Development of broad area target search system based on deep learning

Yan Gao; Donghua Lu; Yiting Zhang; Wei Wang

doi:10.1117/12.3024540

21 February 2024 Development of broad area target search system based on deep learning

Yan Gao, Donghua Lu, Yiting Zhang, Wei Wang

Proceedings Volume 12988, Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023); 1298819 (2024) https://doi.org/10.1117/12.3024540
Event: Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023), 2023, Xi’an, China

Abstract

Deep learning is increasingly being applied in the field of remote sensing image processing. However, researchers often face limitations in establishing high-quality datasets for target recognition in high-resolution imagery due to resource constraints. Moreover, traditional target recognition methods cannot leverage the extensive coverage of remote sensing images for broad-scope searches of specific targets. To address the scarcity of high-resolution remote sensing image sources for target detection tasks, achieve broad-scope automatic searching for areas of interest, and enhance the level of task automation, a system with the capability to acquire data from multiple network sources and recognize nuclear power plant targets was developed. The system initially integrates data from multiple network map servers, enabling the rapid and cost-effective acquisition of high-resolution, large-area remote sensing imagery. Deep learning samples are created based on multi-source image data, and multiple target detection models are trained. The software's user interface was developed and tested, facilitating broad-scope searches for key components of nuclear power stations based on extensive network imagery.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yan Gao, Donghua Lu, Yiting Zhang, and Wei Wang "Development of broad area target search system based on deep learning", Proc. SPIE 12988, Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023), 1298819 (21 February 2024); https://doi.org/10.1117/12.3024540

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KEYWORDS

Object detection

Data modeling

Remote sensing

Target detection

Deep learning

Target recognition

Data acquisition

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