Ground-based radar has been widely used for deformation monitoring and early warning of geological hazard potential areas. However, during long-term monitoring, ground-based radar images are vulnerable to human and environmental factors leading to severe decoherence. The study of ground-based radar image change detection can provide reference information for its long-term monitoring. Based on this, an unsupervised change detection method based on convolutional neural network (CNN) for ground-based radar images is proposed in this paper. First, the interference principle to extract change information is used for the first time for the change detection task, aiming to improve the accuracy of the initial extraction of change regions. Secondly, the fuzzy c-means clustering algorithm is used to obtain the pseudo-label matrix with categories, and the appropriate neighborhood image blocks with pseudo-labels are selected as training samples to train CNN. Finally, the change detection results of ground-based radar images are obtained using the trained CNN. Experiments were conducted using actual measurement data from ground-based radar in a monitoring task in a mining area in China and compared with other methods to verify the effectiveness of this paper's method and more accurate detection results.
Waveform optimization technology based on phase encoding has become a key technology to improve the ability of radar to detect small targets. Designing different phase encoding models for different application scenarios and platforms can effectively improve the performance of radar in complex environments such as clutter and interference. Therefore, it’s very important to design an optimization algorithm with high orthogonality and fast convergence. This paper proposes an improved dynamic genetic algorithm to solve the optimization problem of Multi-Input Multi-Output radar phase encoding signal set. By improving the optimization model of the genetic algorithm, the diversity of the population is quantified to prevent the algorithm from converging prematurely. The improved dynamic genetic algorithm reduces the genetic probability of inferior individuals in the selection operation, then proposes to update the crossover probability in the crossover operation, and finally designs the mutation probability for individual gene points in the mutation operation, which solves the key problem of poor diversity in existing algorithms question. The simulation results show that the improved dynamic genetic algorithm improves the population diversity, optimizes the convergence speed of the algorithm, and the optimized phase encoding set has good performance, and the result is better than the existing improved genetic algorithm.
In recent years, Differential Synthetic Aperture Radar Interferometry (D-InSAR) can effectively measure the surface displacement caused by earthquake, and has been widely used. On January 8, 2022, an earthquake of Ms6.9 with a focal depth of 10km occurred in Menyuan County, Qinghai Province. In order to more accurately monitor the surface deformation caused by the earthquake, in this study, Sentinel-1A ascending and descending orbit SAR images are used and processed by four-pass D-InSAR technology to obtain the coseismic deformation field , and the deformation results are analyzed and verified. And the results of adaptive, boxcar and Goldstein filtering are analyzed qualitatively and quantitatively, the phase unwrapping results of each filtering result are compared and analyzed. The results show that the coseismic deformation field of the earthquake ruptured along the northwest southeast direction (NWW-SEE), and the surface deformation of radar line of sight (LOS) are -53.6 ~ 68.8 cm for descending orbits, -69.1 ~ 20.5 cm and -42.9 ~ 41.9 cm for ascending orbits, respectively. For the earthquake area, Goldstein filter is better than the other two filters, which can better suppress noise and maintain phase information and its continuity. The research results have important reference significance for seismic deformation inversion and filtering methods.
In order to improve the prediction accuracy of the slope deformation prediction method in mining area, aiming at the problems of many disaster causing factors of slope deformation and BP neural network is easy to fall into local minimum value, a prediction method based on the combination of genetic algorithm and BP neural network based on grey correlation analysis is proposed. The grey correlation analysis method is used to screen the main influencing factors, and the factors with high correlation degree are used as input indexes to simplify the network structure. Then, the genetic algorithm is used to optimize the BP neural network to establish the GA-BP model, and finally the prediction is compared. The results show that the grey correlation analysis can further improve the consistency between the predicted value and the real value, and the model can accurately predict the slope deformation. The research results have important auxiliary reference significance for mine safety production.
Aiming at the problem that the traditional viaduct deformation monitoring has high accuracy but long monitoring period and consumes a lot of manpower and material resources, it is difficult to extract the severe deformation area of viaduct in time. In this paper, based on the small baseline set interferometric synthetic aperture radar (SBAS-InSAR) technology, the deformation information of viaducts and surrounding areas is retrieved, and the severe deformation areas of urban viaducts and surrounding areas are extracted. Taking three viaducts with large traffic flow in Hohhot as the research object, the deformation results of the study area from August 2020 to September 2021 were obtained. The deformation causes are analyzed combined with the inversion results. The results show that there are five large deformation areas in the three viaducts, and the main deformation causes include soil erosion or urban waterlogging caused by rainfall, surface construction and rail transit operation. The research shows that this method can accurately extract the severe deformation area of urban viaducts, and provide a reference for analyzing the causes of viaduct deformation.
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