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Typical remote sensing target detection with data synthesis based on reinforcement learning
Received date: 2025-03-10
Revised date: 2025-03-16
Accepted date: 2025-05-15
Online published: 2025-05-30
Supported by
National Natural Science Foundation of China(62125102)
Remote sensing detection of targets such as vehicles and aircraft is essential for traffic monitoring and military reconnaissance， and it continues to drive advances in related technologies. However， remote sensing target detection still faces numerous challenges， including the high cost of image annotation and the interference of complex weather conditions on detection performance. To address these issues， a novel image synthesis method that integrates reinforcement learning with controllable image rendering is proposed， transforming the generation of mixed real and synthetic remote sensing data into a reinforcement learning-based scene parameter search problem. Additionally， weather disturbance factors are introduced by simulating clouds， dust， and fog to augment the training data， enhancing its adaptability and realism in complex environments. Experimental results show that， compared with traditional methods， the proposed method achieves significant improvements across multiple evaluation metrics， with a maximum performance gain of 4.2%. This verifies the effectiveness of the proposed method and highlights the potential of synthetic remote sensing images and complex weather effects in improving target detection model accuracy， thereby providing strong support for the advancement of remote sensing target detection technology.
Tianqi FAN , Zhengxia ZOU , Zhenwei SHI . Typical remote sensing target detection with data synthesis based on reinforcement learning[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(23) : 631955 -631955 . DOI: 10.7527/S1000-6893.2025.31955
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