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Remote-sensing image dehazing (RSID) is crucial for applications such as military surveillance and disaster assessment. However, current methods often rely on complex network architectures, compromising computational efficiency and scalability. Furthermore, the scarcity of annotated remote-sensing-dehazing datasets hinders model development. To address these issues, a Dual-View Knowledge Transfer (DVKT) framework is proposed to generate a lightweight and efficient student network by distilling knowledge from a pre-trained teacher network on natural image dehazing datasets. The DVKT framework includes two novel knowledge-transfer modules: Intra-layer Transfer (Intra-KT) and Inter-layer Knowledge Transfer (Inter-KT) modules. Specifically, the Intra-KT module is designed to correct the learning bias of the student network by distilling and transferring knowledge from a well-trained teacher network. The Inter-KT module is devised to distill and transfer knowledge about cross-layer correlations. This enables the student network to learn hierarchical and cross-layer dehazing knowledge from the teacher network, thereby extracting compact and effective features. Evaluation results on benchmark datasets demonstrate that the proposed DVKT framework achieves superior performance for RSID. In particular, the distilled model achieves a significant speedup with less than 6% of the parameters and computational cost of the original model, while maintaining a state-of-the-art dehazing performance.

The superpixel usually serves as a region-level feature in various image processing tasks, and is known for segmentation accuracy, spatial compactness and running efficiency. However, since these properties are intrinsically incompatible, there is still a compromise within the overall performance of existing superpixel algorithms. In this work, the property constraint in superpixels is relaxed by in-depth understanding of the image content, and a novel two-stage superpixel generation framework is proposed to produce content-aware superpixels. In the global processing stage, a diffusion-based online average clustering framework is introduced to efficiently aggregate image pixels into multiple superpixel candidates according to color and spatial information. During this process, a centroid relocation strategy is established to dynamically guide the region updating. According to the area feature in manifold space, several superpixel centroids are then split or merged to optimize the regional representation of image content. Subsequently, local updating is adopted on pixels in those superpixel regions to further improve the performance. As a result, the dynamic centroid relocating strategy offers online averaging clustering the property of content awareness through coarse-to-fine label updating. Extensive experiments verify that the produced superpixels achieve desirable and comprehensive performance on boundary adherence, visual satisfactory and time consumption. The quantitative results are on par with existing state-of-the-art algorithms in terms with several common property metrics.

The successful detection of key components within satellites is a crucial prerequisite for executing on-orbit capture missions. Due to the inherent data-driven functionality, deep learning-based component detection algorithms rely heavily on the scale and quality of the dataset for their accuracy and robustness. Nevertheless, existing satellite image datasets exhibit several deficiencies, such as the lack of satellite motion states, extreme illuminations, or occlusion of critical components, which severely hinder the performance of detection algorithms. In this work, we bridge the gap via the release of a novel dataset tailored for the detection of key components of satellites. Unlike the conventional datasets composed of synthetic images, the proposed Typical Components of Satellites (TYCOS) dataset comprises authentic photos captured in a simulated space environment. It encompasses three types of satellite, three types of key components, three types of illumination, and three types of motion state. Meanwhile, scenarios with occlusion in front of the satellite are also taken into consideration. On the basis of TYCOS, several state-of-the-art detection methods are employed in rigorous experiments followed by a comprehensive analysis, which further enhances the development of space scene perception and satellite safety.

The Madden–Julian Oscillation (MJO) is a crucial predictability source on a subseasonal-to-seasonal (S2S) timescale. Therefore, the models participating in the World Weather Research Programme and the World Climate Research Programme (WWRP/WCRP) S2S prediction project focus on accurately predicting and analyzing the MJO. This study provides a detailed description of the configuration within the Institute of Atmospheric Physics at the Chinese Academy of Sciences (IAP-CAS) S2S forecast system. We assess the accuracy of the IAP-CAS model's MJO forecast using traditional Real-time Multivariate MJO (RMM) analysis and cluster analysis. Then, we explain the reasons behind any bias observed in the MJO forecast. Comparing the 20-year hindcast with observations, we found that the IAP-CAS ensemble mean has a skill of 24 d. However, the ensemble spread still has potential for improvement. To examine the MJO structure in detail, we use cluster analysis to classify the MJO events during boreal winter into four types: fast-propagating, slow-propagating, standing, and jumping patterns of MJO. The model exhibits biases of overestimated amplitude and faster propagation speed in the propagating MJO events. Upon further analysis, it was found that the model forecasted a wetter background state. This leads to stronger forecasted convection and coupled waves, especially in the fast MJO events. The overestimation of the strength and length of MJO-coupled waves results in a faster MJO mode and quicker dissipation in the IAP-CAS model. These findings show that the IAP-CAS skillfully forecasts signals of MJO and its propagation, and they also provide valuable guidance for improving the current MJO forecast by developing the ensemble system and moisture forecast. [ABSTRACT FROM AUTHOR]

