Pcan—part-based context attention network for thermal power plant detection in remote sensing imagery

Wenxin Yin, Wenhui Diao, Peijin Wang, Xin Gao, Ya Li, Xian Sun

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The detection of Thermal Power Plants (TPPs) is a meaningful task for remote sensing image interpretation. It is a challenging task, because as facility objects TPPs are composed of various distinctive and irregular components. In this paper, we propose a novel end-to-end detection framework for TPPs based on deep convolutional neural networks. Specifically, based on the RetinaNet one-stage detector, a context attention multi-scale feature extraction network is proposed to fuse global spatial attention to strengthen the ability in representing irregular objects. In addition, we design a part-based attention module to adapt to TPPs containing distinctive components. Experiments show that the proposed method outperforms the state-of-the-art methods and can achieve 68.15% mean average precision.
Original languageEnglish (US)
JournalRemote Sensing
Volume13
Issue number7
DOIs
StatePublished - Apr 1 2021
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-21

ASJC Scopus subject areas

  • General Earth and Planetary Sciences

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