Abstract

Robust building extraction is crucial for 3D building models in Unmanned Aerial Vehicle (UAV) images. Automatic building extraction is in extremely high demand due to the productivity gain. Conventional building detection methods were time-consuming, costly, and highly complex for human experts. Several DL techniques were developed for building detection. But the accuracy was not enhanced. To solve this issue, the Weighted Hermitian Wavelet Multilayer Extreme Learning Machine (WHWMELM) technique is proposed with multiple layers for building detection in UAV images. The aim of the proposed method is to accurately detect building objects with maximum accuracy and minimal time. First, the input layer obtains UAV aerial input images, and the Weighted Myriad Filtering model is utilized for eliminating noise in the first hidden layer. The Hermitian multi-wavelet transform uses the next hidden layer to extract color, texture, and shape features. Later, the Schutz Feature Matching Coefficient classifies UAV images into buildings and non-buildings with higher accuracy and less time. The methodology has demonstrated promising results on a demanding benchmark dataset, significantly reducing time.

Keywords

Unmanned Aerial Vehicle, UAV Aerial Images, Building Detection, Multilayer Extreme Learning Machine,

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References

  1. H. Wang, Z. Yao, T. Li, Z. Ying, X. Wu, S. Hao, M. Liu, Z. Wang, T. Gu, Enhanced open biomass burning detection: The BranTNet approach using UAV aerial imagery and deep learning for environmental protection and health preservation. Ecological Indicators, 154, (2023) 110788. https://doi.org/10.1016/j.ecolind.2023.110788
  2. C. Zheng, B. Peng, B. Chen, M. Liu, W. Yu, Y. He, D. Ren, Multiscale fusion network for rural newly constructed building detection in unmanned aerial vehicle imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, (2022) 9160-9173. https://doi.org/10.1109/JSTARS.2022.3209682
  3. J. Liu, H. Huang, H. Sun, Z. Wu, R. Luo, LRAD-Net: An improved lightweight network for building extraction from remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, (2022) 675-687. https://doi.org/10.1109/JSTARS.2022.3229460
  4. R.J. Batista, d. Silva, R. Rodrigues, Dias, J. Dantas, Escarpinati, M. Cunha, Backes, A. Ricardo, Automated detection of sugarcane crop lines from UAV images using deep learning. Information Processing in Agriculture, 11(3), (2024) 385-396. https://doi.org/10.1016/j.inpa.2023.04.001
  5. L.M.A. dos Santos, V.A.G. Zanoni, E. Bedin, H. Pistori, Deep learning applied to equipment detection on flat roofs in images captured by UAV. Case Studies in Construction Materials, 18, (2023) e01917. https://doi.org/10.1016/j.cscm.2023.e01917
  6. P. Ong, K.S. Teo, C.K. Sia, UAV-based weed detection in Chinese cabbage using deep learning. Smart Agricultural Technology, 4, (2023) 100181. https://doi.org/10.1016/j.atech.2023.100181
  7. F. Moradi, F.D. Javan, F. Samadzadegan, Potential evaluation of visible-thermal UAV image fusion for individual tree detection based on convolutional neural network. International Journal of Applied Earth Observation and Geoinformation, 113, (2022) 103011. https://doi.org/10.1016/j.jag.2022.103011
  8. C. Jiang, H. Ren, X. Ye, J. Zhu, H. Zeng, Y. Nan, M. Sun, X. Ren, H. Huo, Object detection from UAV thermal infrared images and videos using YOLO models. International Journal of Applied Earth Observation and Geoinformation, 112, (2022) 102912. https://doi.org/10.1016/j.jag.2022.102912
  9. S. Liu, X. Jin, Y. Bai, W. Wu, N. Cui, M. Cheng, Yadong Liu, L. Meng, X. Jia, C. Nie, D. Yin, UAV multispectral images for accurate estimation of the maize LAI considering the effect of soil background. International Journal of Applied Earth Observation and Geoinformation, 121, (2023) 103383. https://doi.org/10.1016/j.jag.2023.103383