The Madden-Julian Oscillation (MJO) is a crucial predictability source on a sub-seasonal to seasonal (S2S) timescale. Therefore, the models participating in the WWRP/WCRP S2S prediction project focus on accurately predicting and analyzing the MJO. This study provided a detailed description of the configuration within the IAP-CAS S2S forecast system. We assessed the accuracy of the IAP-CAS model's MJO forecast using traditional RMM analysis and cluster analysis. Then, we explained the reasons behind any bias observed in the MJO forecast. Comparing the 20-year hindcast with observations, we found that the IAP-CAS ensemble mean has a skill of 24 days. However, there is still room for improvement in the ensemble spread. To examine the MJO structure in detail, we used cluster analysis to classify the MJO events during boreal winter into four types: fast-propagating, slow-propagating, standing, and jumping patterns of MJO. The model exhibits biases of overestimated amplitude and faster propagation speed in the propagating MJO events. Upon further analysis, it was found that the model forecasted a wetter background state. This leads to more intense forecasted convection and stronger coupled winds, especially in the fast MJO events. However, the horizontal moisture advection effect for eastward propagation is overestimated in IAP-CAS due to the wetter state and more substantial MJO circulations, which results in a faster MJO mode. These findings show that the IAP-CAS skilfully forecasts signals of MJO and its propagation, and they also provide valuable guidance for improving the current MJO forecast by developing the ensemble system and moisture forecast. [ABSTRACT FROM AUTHOR]

The zonal‐mean subtropical westerly jet (SWJ) in boreal winter shows a significant northward shift trend under recent climate change. Previous studies proposed thermal forcing—represented by the thermal wind associated with the temperature gradient—and the driving effect of the eddy momentum flux (EMF) convergence that leads to the eddy‐driven jet as explanations for this process; however, their relative importance in influencing the SWJ shift requires further investigation. In this study, we examined the roles of thermal forced and eddy‐driven jet components in the zonal‐mean northward SWJ shift. We also investigated the role of Hadley circulation because its poleward boundary is related to the zonal‐mean SWJ. Results suggest that thermal forced component plays a major role, while EMF‐driven component plays a secondary role. Specifically, the subtropical warming, which is primarily influenced by enhanced adiabatic downward motion of the Hadley circulation, increases the meridional temperature gradient and the associated thermal wind poleward of the SWJ. It also reduces atmospheric static stability aloft and converges the EMF on the poleward side of the climatological SWJ. Enhanced meridional temperature gradient and EMF convergence on the poleward side push the SWJ northward. Results from further mathematical analysis indicate that thermal forced and eddy‐driven zonal wind components account for 72% and 28% of the shift distance, respectively. In addition to elucidating the relative importance of thermal and EMF forcings, this study emphasizes the critical role of the subtropical warming driven by the intensified local descending branch of the Hadley circulation in shifting the zonal‐mean SWJ. Plain Language Summary: Both the thermal forced and eddy‐driven perspectives are widely used in literature to understand the northward shift of subtropical westerly jet (SWJ) under climate change. The former is related to the temperature structure, while the latter is related to the eddy momentum flux convergence. This study aims to investigate their relative importance in the northward shift of SWJ. By using a simple decomposition, this study shows that the subtropical westerly jet can be viewed as the sum of the thermal forced and eddy‐driven jet components. Results suggest that the thermal forced effect plays a major role in shifting the SWJ, while the eddy‐driven effect plays a secondary role. Here, it should be pointed out that the term thermal forced jet in this study refers in particular to the jet induced by the meridional gradient of temperature field, which is different from the term thermally driven jet caused by diabatic heating in some studies. This study can help readers grasp the main processes in understanding the SWJ shift, and therefore can be used in fields such as emergent constraints, that is, using the thermal forced jet to constrain the future projection of the SWJ. Key Points: The thermal forced (eddy‐driven) wind plays a major (secondary) role in shifting the subtropical westerly jet under recent climate changeThermal forced and eddy‐driven effects are related to subtropical warming which mainly comes from the enhanced adiabatic sinking motion [ABSTRACT FROM AUTHOR]