Remote Sensing Panoptic Segmentation (RSPS) plays a crucial role in the intelligent interpretation of remote sensing data. Specifically, panoptic segmentation of airport scenes presents significant challenges, the varied shapes of terminal buildings with differences in appearance, size, scale, materials, and color make segmentation tasks highly complex. The intricate geometric shapes and varying scale parameters lead to the entangled object scales in the feature space, posing challenges for accurate segmentation. These factors collectively contribute to the complexity of panoptic segmentation for irregularly-shaped buildings. Current mainstream mask-based panoptic segmentation methods do not fully consider the importance of enhancing and integrating low-level and high-level semantic features. They lack the ability to decouple features, leading to misclassification and false negatives. To solve the above-mentioned problem of panoptic segmentation of irregularly-shaped buildings, this study designs a morphology-guided feature aggregation (MGFA) module to establish the correlation between low and high level semantic features at different stages. At the same time, a multi-order semantic decoupling (MOSD) module is designed to capture structural information at different scales and improve the network’s understanding of complex structural information. In addition, due to the lack of datasets for transportation hub scenarios in the RSPS task, we have collected the airport panoptic segmentation dataset (AP-RSPS) to promote the practical application of RSPS tasks. Extensive experiments on the AP-RSPS dataset and BSB Aerial Dataset show that the proposed MFGS2Net outperforms several SOTA methods.

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.