23.1.7.1 Transmission Towers, Pylons, Poles, Extraction, Radar, SAR, Lidar, Laser, Depth

Chapter Contents (Back)
Aerial Image Analysis. SAR. Radar. Towers. Pylons.
See also Power Line Extraction, Powerline Extraction, Radar, SAR, Lidar, Laser, Depth.

Li, Q.Q.[Qing-Quan], Chen, Z.P.[Zhi-Peng], Hu, Q.W.[Qing-Wu],
A Model-Driven Approach for 3D Modeling of Pylon from Airborne LiDAR Data,
RS(7), No. 9, 2015, pp. 11501.
DOI Link 1511
BibRef

Conde, B.[Borja], Villarino, A.[Alberto], Cabaleiro, M.[Manuel], Gonzalez-Aguilera, D.[Diego],
Geometrical Issues on the Structural Analysis of Transmission Electricity Towers Thanks to Laser Scanning Technology and Finite Element Method,
RS(7), No. 9, 2015, pp. 11551.
DOI Link 1511
BibRef

Guo, B.[Bo], Huang, X.F.[Xian-Feng], Li, Q.Q.[Qing-Quan], Zhang, F.[Fan], Zhu, J.S.[Jia-Song], Wang, C.S.[Chi-Sheng],
A Stochastic Geometry Method for Pylon Reconstruction from Airborne LiDAR Data,
RS(8), No. 3, 2016, pp. 243.
DOI Link 1604
BibRef

Zhou, R.[Ruqin], Jiang, W.[Wanshou], Huang, W.[Wei], Xu, B.[Bo], Jiang, S.[San],
A Heuristic Method for Power Pylon Reconstruction from Airborne LiDAR Data,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Cerón, A.[Alexander], Mondragón, I.[Iván], Prieto, F.[Flavio],
Real-time transmission tower detection from video based on a feature descriptor,
IET-CV(11), No. 1, February 2017, pp. 33-42.
DOI Link 1703
BibRef

Shi, Z.W.[Zhen-Wei], Kang, Z.Z.[Zhi-Zhong], Lin, Y.[Yi], Liu, Y.[Yu], Chen, W.[Wei],
Automatic Recognition of Pole-Like Objects from Mobile Laser Scanning Point Clouds,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Liu, L.[Lianguang], Du, R.[Rujun], Liu, W.[Wenlin],
Flood Distance Algorithms and Fault Hidden Danger Recognition for Transmission Line Towers Based on SAR Images,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Chen, S.C.[Shi-Chao], Wang, C.[Cheng], Dai, H.Y.[Hua-Yang], Zhang, H.B.[He-Bing], Pan, F.F.[Fei-Fei], Xi, X.H.[Xiao-Huan], Yan, Y.G.[Yue-Guan], Wang, P.[Pu], Yang, X.B.[Xue-Bo], Zhu, X.X.[Xiao-Xiao], Aben, A.[Ardana],
Power Pylon Reconstruction Based on Abstract Template Structures Using Airborne LiDAR Data,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Awrangjeb, M.[Mohammad],
Extraction of Power Line Pylons and Wires Using Airborne LiDAR Data at Different Height Levels,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Munir, N.[Nosheen], Awrangjeb, M.[Mohammad], Stantic, B.[Bela],
Extraction of Forest Power Lines from LiDAR Point Cloud Data,
DICTA21(01-06)
IEEE DOI 2201
Image segmentation, Solid modeling, Shape, Poles and towers, Vegetation mapping, Forestry, power lines, pylons, vegetation, span, bundles
See also Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data. BibRef

Awrangjeb, M., Jonas, D., Zhou, J.,
An Automatic Technique for Power Line Pylon Detection from Point Cloud Data,
DICTA17(1-8)
IEEE DOI 1804
feature extraction, object detection, trees (mathematics), candidate pylons, connected component analysis, Wires BibRef

