Synthesis and Detection Algorithms for Oblique Stripe Noise of Space-Borne Remote Sensing Images

作者：Binbo Li, Donghai Xie, Yu Wu, Lijuan Zheng, Chongbin Xu, Ying Zhou, Yibo Fu, Chenglong Wang, Bin Liu, Xin Zuo · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2024 · DOI：10.1109/tgrs.2024.3360268 · 被引用次数：5 · 研究领域：Advanced Image Fusion Techniques、Image and Signal Denoising Methods、Remote-Sensing Image Classification

Oblique stripe noise widely appears in remote sensing images after image correction, exhibiting arbitrary tilt angles and parallel distribution. Due to its arbitrary randomness in tilt angles and lengths, oblique stripe noise increases the difficulty of detection compared to vertical or horizontal stripe noise. For the first time, we propose a group of oblique stripe noise synthesis and detection algorithms combining imaging mechanisms and deep learning. To get controllable synthetic oblique stripe noise data for training detection model, two sample augmentation methods are presented by the image correction’s imaging mechanisms with new linear transformation and the generative adversarial network algorithm with Cycle-GAN, respectively. A large-scale simulated stripe noise dataset (SOSD, simulated oblique stripe noise dataset) is simulated using these two methods. A new deep learning detection algorithm (RDOS, Robust detection of oblique stripe Noise) is presented considering the presence of oblique stripe noise. RDOS is trained using both SOSD and a real stripe noise dataset, and it obtains the optimal detection model for testing. The experimental results show that the accuracy reaches 82.93%, the recall rate reaches 85.17%, the F1 score reaches 84.04%, the average precision (AP) reaches 82.34%, and the frames per second (FPS) reaches 33.33. Compared with the general line detection models, our model exceeds ~300% in accuracy and ~60% in speed. In the future, the proposed algo...