Cloud detection plays a vital role in remote sensing data preprocessing. Traditional cloud detection algorithms have difficulties in feature extraction and thus produce a poor detection result when processing remote sensing images with uneven cloud distribution and complex surface background. To achieve better detection results, a cloud detection method with multi-scale feature extraction and content-aware reassembly network (MCNet) is proposed. Using pyramid convolution and channel attention mechanisms to enhance the model’s feature extraction capability, MCNet can fully extract the spatial information and channel information of clouds in an image. The content-aware reassembly is used to ensure that sampling on the network can recover enough in-depth semantic information and improve the model cloud detection effect. The experimental results show that the proposed MCNet model has achieved good detection results in cloud detection tasks.
Target tracking technology that is based on aerial videos is widely used in many fields; however, this technology has challenges, such as image jitter, target blur, high data dimensionality, and large changes in the target scale. In this paper, the research status of aerial video tracking and the characteristics, background complexity and tracking diversity of aerial video targets are summarized. Based on the findings, the key technologies that are related to tracking are elaborated according to the target type, number of targets and applicable scene system. The tracking algorithms are classified according to the type of target, and the target tracking algorithms that are based on deep learning are classified according to the network structure. Commonly used aerial photography datasets are described, and the accuracies of commonly used target tracking methods are evaluated in an aerial photography dataset, namely, UAV123, and a long-video dataset, namely, UAV20L. Potential problems are discussed, and possible future research directions and corresponding development trends in this field are analyzed and summarized.
Complete meteorological data is essential for meteorological research. However, due to sensor failure or occlusion, data loss always occurs. In order to deal with this problem, geoscience often uses Kriging and other traditional interpolation methods. Nevertheless, because the traditional method only considers the existing data, it cannot provide accurate reconstruction results when the spatial missing rate is large. Inspired by image inpainting algorithms, we propose a new deep learning method that combines other variables related to missing meteorological variables to provide more effective information for missing regions, and proves the good effect of the method through a series of experiments. To illustrate that our method can maintain good performance under different missing rates, we use the reanalysis data of the European Centre for Medium-Range Weather Forecasts to create a data set with three different missing rates of 30%, 50%, and 70% through the artificial mask. Under different missing rates, the average RMSE of the method based on deep learning decreased by 77% and 31% respectively compared with Kriging and DINEOF. In addition, by combining relevant variables, the accuracy of the deep learning model can be further improved by about 16%.
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