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Adversarial pair-wise distribution matching for remote sensing image cross-scene classification.

Sihan Zhu1, Chen Wu1, Bo Du2

  • 1The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China.

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|March 20, 2024
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Summary

This study introduces Adversarial Pair-wise Distribution Matching (APDM), a novel framework for remote sensing cross-scene classification. APDM effectively transfers knowledge across different scenes by addressing background complexity and ensuring prediction diversity.

Keywords:
Cross-scene classificationDeep learningDomain adaptationNuclear-norm

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Machine Learning

Background:

  • Current remote sensing cross-scene classification methods often fail due to direct feature alignment, ignoring image complexity and category inconsistency.
  • Adversarial training paradigms can hinder performance by lacking prediction discriminability and diversity.

Purpose of the Study:

  • To propose a novel framework, Adversarial Pair-wise Distribution Matching (APDM), for improved remote sensing cross-scene classification.
  • To enable effective cross-domain modeling and avoid irrelevant knowledge transfer in complex remote sensing data.

Main Methods:

  • Developed a discriminator-free adversarial paradigm called Adversarial Pair-wise Distribution Matching (APDM).
  • Introduced pair-wise cosine discrepancy for inter-domain and intra-domain prediction measurements to suppress negative features and align distributions.
  • Utilized nuclear-norm maximization and minimization to enhance target prediction and source knowledge applicability.

Main Results:

  • APDM effectively suppresses irrelevant semantic features and implicitly aligns cross-scene distributions.
  • The framework enhances target prediction quality and increases the applicability of source knowledge.
  • Experimental results demonstrate APDM's competitive and effective performance on cross-scene classification tasks.

Conclusions:

  • APDM offers a robust solution for remote sensing cross-scene classification challenges.
  • The proposed pair-wise distribution matching approach improves knowledge transfer and classification accuracy.
  • APDM can be integrated with existing methods to enhance their performance.