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Remote Sensing
Volume 17
Issue 5
10.3390/rs17050733
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Article

Quantitative Remote Sensing Supporting Deep Learning Target Identification: A Case Study of Wind Turbines

by
Xingfeng Chen
1,
Yunli Zhang
2,
Wu Xue
3,
Shumin Liu
2,
Jiaguo Li
1,*,
Lei Meng
1,
Jian Yang
1,
Xiaofei Mi
1,
Wei Wan
4 and
Qingyan Meng
1
1
The Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
Advanced Copper Industry College, Jiangxi University of Science and Technology, Yingtan 335000, China
3
Space Engineering University, Beijing 101416, China
4
Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(5), 733; https://doi.org/10.3390/rs17050733
Submission received: 26 November 2024 / Revised: 27 January 2025 / Accepted: 17 February 2025 / Published: 20 February 2025
(This article belongs to the Special Issue Target Recognition and Change Detection for High-Resolution Remote Sensing Images)
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Abstract

Small Target Detection and Identification (TDI) methods for Remote Sensing (RS) images are mostly inherited from the deep learning models of the Computer Vision (CV) field. Compared with natural images, RS images not only have common features such as shape and texture but also contain unique quantitative information such as spectral features. Therefore, RS TDI in the CV field, which does not use Quantitative Remote Sensing (QRS) information, has the potential to be explored. With the rapid development of high-resolution RS satellites, RS wind turbine detection has become a key research topic for power intelligent inspection. To test the effectiveness of integrating QRS information with deep learning models, the case of wind turbine TDI from high-resolution satellite images was studied. The YOLOv5 model was selected for research because of its stability and high real-time performance. The following methods for integrating QRS and CV for TDI were proposed: (1) Surface reflectance (SR) images obtained using quantitative Atmospheric Correction (AC) were used to make wind turbine samples, and SR data were input into the YOLOv5 model (YOLOv5_AC). (2) A Convolutional Block Attention Module (CBAM) was added to the YOLOv5 network to focus on wind turbine features (YOLOv5_AC_CBAM). (3) Based on the identification results of YOLOv5_AC_CBAM, the spectral, geometric, and textural features selected using expert knowledge were extracted to conduct threshold re-identification (YOLOv5_AC_CBAM_Exp). Accuracy increased from 90.5% to 92.7%, then to 93.2%, and finally to 97.4%. The integration of QRS and CV for TDI showed tremendous potential to achieve high accuracy, and QRS information should not be neglected in RS TDI.
Keywords: atmospheric correction (AC); deep learning; quantitative remote sensing (QRS); target identification atmospheric correction (AC); deep learning; quantitative remote sensing (QRS); target identification
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MDPI and ACS Style

Chen, X.; Zhang, Y.; Xue, W.; Liu, S.; Li, J.; Meng, L.; Yang, J.; Mi, X.; Wan, W.; Meng, Q. Quantitative Remote Sensing Supporting Deep Learning Target Identification: A Case Study of Wind Turbines. Remote Sens. 2025, 17, 733. https://doi.org/10.3390/rs17050733

AMA Style

Chen X, Zhang Y, Xue W, Liu S, Li J, Meng L, Yang J, Mi X, Wan W, Meng Q. Quantitative Remote Sensing Supporting Deep Learning Target Identification: A Case Study of Wind Turbines. Remote Sensing. 2025; 17(5):733. https://doi.org/10.3390/rs17050733

Chicago/Turabian Style

Chen, Xingfeng, Yunli Zhang, Wu Xue, Shumin Liu, Jiaguo Li, Lei Meng, Jian Yang, Xiaofei Mi, Wei Wan, and Qingyan Meng. 2025. "Quantitative Remote Sensing Supporting Deep Learning Target Identification: A Case Study of Wind Turbines" Remote Sensing 17, no. 5: 733. https://doi.org/10.3390/rs17050733

APA Style

Chen, X., Zhang, Y., Xue, W., Liu, S., Li, J., Meng, L., Yang, J., Mi, X., Wan, W., & Meng, Q. (2025). Quantitative Remote Sensing Supporting Deep Learning Target Identification: A Case Study of Wind Turbines. Remote Sensing, 17(5), 733. https://doi.org/10.3390/rs17050733

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