  10. P.K. Yadav, J.A. Thomasson, R. Hardin, S.W. Searcy, U. Braga-Neto, S.C. Popescu, D.E. Martin, R. Rodriguez, K. Meza, J. Enciso, J.S. Diaz, T. Wang, Detecting volunteer cotton plants in a corn field with deep learning on UAV remote-sensing imagery. Computers and Electronics in Agriculture, 204, (2023) 107551. https://doi.org/10.1016/j.compag.2022.107551
  11. B. Fu, P. Zuo, M. Liu, G. Lan, H. He, Z. Lao, Y. Zhang, D. Fan, E. Gao, Classifying vegetation communities karst wetland synergistic use of image fusion and object-based machine learning algorithm with Jilin-1 and UAV multispectral images. Ecological Indicators, 140, (2022) 108989. https://doi.org/10.1016/j.ecolind.2022.108989
  12. C.J. Lee, M.D. Yang, H.H. Tseng, Y.C. Hsu, Y. Sung, W.L. Chen, Single-plant broccoli growth monitoring using deep learning with UAV imagery. Computers and Electronics in Agriculture, 207, (2023) 107739. https://doi.org/10.1016/j.compag.2023.107739
  13. Z. Ye, K. Yang, Y. Lin, S. Guo, Y. Sun, X. Chen, R. Lai, H. Zhang, A comparison between Pixel-based deep learning and Object-based image analysis (OBIA) for individual detection of cabbage plants based on UAV Visible-light images. Computers and Electronics in Agriculture, 209, (2023) 107822. https://doi.org/10.1016/j.compag.2023.107822
  14. S.K. Srivastava, K.P. Seng, L.M. Ang, Anibal ‘Nahuel’ A. Pachas, T. Lewis, Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest, Sensors, MDPI, 22(20), (2022) 7872. https://doi.org/10.3390/s22207872
  15. M.A. Ahmed, J. Aloufi, S. Alnatheer, Satin bowerbird optimization with convolutional LSTM for food crop classification on UAV imagery. IEEE Access, 11, (2023) 41075-41083. https://doi.org/10.1109/ACCESS.2023.3269806
  16. C. Kyrkou, T. Theocharides, EmergencyNet: Efficient aerial image classification for drone-based emergency monitoring using atrous convolutional feature fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, (2020) 1687-1699. https://doi.org/10.1109/JSTARS.2020.2969809
  17. M. Liu, H. Deng, W. Dong, Identification of mangrove invasive plant derris trifoliate using UAV images and deep learning algorithms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, (2022) 10017-10026. https://doi.org/10.1109/JSTARS.2022.3223227
  18. M. Ariza-Sentís, H. Baja, S. Vélez, J. Valente, Object detection and tracking on UAV RGB videos for early extraction of grape phenotypic traits. Computers and Electronics in Agriculture, 211, (2023) 108051. https://doi.org/10.1016/j.compag.2023.108051
  19. W. Xing, Z. Cui, J. Qi, HRCTNet: a hybrid network with high-resolution representation for object detection in UAV image. Complex & Intelligent Systems, 9(6), (2023) 6437-6457. https://doi.org/10.1007/s40747-023-01076-6
  20. C. Song., F. Zhang, Y.A.N.G. Chen, Z.H.O.U. Hang, J.X. Zhang, (2023). Detection of maize tassels for UAV remote sensing image with an improved YOLOX model. Journal of Integrative Agriculture, 22(6), 1671-1683. https://doi.org/10.1016/j.jia.2022.09.021
  21. E. Khankeshizadeh, A. Mohammadzadeh, H. Arefi, A. Mohsenifar, S. Pirasteh, E. Fan, H. Li, J. Li, A novel weighted ensemble transferred U-Net based model (WETUM) for postearthquake building damage assessment from UAV data: A comparison of deep learning-and machine learning-based approaches. IEEE Transactions on Geoscience and Remote Sensing, 62, (2024) 1-17. https://doi.org/10.1109/TGRS.2024.3354737
  22. T. Hou, J. Li, Application of mask R-CNN for building detection in UAV remote sensing images. Heliyon, 10(19), (2024). https://doi.org/10.1016/j.heliyon.2024.e38141
  23. Q. Fan, Y. Li, M. Deveci, K. Zhong, S. Kadry, LUD-YOLO: A novel lightweight object detection network for unmanned aerial vehicle. Information Sciences, 686, (2025) 121366.
  24. C. Tan, Chen, T., Liu, J., Deng, X., Wang, H., & Ma, J. (2024). Building extraction from Unmanned Aerial Vehicle (UAV) data in a landslide-affected scattered mountainous area based on res-unet. Sustainability, 16(22), 9791. https://doi.org/10.3390/su16229791
  25. W. Alsabhan, T. Alotaiby, B. Dudin, Detecting Buildings and Nonbuildings from Satellite Images Using U‐Net. Computational Intelligence and Neuroscience, 2022(1), (2022) 4831223. https://doi.org/10.1155/2022/4831223