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Instance Similarity-based Adversarial Domain Adaptation Network for Hyperspectral Image Classification

Published: 03 May 2024 Publication History

Abstract

In recent years, deep domain adaptation (DA) technology has been widely used in cross-domain hyperspectral image (HSI) classification problems. However, most existing deep DA algorithms aim to align the distributions of domains while ignoring sample information. In order to solve this problem, this paper proposes a new instance similarity-based adversarial domain adaptation (ISADA) method. ISADA combines the ideas of adversarial learning and contrastive learning to reduce domain shift from two aspects: marginal distribution and class conditional probability distribution. The adversarial training of generator and discriminator is used to reduce the difference in marginal distribution between domains, and the multi-instance contrastive (MIC) loss is used to align the category distribution between domains. Experimental results on two cross-domain HSI classification tasks show that our proposed ISADA method outperforms some existing deep DA methods.

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  • (2024)Kernel Extreme Learning Machine with Discriminative Transfer Feature and Instance Selection for Unsupervised Domain AdaptationNeural Processing Letters10.1007/s11063-024-11677-y56:4Online publication date: 13-Aug-2024

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  1. Instance Similarity-based Adversarial Domain Adaptation Network for Hyperspectral Image Classification

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      ICIGP '24: Proceedings of the 2024 7th International Conference on Image and Graphics Processing
      January 2024
      480 pages
      ISBN:9798400716720
      DOI:10.1145/3647649
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      Published: 03 May 2024

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      Author Tags

      1. Hyperspectral images
      2. adversarial learning
      3. contrastive learning
      4. domain adaptation

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      • (2024)Kernel Extreme Learning Machine with Discriminative Transfer Feature and Instance Selection for Unsupervised Domain AdaptationNeural Processing Letters10.1007/s11063-024-11677-y56:4Online publication date: 13-Aug-2024

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