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.

[1] Huang X, Zhang L P. Morphological building/shadow index for building extraction from high-resolution imagery over urban areas [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(1): 161-172.
[2] Jin X Y, Davis C H. Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information [J]. EURASIP Journal on Applied Signal Processing, 2005, 14(8): 2196-2206.
[3] Wang J, Yang X C, Qin X B, et al. An efficient approach for automatic rectangular building extraction from very high resolution optical satellite imagery [J]. IEEE Geoscience & Remote Sensing Letters, 2014, 12(3): 487-491.
[4] Mnih V. Machine learning for aerial image labeling [D]. Toronto, Canada: University of Toronto, 2013.
[5] 李成范, 孟令奎, 刘学锋. 基于深度学习的高分遥感图像建筑物识别[J]. 应用科学学报, 2024, 42(3): 375-387. Li C F, Meng L K, Liu X F. Building recognition of high-resolution remote sensing images based on deep learning [J]. Journal of Applied Sciences, 2024, 42(3): 375-387. (in Chinese)
[6] 黄显培, 孟庆祥. 基于多特征卷积神经网络的哨兵二号影像地物分类[J]. 应用科学学报, 2023, 41(5): 766-776. Huang X P, Meng Q X. Land cover classification of sentinel-2 image based on multi-feature convolution neural network [J]. Journal of Applied Sciences, 2023, 41(5): 766-776. (in Chinese)
[7] Huang Z M, Cheng G L, Wang H Z, et al. Building extraction from multi-source remote sensing images via deep deconvolution neural networks [C]//IEEE International Geoscience and Remote Sensing Symposium, 2016: 1835-1838.
[8] Chen K Q, Fu K, Gao X, et al. Building extraction from remote sensing images with deep learning in a supervised manner [C]//IEEE International Geoscience and Remote Sensing Symposium, 2017: 1672-1675.
[9] Liu P H, Liu X P, Liu M X, et al. Building footprint extraction from high-resolution images via spatial residual inception convolutional neural network [J]. Remote Sensing, 2019, 11(10): 1-19.
[10] 张祖勋, 姜慧伟, 庞世燕, 等. 多时相遥感影像的变化检测研究现状与展望[J]. 测绘学报, 2022, 51(7): 1091-1107. Zhang Z X, Jiang H W, Pang S Y, et al. Review and prospect in change detection of multitemporal remote sensing images [J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7): 1091-1107. (in Chinese)
[11] 张良培, 武辰. 多时相遥感变化检测的现状与展望[J]. 测绘学报, 2017, 46(10): 1447-1459. Zhang L P, Wu C. Advance and future development of change detection for multi-temporal remote sensing imagery [J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1447-1459. (in Chinese)
[12] 眭海刚, 冯文卿, 李文卓, 等. 多时相遥感影像变化检测方法综述[J]. 武汉大学学报(信息科学版). 2018, 43(12): 1885-1898. Sui H G, Feng W Q, Li W Z, et al. Overview of multi-temporal remote sensing image change detection methods [J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1885-1898. (in Chinese)
[13] Guan H. End-to-end change detection for high resolution satellite images using improved UNet++[J]. Remote Sensing, 2019, 11(11): 1382.
[14] Rodrigo C D, Bertr L S, Alexandre B. Fully convolutional Siamese networks for change detection [C]//IEEE International Conference on Image Processing, 2018: 4063-4067.
[15] Zhang P, Gong M, Su L, et al. Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 116(1): 24-41.
[16] 张涵, 秦昆, 毕奇, 等. 注意力引导的三维卷积网络用于遥感场景变化检测[J]. 应用科学学报, 2021, 39(2): 272-280. Zhang H, Qin K, Bi Q, et al. Attention guided 3D ConvNet for aerial scene change detection [J]. Journal of Applied Sciences, 2021, 39(2): 272-280. (in Chinese)
[17] Fang S, Li K, Shao J, et al. SNUNet-CD: a densely connected siamese network for change detection of VHR images [J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.
[18] Zhi Z A, Yi W A, Zhang Y J, et al. CLNet: cross-layer convolutional neural network for change detection in optical remote sensing imagery [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 175(3): 247-267.
[19] Chen H, Shi Z. A spatial-temporal attention-based method and a new dataset for remote sensing image change detection [J]. Remote Sensing, 2020, 12(10): 1662.
[20] Chen H, Qi Z, Shi Z. Remote sensing image change detection with transformers [J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60(1): 1-14.
[21] Bandara W, Patel V M. A transformer-based siamese network for change detection [C]//IEEE International Geoscience and Remote Sensing Symposium, 2022: 207-210.
[22] 阚亚亚, 张孙杰, 熊娟, 等. 结合transformer多尺度实例交互的稀疏集目标检测[J]. 应用科学学报, 2023, 41(5): 777-788. Kan Y Y, Zhang S J, Xiong J, et al. Sparse set object detection combined with transformer multi-scale instance interaction [J]. Journal of Applied Sciences, 2023, 41(5): 777-788. (in Chinese)
[23] 童矿. 机场净空区建筑物的智能化动态监测与超限预警研究[D]. 天津：中国民航大学, 2022.
[24] 张涵. 基于高分影像的机场净空区新增建筑物检测与识别方法[D]. 武汉：武汉大学, 2021.
[25] Peng D, Bruzzone L, Zhang Y, et al. SemiCDNet: a semisupervised convolutional neural network for change detection in high resolution remote-sensing images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(7): 5891-5906.
[26] Roy M, Ghosh S, Ghosh A. A novel approach for change detection of remotely sensed images using semi-supervised multiple classifier system [J]. Information Sciences, 2014, 269(1): 35-47.
[27] Huang G, Liu Z, Van D M L, et al. Densely connected convolutional networks [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2261-2269.
[28] Dietterich T, Lathrop R, Tomas L. Solving the multiple instance problem with axis-parallel rectangles [J]. Artificial Intelligence, 1997, 89(1-2): 31-71.
[29] Bi Q, Qin K, Li Z L, et al. A multiple-instance densely-connected ConvNet for aerial scene classification [J]. IEEE Transaction on Image Processing, 2020, 29(1): 4911-4926.
[30] Liu X, Jiao L, Zhao J, et al. Deep multiple instance learning-based spatial-spectral classification for PAN and MS imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 56(1): 461-473.
[31] Woo S, Park J, Lee J Y, et al. CBAM: Convolutional block attention module [C]//European Conference on Computer Vision, 2018: 3-19.
[32] Hu J, Shen L, Sun G. Squeeze-and-excitation networks [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.
[33] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks [J]. Communications of the ACM, 2017, 60(6): 84-90.
[34] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [C]//3rd International Conference on Learning Representations, 2015: 1-14.
[35] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1-9.
[36] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
[37] Han X, Zhong Y, Cao L. Pretrained AlexNet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification [J]. Remote Sensing, 2017, 9(8): 848.
[38] Bi Q, Zhang H, Qin K. Multi-scale stacking attention pooling for remote sensing scene classification [J]. Neurocomputing, 2021, 436(12): 147-161.
[39] Zhou B, Yi J, Bi Q. Differential convolution feature guided deep multiscale multiple instance learning for aerial scene classification [C]//IEEE International Conference on Acoustics, Speech and Signal Processing, 2021: 4594-4599.

密集多实例建筑物场景变化检测

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

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