PNBT-CR: A Cloud Removal Method for Ship Detection

In the ship detection of the remote sensing images, cloud occlusion could blur the boundaries between ships and backgrounds, making it more challenging to distinguish them. Cloud occlusion can also result in partial or complete occlusion of target, making it difficult for models to detect ships in their entirety. Therefore, the use of cloud removal techniques is essential to enhance the accuracy and robustness of target detection. However, existing cloud removal processes are applied to entire images, providing limited improvements for specific object detection. In this letter, A Perlin Noise Based Thin Cloud Removal (PNBT-CR) Network is proposed for ship detection. The proposed algorithm introduces a Perlin noise mist mix module, which can improve the cloud removal effect of the network effectively. And it designed a Target-Oriented Structural Similarity (TOSS) loss function that enhances the network’s ability to boost the confidence of detected ships in the results. Experimental results demonstrate the efficacy of this approach in restoring texture details in ships and enhancing the accuracy of ship detection in remote sensing imagery. Moreover, the images processed using our method can have 89.86% SSIM compared to the original images, and when used for ship detection, there can be a maximum improvement of 11.7% F1-score.