A new fuzzy level set method (FLSM) based on the global search capability of quantum particle swarm optimization (QPSO) is proposed to improve the stability and precision of image segmentation, and reduce the sensitivity of initialization. The new combination of QPSO-FLSM algorithm iteratively optimizes initial contours using the QPSO method and fuzzy c-means clustering, and then utilizes level set method (LSM) to segment images. The new algorithm exploits the global search capability of QPSO to obtain a stable cluster center and a pre-segmentation contour closer to the region of interest during the iteration. In the implementation of the new method in segmenting liver tumors, brain tissues, and lightning images, the fitness function of the objective function of QPSO-FLSM algorithm is optimized by 10% in comparison to the original FLSM algorithm. The achieved initial contours from the QPSO-FLSM algorithm are also more stable than that from the FLSM. The QPSO-FLSM resulted in improved final image segmentation.
This paper proposes an improved algorithm which combines neural networks with diverse 3-D structural features to partition radar reflectivity into convective and stratiform precipitation types. Radar data used in this work were obtained from three networked X-band Doppler radars located in Chengdu, China. The proposed algorithm consists of the two sections: six high-resolution features, which could be extracted from radar volume scanning and expressed the characteristics of the target in many ways, are selected as input to the neural network; systematic self-diagnosis is implemented; and the optimized model is determined according to analysis of bias and variance of classifier. Three state-of-art classification algorithms were implemented as references for algorithm evaluation. Both subjective comparison and statistical results convince that performance of the proposed algorithm is better than performance of traditional classification algorithms in various weather systems. The statistical results show that the proposed algorithm F-score values are 3%, 24%, and 30% higher than the fuzzy logic, SHY95, and BL algorithms and the recognition speed of the proposed algorithm is 54 times, two times, and four times that of fuzzy logic, SHY95, and BL, respectively. Considering network training is an offline procedure, classification by proposed neural network algorithm has great potential in real-time weather analysis for the precipitation classification.
Plain Language SummaryIn recent years, networked radar become the popular way to overcome some of the inherent shortcomings of single radar. There are different scanning strategies for convective and stratiform clouds, and it is difficult to accurately distinguish between convective clouds and stratiform clouds, and when the accuracy is high, there will be a problem of low-recognition rate. Our algorithm utilizes a variety of features derived from radar products that have a good distinction between the two categories. At the same time, combined with neural network, its powerful nonlinear fitting and high speed make the recognition speed fast and the recognition result is high. The recognition result of our algorithm is higher than the recognition result of the popular fuzzy logic algorithm, and its recognition speed is even several times that of the fuzzy logic algorithm.
Weather radars are commonly used to track the development of convective storms due to their high resolution and accuracy. However, the coverage of existing weather radar is very limited, especially in mountainous and ocean areas. Geostationary meteorological satellites can provide near global coverage and near real-time observations, which can compensate for the lack of radar observations. In this paper, a deep learning method was used to estimate the radar composite reflectivity from observations of China’s new-generation geostationary meteorological satellite FY-4A and topographic data. The derived radar reflectivity products from satellite observations can be used over regions without radar coverage. In general, the deep learning model can reproduce the overall position, shape, and intensity of the radar echoes. In addition, evaluation of the reconstruction radar observations indicates that a modified model based on the attention mechanism (Attention U-Net model) has better performance than the traditional U-Net model in terms of all statistics such as the probability of detection (POD), critical success index (CSI), and root-mean-square error (RMSE), and the modified model has stronger capability on reconstructing details and strong echoes.
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