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

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.