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Cross-Layer Refinement and Frequency-aware Fusion Network for Multi-Source Remote Sensing Image Classification — arXiv:2611.03078

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03078
License (as archived): CC0-1.0
Archived: 2026-11-03T11:43:46.781Z
SHA-256 of the archived text: 377d9bda4c2872e7c4d478d9a137cf4ca10df8b4452929ef5cf1652477f241f8
Stable URL of this page: https://metraipolis.com/documento/dc_f445a0ec6529

How to cite

Cross-Layer Refinement and Frequency-aware Fusion Network for Multi-Source Remote Sensing Image Classification — arXiv:2611.03078 — arXiv (en), https://arxiv.org/abs/2611.03078 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f445a0ec6529 (Metraipolis archive dc_f445a0ec6529, sha256 377d9bda4c2872e7c4d478d9a137cf4ca10df8b4452929ef5cf1652477f241f8)

BibTeX

@misc{dcf445a0ec6529,
  title = {Cross-Layer Refinement and Frequency-aware Fusion Network for Multi-Source Remote Sensing Image Classification — arXiv:2611.03078},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03078},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f445a0ec6529; sha256 377d9bda4c2872e7c4d478d9a137cf4ca10df8b4452929ef5cf1652477f241f8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03078. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multi-source remote sensing image classification benefits from complementary spectral and structural information. However, identifying discriminative cross-modal features remains challenging. This letter proposes a Cross-Layer Refinement and Frequency-aware Fusion Network (CRFNet) within a lightweight dual-branch architecture. Specifically, a Cross-Layer Refinement Attention (CLRA) module propagates selected attention relationships across successive layers and employs top relation selection to progressively strengthen discriminative spatial dependencies while suppressing irrelevant responses. Furthermore, a Frequency-aware Complementary Fusion (FCF) module combines frequency-wise interactions with cross-modal channel attention in the Fourier domain. The resulting representations are then converted into spatial-domain gates to selectively modulate complementary features. Experiments on the Houston 2013 and Augsburg datasets demonstrate that the proposed CRFNet consistently outperforms several state-of-the-art methods. The codes are publicly available at this https URL

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.03078 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T11:06:37.697Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).