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research-article

Locality Robust Domain Adaptation for cross-scene hyperspectral image classification

Authors:

Ran TaoAuthors Info & Claims

Volume 238, Issue PA

https://doi.org/10.1016/j.eswa.2023.121822

Published: 27 February 2024 Publication History

Abstract

Domain adaptation (DA) has become a widely used technique for cross-scene hyperspectral image (HSI) classification. Most DA methods aim to learn a domain invariant subspace that reduce the domain discrepancy between source and target domains. However, some of them fail to explore the local manifold structure between different domains, while also neglecting the negative influences of abnormal features. To solve these problems, we propose a Locality Robust Domain Adaptation (LRDA) method for cross-domain data recognition. In LRDA, the statistical alignment is applied to reduce the domain-shift between the source and target domains. Then, LRDA combines the row-sparsity constraint and the discriminant regularization term to learn a robust projection matrix, while maintaining the discriminative capability of the matrix. Furthermore, a manifold regularization term is proposed to automatically learn the nearest neighbors and weights between two domains. The proposed LRDA not only reduces the discrepancy between two domains but also explores the local neighbor information between them. Experiment results on three HSI datasets illustrate that the proposed LRDA has better performance than other related methods.

Highlights

•

A marginal distance constraint is utilized to reduce the domain shift.

•

LRDA adopts L2,1-norm to eliminate the influence of inessential features.

•

A ridge regression term is designed to improve the discriminative ability.

•

LRDA explores the local structure of samples automatically.

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Cited By

Zhang XZhao SSong ZGuo HZhang JZheng CQiang WCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Not All Frequencies Are Created Equal: Towards a Dynamic Fusion of Frequencies in Time-Series ForecastingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681210(4729-4737)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681210

Index Terms

Locality Robust Domain Adaptation for cross-scene hyperspectral image classification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Classification and regression trees

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 238, Issue PA

Mar 2024

1584 pages

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 27 February 2024

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Cited By

Zhang XZhao SSong ZGuo HZhang JZheng CQiang WCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Not All Frequencies Are Created Equal: Towards a Dynamic Fusion of Frequencies in Time-Series ForecastingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681210(4729-4737)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681210

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