A novel dataset for transmission tower recognition in high-resolution remote sensing imagery

Xin Chi; Yu Sun; Donghua Lu; Jun Yang; Da Guo; Yiting Zhang

doi:10.1117/12.3059146

2 January 2025 A novel dataset for transmission tower recognition in high-resolution remote sensing imagery

Xin Chi, Yu Sun, Donghua Lu, Jun Yang, Da Guo, Yiting Zhang

Author Affiliations +

Proceedings Volume 13514, International Conference on Remote Sensing and Digital Earth (RSDE 2024); 135140G (2025) https://doi.org/10.1117/12.3059146
Event: 2024 International Conference on Remote Sensing and Digital Earth, 2024, Chengdu, China

Abstract

The integration of remote sensing technology and deep learning has significantly advanced object recognition in high-resolution satellite imagery. However, most existing datasets mainly focus on stumpy objects, while slender targets, characterized by a height that significantly exceeds their length or width, have been overlooked. This gap highlights the need for specialized datasets to improve recognition of slender objects. In response, this study developed the Transmission Tower Oriented Bounding Box (TT-OBB) dataset, which systematically classifies transmission towers as representative slender targets into four distinct categories. To validate the effectiveness of deep learning methods in recognizing these structures, comprehensive experiments were conducted using several mainstream detectors on the TT-OBB dataset. The results demonstrate the practicality of this dataset, highlighting its utility as a reference for deep learning applications in transmission tower identification, and providing valuable insights for model selection in real world scenarios

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xin Chi, Yu Sun, Donghua Lu, Jun Yang, Da Guo, and Yiting Zhang "A novel dataset for transmission tower recognition in high-resolution remote sensing imagery", Proc. SPIE 13514, International Conference on Remote Sensing and Digital Earth (RSDE 2024), 135140G (2 January 2025); https://doi.org/10.1117/12.3059146

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