CoF-Net: A Progressive Coarse-to-Fine Framework for Object Detection in Remote-Sensing Imagery

Cong Zhang, Kin Man Lam, Qi Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)


Object detection in remote-sensing images is a crucial task in the fields of Earth observation and computer vision. Despite impressive progress in modern remote-sensing object detectors, there are still three challenges to overcome: 1) complex background interference; 2) dense and cluttered arrangement of instances; and 3) large-scale variations. These challenges lead to two key deficiencies, namely, coarse features and coarse samples, which limit the performance of existing object detectors. To address these issues, in this article, a novel coarse-to-fine framework (CoF-Net) is proposed for object detection in remote-sensing imagery. CoF-Net mainly consists of two parallel branches, namely, coarse-to-fine feature adaptation (CoF-FA) and coarse-to-fine sample assignment (CoF-SA), which aim to progressively enhance feature representation and select stronger training samples, respectively. Specifically, CoF-FA smoothly refines the original coarse features into multispectral nonlocal fine features with discriminative spatial-spectral details and semantic relations. Meanwhile, CoF-SA dynamically considers samples from coarse to fine by progressively introducing geometric and classification constraints for sample assignment during training. Comprehensive experiments on three public datasets demonstrate the effectiveness and superiority of the proposed method.

Original languageEnglish
Article number5600617
Pages (from-to)1-17
JournalIEEE Transactions on Geoscience and Remote Sensing
Publication statusPublished - Jan 2023


  • Coarse-to-fine paradigms
  • geometric constraints
  • object detection
  • remote-sensing imagery
  • spatial-spectral nonlocal features

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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