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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]