Road is an important kind of basic geographic information. Road information extraction plays an important role in traffic management, urban planning, automatic vehicle navigation, and emergency management. With the de...
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Road is an important kind of basic geographic information. Road information extraction plays an important role in traffic management, urban planning, automatic vehicle navigation, and emergency management. With the development of remote sensing technology, the quality of high-resolution satellite images is improved and more easily obtained, which makes it possible to use remote sensing images to locate roads accurately. Therefore, it is an urgent problem to extract road information from remote sensing images. To solve this problem, a road extraction method based on convolutional neural network is proposed in this paper. Firstly, convolutional neural network is used to classify the high-resolution remote sensing images into two classes, which can distinguish the road from the non-road and extract the road information initially. Secondly, the convolutional neural network is optimized and improved from the training algorithm. Finally, because of the influence of natural scene factors such as house and tree shadow, the non-road noise still exists in the road results extracted by the optimized convolutional neural network method. Therefore, this paper uses wavelet packet method to filter these non-road noises, so as to accurately present the road information in remote sensing images. The simulation results show that the road information of remote sensing image can be preliminarily distinguished by convolutional neural network;the road information can be distinguished effectively by optimizing convolutional neural network;and the wavelet packet method can effectively remove noise interference. Therefore, the proposed road extraction method based on convolutional neural network has good road information extraction effect.
In combination with short circuit simulation and waveletpacket theory, the corresponding probabilistic analysis on the characteristics of single-phase grounding faults of distribution network under stochastic context...
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ISBN:
(纸本)9781728149318
In combination with short circuit simulation and waveletpacket theory, the corresponding probabilistic analysis on the characteristics of single-phase grounding faults of distribution network under stochastic context is studied in this paper. There are two pieces of major research works: firstly, the Latin hypercube sampling (LHS) method is adopted to accelerate probabilistic short circuit analysis, thus improving the efficiency for a further implementation; secondly, wavelet packet method is employed to investigate the influence of stochastic factors on the characteristics of short circuit and fault line identification. Test results verify the effects of the works, as well as some conclusions are drawn.
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