Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

doi:10.11947/j.JGGS.2023.0206

Journal of Geodesy and Geoinformation Science ›› 2023, Vol. 6 ›› Issue (2): 51-61.doi: 10.11947/j.JGGS.2023.0206

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Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

Zhanlong CHEN¹^,²^,⁶(), Shuangjiang LI¹^,⁷, Yongyang XU¹^,³^,⁶(), Daozhu XU⁴^,⁵, Chao MA⁴^,⁵, Junli ZHAO²

1. School of Geography and Information Engineering, China University of Geoscience, Wuhan 430078, China
2. Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China
3. Key Laboratory of Urban Land Resource Monitoring and Simulation, Ministry of Natural Resource, Shenzhen 518034, China
4. Xi’an Research Institute of Surveying and Mapping, Xi’an 710054, China
5. State Key Laboratory of Geo-Information Engineering, Xi’an 710054, China
6. School of Computer Science, China University of Geoscience, Wuhan 430078, China
7. Central Southern China Electric Power Design Institute Co., Ltd., Wuhan 430071, China

Received:2023-02-25 Accepted:2023-05-30 Online:2023-06-20 Published:2023-07-10
Contact: Yongyang XU. E-mail: yongyangxu@cug.edu.cn
About author:Zhanlong CHEN (1980—), male, PhD, professor, majors in spatial analysis algorithms, spatial reasoning, geographic information system software and application development.E-mail: chenzhanlong2005@126.com
Supported by:
National Natural Science Foundation of China(41871305);National Key Research and Development Program of China(2017YFC0602204);Fundamental Research Funds for the Central Universities, China University of Geosciences(Wuhan)(CUGQY1945);Open Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education and the Fundamental Research Funds for the Central Universities(GLAB2019ZR02);Open Fund of Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, China(KF-2020-05-068)

Abstract

Abstract:

The exploration of building detection plays an important role in urban planning, smart city and military. Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images, we present an effective YOLOv3 framework, corner regression-based YOLOv3 (Correg-YOLOv3), to localize dense building accurately. This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box. By extending output dimensions, the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile. Finally, we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively. The experimental results achieve high performance in precision (96.45%), recall rate (95.75%), F1 score (96.10%) and average precision (98.05%), which were 2.73%, 5.4%, 4.1% and 4.73% higher than that of YOLOv3. Therefore, our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.

Key words: high resolution remote sensing image; Correg-YOLOv3; corner regression; dense buildings; object detection

Zhanlong CHEN, Shuangjiang LI, Yongyang XU, Daozhu XU, Chao MA, Junli ZHAO. Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images[J]. Journal of Geodesy and Geoinformation Science, 2023, 6(2): 51-61.

