中文

Journal of Applied Sciences >

2025 , Vol. 43 >Issue 4: 656 - 671

DOI: https://doi.org/10.3969/j.issn.0255-8297.2025.04.008

Computer Science and Applications

Scene-Level Building Change Detection Based on Dense Connection and Multiple Instance

  • SHAO Zilong ,
  • QI Lin ,
  • CHEN Kun ,
  • XU Yubin ,
  • QIN Kun ,
  • YU Changhui
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  • 1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, Hubei, China;
    2. China Academy of Civil Aviation Science and Technology, Beijing 100028, China

Received date: 2024-06-27

  Online published: 2025-07-31

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Abstract

This paper proposes a lightweight scene-level change detection network MIDF-Net, designed to address the issues of high background noise impact and low detection efficiency in large-scale building change detection within complex airport clearance zones. MIDF-Net consists of three parts: a dense connection feature extractor, a differential feature extractor, and a multi-instance classifier. The dense connection feature extractor uses a siamese dense connection network to extract dual temporal image features, while the difference feature extractor focuses on generating variation features by combining dual temporal image features. The multi-instance classifier obtains scene classification results from key local semantic features. Using image data from airport clearance zones across seven different cities, this study constructs a building change detection dataset. Experimental results show that MIDF-Net achieves high detection performance on this dataset. Furthermore, ablation experiments verify the efficacy of each module in MIDF-Net.

Key words： airport clearance zones; change detection; deep learning; dense connection; differential feature extraction; multiple instance learning; multiple instance and differential feature net (MIDF-Net)

Cite this article

SHAO Zilong , QI Lin , CHEN Kun , XU Yubin , QIN Kun , YU Changhui . Scene-Level Building Change Detection Based on Dense Connection and Multiple Instance[J]. Journal of Applied Sciences, 2025 , 43(4) : 656 -671 . DOI: 10.3969/j.issn.0255-8297.2025.04.008

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