密集多实例建筑物场景变化检测

doi:10.3969/j.issn.0255-8297.2025.04.008

摘要/Abstract

摘要： 针对大范围复杂机场净空区建筑物在进行变化检测时存在受背景噪声影响较大以及检测效率低等问题，设计了一种多实例差异特征网络（multiple instance and differential feature net,MIDF-Net），用以检测轻量级的场景变化。MIDF-Net由密集连接特征提取器、差异特征提取器和多实例分类器3部分构成。密集连接特征提取器使用孪生密集连接网络提取双时相的影像特征，差异特征提取器结合双时相影像特征聚焦于变化差异特征生成，多实例分类器从关键局部语义特征中获得场景分类结果。本文利用7个不同城市的机场净空区影像数据制作了一个建筑物变化检测数据集，在此基础上将MIDF-Net应用于机场净空区建筑物变化检测实验，结果表明了所提网络模型的有效性。同时，通过消融实验验证了MIDF-Net各模块的有效性。

关键词: 机场净空区, 变化检测, 深度学习, 密集连接, 差异特征提取, 多实例学习, 多实例差异特征网络

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)

中图分类号:

P237

邵子龙, 漆林, 陈昆, 许玉斌, 秦昆, 余长慧. 密集多实例建筑物场景变化检测[J]. 应用科学学报, 2025, 43(4): 656-671.

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.

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