Knyaz, V.A.[Vladimir A.], Kniaz, V.V.[Vladimir V.], Remondino, F.[Fabio], Zheltov, S.Y.[Sergey Y.], Gruen, A.[Armin],
3D Reconstruction of a Complex Grid Structure Combining UAS Images and Deep Learning,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
radio or television towers. BibRef

Kniaz, V.V.[Vladimir V.], Zheltov, S.Y.[Sergey Y.], Remondino, F.[Fabio], Knyaz, V.A.[Vladimir A.], Bordodymov, A., Gruen, A.[Armin],
Wire Structure Image-based 3d Reconstruction Aided By Deep Learning,
ISPRS20(B2:435-441).
DOI Link 2012
BibRef

Qiao, S.J.[Si-Jia], Sun, Y.[Yu], Zhang, H.P.[Hao-Peng],
Deep Learning Based Electric Pylon Detection in Remote Sensing Images,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

McCulloch, J.[Josh], Green, R.[Richard],
Conductor Reconstruction for Dynamic Line Rating Using Vehicle-Mounted LiDAR,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef
Earlier:
Utility pole extraction using vehicle-mounted LIDAR for dynamic line rating,
IVCNZ17(1-5)
IEEE DOI 1902
BibRef
And:
Density Based Recovery of Urban Power Lines Using Vehicle-Mounted LiDAR,
IVCNZ18(1-5)
IEEE DOI 1902
BibRef
Earlier:
Extraction of utility poles in LIDAR scans using cross-sectional slices,
ICVNZ16(1-4)
IEEE DOI 1701
Conductors, Meters, Laser radar, Clustering algorithms, Poles and towers, Urban areas, lidar. optical radar, principal component analysis, radar clutter, road vehicle radar, utility pole extraction, dynamic line rating. Clustering algorithms BibRef

Tarighat, F.[Fereshteh], Foroughnia, F.[Fatemeh], Perissin, D.[Daniele],
Monitoring of Power Towers' Movement Using Persistent Scatterer SAR Interferometry in South West of Tehran,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Arastounia, M.[Mostafa], Lichti, D.D.[Derek D.],
Simultaneous identification, modeling and registration refinement of poles using laser scanning point clouds,
PandRS(181), 2021, pp. 327-344.
Elsevier DOI 2110
Point cloud, Pole extraction, As-built modeling, Mapping, And laser scanning BibRef

Lu, Z.[Zhumao], Gong, H.[Hao], Jin, Q.[Qiuheng], Hu, Q.W.[Qing-Wu], Wang, S.H.[Shao-Hua],
A Transmission Tower Tilt State Assessment Approach Based on Dense Point Cloud from UAV-Based LiDAR,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Li, J.N.[Jia-Nan], Li, Y.[Yu], Jiang, H.N.[Hao-Nan], Zhao, Q.H.[Quan-Hua],
Hierarchical Transmission Tower Detection from High-Resolution SAR Image,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Wang, J.[Jingru], Wang, C.[Cheng], Xi, X.H.[Xiao-Huan], Wang, P.[Pu], Du, M.[Meng], Nie, S.[Sheng],
Location and Extraction of Telegraph Poles from Image Matching-Based Point Clouds,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Huang, Z.Y.[Zhao-Yang], Wang, F.[Feng], You, H.J.[Hong-Jian], Hu, Y.X.[Yu-Xin],
Imaging Parameters-Considered Slender Target Detection in Optical Satellite Images,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Liu, C.[Chun], Yang, J.[Jian], Ou, J.[Jianghong], Fan, D.[Dahua],
Offshore Oil Platform Detection in Polarimetric SAR Images Using Level Set Segmentation of Limited Initial Region and Convolutional Neural Network,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

da Silva, F.G.[Fabiano G.], Ramos, L.P.[Lucas P.], Palm, B.G.[Bruna G.], Machado, R.[Renato],
Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef


