Surface-defect detection is crucial for assuring the quality of strip-steel manufacturing. Strip-steel surface-defect detection requires defect classification and precision localization, which is a challenge in real-world applications. In this research, we propose an improved PP-YOLOE-m network for detecting strip-steel surface defects. First, data augmentation is performed to avoid the overfitting problem and to improve the model’s capacity for generalization. Secondly, Coordinate Attention is embedded in the CSPRes structure of the backbone network to improve the backbone network’s feature extraction capabilities and obtain more spatial location information. Thirdly, Spatial Pyramid Pooling is specifically replaced for the Atrous Spatial Pyramid Pooling in the neck network, enabling the multi-scale network to broaden its receptive field and gain more information globally. Finally, the SIoU loss function more accurately calculates the regression loss over GIoU. Experimental results show that the improved PP-YOLOE-m network’s AP, AP${}_{50}$, and AP${}_{75}$, respectively, achieved 44.6%, 80.3%, and 45.3% for strip-steel surface defects detection on the NEU-DET dataset and improved by 2.2%, 4.3%, and 4.6% over the PP-YOLOE-m network. Further, our method has fast and real-time detection capabilities and can run at 95 FPS on a single Tesla V100 GPU.

Multilayer clouds have a significant importance on cloud climate effects and remote sensing retrieval. In this study, a multilayer clouds detection algorithm is developed for the Advanced Geostationary Radiation Imagers (AGRI) onboard the FY-4A geostationary satellite. The algorithm is based on basic physical assumptions that are also employed for MODIS and VIIRS to identify ice overlaying water multilayer clouds. Synchronous observation of Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) has been collected and acknowledged as a reliable reference dataset for determination of the thresholds. The algorithm used the longwave infrared bands (8.5μm and 10.8μm) to determine the phase of the upper layer cloud. Then difference between solar reflectance band pairs (1.375μm and 1.61μm) is used to identify ice overlayer water multilayer clouds when the upper layer is ice cloud. When the upper layer cloud is water, the infrared band(7.1μm) is applied to find out misclassified multilayer clouds. The algorithm demonstrates a notable improvement of approximately 0.146 in the probability of detection (POD) as compared to MODIS, while using CALIOP products as reference, specifically for cases when the cloud optical depth surpasses 4. Nevertheless, it does result in a slightly elevated false alarms rate (FAR), around 0.042. In the future, it is necessary to conduct a more comprehensive validation of the algorithm, with particular emphasis on its limits in scenarios where the upper cloud layer is too thin(thick).

Hyperspectral infrared observations are an important data source for numerical weather prediction (NWP) because they provide rich information on the vertical structure of atmospheric temperature and humidity. However, most existing deep learning methods mainly focus on one-way retrieval from radiances to atmospheric profiles, while the reverse radiance simulation process and the consistency between atmospheric state space and radiance observation space are insufficiently considered. In this study, we propose SIMBA, a unified bidirectional retrieval–forward simulation framework for FY-4A/GIIRS hyperspectral infrared radiance modeling toward NWP applications. The framework jointly performs atmospheric profile retrieval and radiance reconstruction, introduces a cycle-consistency constraint to strengthen the coupling between the two processes, and employs a bidirectional Mamba state-space module to capture long-range dependencies along pressure levels. Using collocated FY-4A/GIIRS observations and ERA5 reanalysis data, the proposed method is evaluated for temperature retrieval, specific humidity retrieval, long-wave radiance reconstruction, and medium wave radiance reconstruction. Experimental results show that SIMBA outperforms several representative deep learning baselines across both retrieval and reconstruction tasks, while ablation experiments confirm the contribution of the bidirectional design and cycle-consistency mechanism. These results demonstrate that the proposed framework is effective for joint atmospheric profile retrieval and hyperspectral infrared radiance modeling, and suggests potential for future Jacobian-related analysis and NWP-oriented extensions.

Satellite remote sensing technology can see the breadth and fineness that humans cannot see, and can see the spatio-temporal changes that humans cannot see. The inversion of PM2.5 based on remote sensing satellite has wide spatial coverage and relatively high spatial and temporal resolution, and the estimation results are reliable and easy to obtain, which can provide the data basis and scientific basis for managing PM2.5 pollution. This paper summarizes 2 aspects from remote sensing information processing technology and PM2.5 concentration estimation model. The development trend of current satellite remote sensing technology is analyzed from the perspective of the principle and development of satellite remote sensing technology, and the basic analytical thinking and analytical steps of current remote sensing information technology processing are described from the starting point of remote sensing information acquisition and processing technology; the advantages, disadvantages and adaptability of different models are pointed out through the comparative analysis of domestic and foreign estimation models of PM2.5 concentration. The study shows that the number of remote sensing satellites in China is increasing year by year, and remote sensing data and estimation models are being used effectively and improved gradually.