we try to classify an HSI scene containing no labeled sample or only a few labeled samples with the help of a similar HSI scene having a relative large size of labeled samples. The former scene is defined as the target scene, while the latter one is the source scene. We name this classification problem as cross-scene classification. The main challenge of cross-scene classification is spectral shift, i.e., even for the same class in different scenes, their spectral distributions maybe have significant deviation. As all or most training samples are drawn from the source scene, while the prediction is performed in the target scene, the difference in spectral distribution would greatly deteriorate the classification performance. To solve this problem, we propose a dictionary learning-based feature-level domain adaptation technique, which aligns the spectral distributions between source and target scenes by projecting their spectral features into a shared low-dimensional embedding space by multitask dictionary learning. The basis atoms in the learned dictionary represent the common spectral components, which span a cross-scene feature space to minimize the effect of spectral shift. After the HSIs of two scenes are transformed into the shared space, any traditional HSI classification approach can be used. In this paper, sparse logistic regression (SRL) is selected as the classifier. Especially, if there are a few labeled pixels in the target domain, multitask SRL is used to further promote the classification performance. The experimental results on synthetic and real HSIs show the advantages of the proposed method for cross-scene classification.

KeywordCross-scene Classification Dictionary Learning Domain Adaptation Hyperspectral Image (Hsi) Multitask Learning
DOIhttps://doi.org/10.1109/TGRS.2016.2627042
URLView the original
Indexed BySCI
Language英语
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000396106700027
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:21   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorQian, Yuntao
Affiliation1.College of Computer Science, Institute of Artificial Intelligence, Zhejiang University, Hangzhou 310027, China
2.School of Information and Communication Technology, Griffith University, Nathan, QLD 4111, Australia.
3.Faculty of Science and Technology, University of Macau, Macau 999078, China.
Recommended Citation
GB/T 7714
Ye, Minchao,Qian, Yuntao,Zhou, Jun,et al. Dictionary Learning-Based Feature-Level Domain Adaptation for Cross-Scene Hyperspectral Image Classification[J]. IEEE Transactions on Geoscience and Remote Sensing,2017,55(3):1544-1562.
APA Ye, Minchao,Qian, Yuntao,Zhou, Jun,&Tang, Yuan Yan.(2017).Dictionary Learning-Based Feature-Level Domain Adaptation for Cross-Scene Hyperspectral Image Classification.IEEE Transactions on Geoscience and Remote Sensing,55(3),1544-1562.
MLA Ye, Minchao,et al."Dictionary Learning-Based Feature-Level Domain Adaptation for Cross-Scene Hyperspectral Image Classification".IEEE Transactions on Geoscience and Remote Sensing 55.3(2017):1544-1562.
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