Munir, N.[Nosheen], Awrangjeb, M.[Mohammad], Stantic, B.[Bela],
An improved method for pylon extraction and vegetation encroachment analysis in high voltage transmission lines using LiDAR data,
DICTA20(1-8)
IEEE DOI 2201
Poles and towers, Urban areas, Vegetation mapping, Vegetation, High-voltage techniques, Forestry, Monitoring, Power lines, pylons, power line corridor BibRef

Abdelfattah, R.[Rabab], Wang, X.F.[Xiao-Feng], Wang, S.[Song],
Ttpla: An Aerial-image Dataset for Detection and Segmentation of Transmission Towers and Power Lines,
ACCV20(VI:601-618).
Springer DOI 2103
BibRef

Yang, Z.[Zhi], Zhao, B.B.[Bin-Bin], Ma, X.[Xiao], Luo, M.[Meng], Han, J.J.[Jia-Jia], Si, W.G.[Wei-Guo],
Super Resolution Enhancement of Satellite Remote Sensing Images of Transmission Tower Based on Multi-map Residual Network and Wavelet Transform,
CVIDL20(16-20)
IEEE DOI 2102
convolutional neural nets, edge detection, feature extraction, image enhancement, image resolution, image sampling, edge enhancement BibRef

Deidda, M., Pala, A., Sanna, G.,
Modelling A Cell Tower Using SFM: Automated Detection of Structural Elements From Skeleton Extraction on A Point Cloud,
ISPRS20(B2:399-406).
DOI Link 2012
BibRef

Wu, Z., Wang, H., Yu, W., Xi, J., Lei, W., Tang, T.,
3d High-efficiency and High-precision Model-driven Modelling for Power Transmission Tower,
SmartCityApp20(421-426).
DOI Link 2012
BibRef

Liu, L., Zhang, T., Zhao, K., Wiliem, A., Astin-Walmsley, K., Lovell, B.,
Deep Inspection: An Electrical Distribution Pole Parts Study VIA Deep Neural Networks,
ICIP19(4170-4174)
IEEE DOI 1910
electrical distribution pole, integrated inspection system, deep neural networks, object detection, imbalanced data classification BibRef

Maeda, K., Takahashi, S., Ogawa, T., Haseyama, M.,
Automatic estimation of deterioration level on transmission towers via deep extreme learning machine based on local receptive field,
ICIP17(2379-2383)
IEEE DOI 1803
Estimation, Feature extraction, Inspection, Machine learning, Poles and towers, Training, Visualization, transmission tower BibRef

Cabello, F.C.[Frank C.], Iano, Y.[Yuzo], Arthur, R.[Rangel], Dueñas, A.[Abel], León, J.[Julio], Caetano, D.G.[Diogo G.],
Automatic Detection of Utility Poles Using the Bag of Visual Words Method for Different Feature Extractors,
CAIP17(II: 116-126).
Springer DOI 1708
BibRef

Pontecorvo, C.[Carmine], Redding, N.J.[Nicholas J.],
Non-Periodic Translation Symmetry Detection Using Global Self Similarity,
DICTA17(1-8)
IEEE DOI 1804
Multiple poles and shadows in aerial images. correlation methods, image matching, object detection, actual aerial imagery, approximate size, blobs, consistent regions, Tensile stress BibRef

Sharma, H.[Hrishikesh], Vellaiappan, A.[Adithya], Dutta, T.[Tanima], Balamuralidhar, P.,
Image Analysis-Based Automatic Utility Pole Detection for Remote Surveillance,
DICTA15(1-7)
IEEE DOI 1603
BibRef
And: A1, A3, A2, A4:
A Real-Time Framework for Detection of Long Linear Infrastructural Objects in Aerial Imagery,
ICIAR15(71-81).
Springer DOI 1507
BibRef
And: A3, A1, A2, A4:
Image Analysis-Based Automatic Detection of Transmission Towers using Aerial Imagery,
IbPRIA15(641-651).
Springer DOI 1506
autonomous aerial vehicles BibRef

Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Tunnels, Tunnel Descriptions, Tunnel Analysis .


Last update:Aug 14, 2022 at 21:20:19