Figures/Tables 11

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References 34

[1]	CHENG Gong, HAN Junwei. A survey on object detection in optical remote sensing images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 117: 11-28. doi: 10.1016/j.isprsjprs.2016.03.014
[2]	DENG Ruizhe, CHEN Qihao, CHEN Qi, et al. A deformable feature pyramid network for ship detection from remote sensing images[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(6):787-797. DOI: 10.11947/j.AGCS.2020.20190117. doi: 10.11947/j.AGCS.2020.20190117
[3]	ZHANG Tao, DING Lele, SHI Furong. Urban villages extraction from high-resolution remote sensing imagery based on landscape semantic metrics[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(1): 97-104. DOI: 10.11947/j.AGCS.2021.20190463. doi: 10.11947/j.AGCS.2021.20190463
[4]	ZHANG Yong. Application of remote sensing and GIS in monitoring and management of illegal construction Xiamen City[J]. Geomatics & Spatial Information Technology, 2016, 39(2): 75-77.
[5]	SUN Changkui, LIU Shanlei, WANG Shengyao, et al. Application of UAV in construction of smart city[J]. Remote Sensing for Land & Resources, 2018, 30(4): 8-12.
[6]	LIU Yang, FU Zhengye, ZHENG Fengbin. Review on high-resolution remote sensing image classification and recognition[J]. Journal of Geo-Information Science, 2015, 17(9): 1080-1091.
[7]	LIN Jingbo, JING Weipeng, SONG Houbing, et al. ESFNet: efficient network for building extraction from high-resolution aerial images[J]. IEEE Access, 2019(7): 54285-54294.
[8]	ZOU Weitao, JING Weipeng, CHEN Guangsheng, et al. A survey of big data analytics for smart forestry[J]. IEEE Access, 2019, 7: 46621-46636. doi: 10.1109/ACCESS.2019.2907999
[9]	FENG Liying. Research on construction land information extraction from high resolution images with deep learning technology[D]. Hangzhou: Zhejiang University, 2017.
[10]	MA Changhui, HUANG Dengshan. Application of texture features and geometric feature information in high spatial resolution remote sensing image classification[J]. Journal of Geomatics, 2019, 44(6): 66-70, 92.
[11]	HU Lei, ZHENG Jin, GAO Feng. A building extraction method using shadow in high-resolution multispectral images[C]// Proceedings of 2011 IEEE International Geoscience and Remote Sensing Symposium. Las Vegas, NV, USA: IEEE, 2011: 1862-1865.
[12]	SHI Wenzao, MAO Zhengyuan. Building extraction from high-resolution remotely sensed imagery based on shadows and graph-cut segmentation[J]. Acta ElectronicaSinica, 2016, 44(12): 2849-2854.
[13]	KONSTANTINIDIS D, STATHAKI T, ARGYRIOU V, et al. Building detection using enhanced HOG-LBP features and region refinement processes[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 10(3): 888-905. doi: 10.1109/JSTARS.4609443
[14]	LÜ Fenghua, SHU Ning, GONG Cun, et al. Regular building extraction from high-resolution image based on multilevel-features[J]. Geomatics and Information Science of Wuhan University, 2017, 42(5): 656-660.
[15]	CHAUDHURI D, KUSHWAHA N K, SAMAL A, et al. Automatic building detection from high-resolution satellite images based on morphology and internal gray variance[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 9(5): 1767-1779. doi: 10.1109/JSTARS.2015.2425655
[16]	AWRANGJEB M, ZHANG Chunsun, FRASER C S. Improved building detection using texture information[J]. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2011, 38: 143-148.
[17]	ZHAO Chuan, ZHANG Baoming, CHEN Xiaowei, et al. A method of extracting building based on LiDAR point clouds[J]. Bulletin of Surveying and Mapping, 2017(2): 35-39. doi: 10.13474/j.cnki.11-2246.2017.0044
[18]	GUO Haonan, SHI Qian, DU Bo, et al. Scene-driven multitask parallel attention network for building extraction in high-resolution remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(5): 4287-4306. doi: 10.1109/TGRS.2020.3014312
[19]	LI Zhenshi, ZHANG Xueliang, XIAO Pengfeng, et al. On the effectiveness of weakly supervised semantic segmentation for building extraction from high-resolution remote sensing imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 3266-3281. doi: 10.1109/JSTARS.2021.3063788
[20]	HU Shu, WANG Shugen, WANG Yue, et al. Building object detection in high-resolution remote sensing image based on mask R-CNN[J/OL]. Journal of Geomatics. [2021-04-01]. https://doi.org/10.14188/j.2095-6045.2020416.
[21]	GIRSHICK R. Fast R-CNN[C]// Proceedings of 2015 IEEE International Conference on Computer Vision. New York, USA: IEEE, 2015: 1440-1448.
[22]	REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 39(6): 1137-1149. doi: 10.1109/TPAMI.2016.2577031
[23]	HE Kaiming, GKIOXARI G, DOLLAR P, et al. Mask r-cnn[C]// Proceedings of 2017 IEEE International Conference on Computer Vision. New York, USA: IEEE, 2017: 2961-2969.
[24]	SHI Wenxu, BAO Jiahui, YAO Yu. Remote sensing image target detection and identification based on deep learning[J]. Journal of Computer Applications, 2020, 40(12):3558-3562. doi: 10.11772/j.issn.1001-9081.2020040579
[25]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2016: 779-788.
[26]	LIU Wei, ANGUELOY D, ERHAN D, et al. Ssd: single shot multibox detector[C]// Proceedings of 2016 European Conference on Computer Vision. Cham, Switzerland: Springer, 2016: 21-37.
[27]	DONG Biao, XIONG Fengguang, HAN Xie, et al. Research on remote sensing building detection based on improved Yolo-v3 algorithm[J]. Computer Engineering and Applications, 2020, 56(18):209-213. doi: 10.3778/j.issn.1002-8331.2004-0410
[28]	LI Xiang, SU Juan, YANG Long. A SAR image building detection algorithm based on improved YOLOv3[J]. Acta Armamentarii, 2020, 41(7):1347-1359. doi: 10.3969/j.issn.1000-1093.2020.07.012
[29]	MA Haojie, LIU Yalan, REN Yuhuan, et al. Detection of collapsed buildings in post-earthquake remote sensing images based on the improved YOLOv3[J]. Remote Sensing, 2020, 12(1): 44. doi: 10.3390/rs12010044
[30]	LI Qingpeng, WANG Yunhong, LIU Qingjie, et al. Hough transform guided deep feature extraction for dense building detection in remote sensing images[C]// Proceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New York, USA: IEEE, 2018: 1872-1876.
[31]	JIANG Kaiyu, LI Qingpeng. TQR-Net: Tighter quadrangle-based convolutional neural network for dense building instance localization in remote sensing imagery[C]// Proceedings of 2019 International Conference on Image and Graphics. Cham, Switzerland: Springer, 2019: 281-291.
[32]	WANG Kun, LIU Maozhen, YE Zhaojun. An advanced YOLOv3 method for small-scale road object detection[J]. Applied Soft Computing, 2021: 107846.
[33]	IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]// Proceedings of 2015 International conference on machine learning. Calgary Canada: PMLR, 2015: 448-456.
[34]	HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]// Proceedings of 2016 IEEE conference on computer vision and pattern recognition. Las Vegas, NV, USA: IEEE, 2016: 770-778.

	p	r	F1	AP
YOLOv3	93.72	90.35	92.00	93.32
YOLOv3+K-means	94.07	90.80	92.41	93.63
Correg-YOLOv3	94.68	95.53	95.10	97.63
Correg-YOLOv3+ K-means	96.45	95.75	96.10	98.05

Detection method	p	r	F1	AP
Faster R-CNN	57.25	87.53	69.22	81.86
SSD	91.42	87.98	89.67	93.84
YOLOv3	93.72	90.35	92.00	93.32
Correg-YOLOv3	96.45	95.75	96.10	98.05

IoU	p	r	F1	AP
0.6	96.2	95.76	96.00	98.11
0.65	95.52	95.76	95.64	98.01
0.7	89.69	95.78	92.64	97.52
0.75	64.54	95.80	77.12	95.83
0.8	41.97	95.78	58.36	91.66

Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

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