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Cloud detection is one of the critical tasks in remote sensing image preprocessing. Remote sensing images usually contain multi-dimensional information, which is not utilized entirely in existing deep learning methods. This paper proposes a novel cloud detection algorithm based on multi-scale input and dual-channel attention mechanisms. Firstly, we remodeled the original data to a multi-scale layout in terms of channels and bands. Then, we introduced the dual-channel attention mechanism into the existing semantic segmentation network, to focus on both band information and angle information based on the reconstructed multi-scale data. Finally, a multi-scale fusion strategy was introduced to combine band information and angle information simultaneously. Overall, in the experiments undertaken in this paper, the proposed method achieved a pixel accuracy of 92.66% and a category pixel accuracy of 92.51%. For cloud detection, the proposed method achieved a recall of 97.76% and an F1 of 95.06%. The intersection over union (IoU) of the proposed method was 89.63%. Both in terms of quantitative results and visual effects, the deep learning model we propose is superior to the existing semantic segmentation methods. [ABSTRACT FROM AUTHOR]

Recently, single object tracking has demonstrated great success. However, due to various problems caused by fast motion, occlusion, and deformation, it is still intractable for traditional trackers to adapt to changes in object appearance. In this work, a dynamic memory network is proposed for visual tracking to handle the template matching process globally. More specifically, we first build a memory model, which consists of a memory feature fusion module and a memory bank. By the memory model, the network not only accepts the first frame as initial information but also memorizes the selective frames in the video sequence to provide rich time-domain information. Second, an innovative sampling strategy is adopted in the tracking process. By updating the template and guiding the selection of memory frames, our model can output higher-quality features. In addition, a spatial-channel fused attention module that effectively improves the representational capability and discriminability of the model is introduced. Our proposed method obtains compelling results on six challenging tracking benchmarks, including the OTB100, VOT2019, UAV123, NFS, GOT-10k, and LaSOT datasets. Extensive experiments demonstrate that our approach shows satisfactory robustness and leading application potential in real-time speed. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting has a profound impact on humanity and society, especially in areas with heavy rainfall, playing a central role in alerting against rainstorm disasters. At present, numerous deep learning-based methods have been proposed and proven superior to traditional radar echo extrapolation techniques like the Recurrent Neural Networks (RNNs). Our study introduces a novel precipitation forecasting model named TISE-LSTM, which can use the real image of the past half hour to predict the radar echo image of the next hour. By integrating the TIB and SEB into TISL-LSTM, we can alleviate the inherent issue observed in existing models, which is the decrease in forecast accuracy for high radar echo regions with extended extrapolation time. On both the CIKM23017 and AHEM real radar echo datasets, the performance of TISL-LSTM (including POD, FAR, CSI and HSS) is improved compared to the second-ranked model by 7.53%, 2.32%, 8.93%, 2.6%, and 14.84%, 6.18%, 8.92%, 18.12%, respectively, when the precipitation threshold is set to 40. Moreover, our model obtained an optimal MAE, MSE, SSIM score. Predicted images and the graphs of each metric over extrapolation time both demonstrate that our model accurately forecasts regions with high radar echoes even with a one-hour extrapolation. [ABSTRACT FROM AUTHOR]