Training samples for deep learning networks are typically obtained through various field experiments,which require significant manpower,resource and time consumption.However,it is possible to utilize simulated data to...Training samples for deep learning networks are typically obtained through various field experiments,which require significant manpower,resource and time consumption.However,it is possible to utilize simulated data to augment the training samples.In this paper,by comparing the actual experimental model with the simulated model generated by the gprMax[1]forward simulation method,the feasibility of obtaining simulated samples through gprMax simulation is validated.Subsequently,the samples generated by gprMax forward simulation are used for training the network to detect objects in existing real samples.At the same time,aiming at the detection and intelligent recognition of road sub-surface defects,the Swin-YOLOX algorithm is introduced,and the excellence of the detection network,which is improved by augmenting the simulated samples with real samples,is further verified.By comparing the prediction performance of the object detection models,it is observed that the model trained with mixed samples achieved a recall of 94.74%and a mean average precision(maP)of 97.71%,surpassing the model trained only on real samples by 12.95%and 15.64%,respectively.The feasibility and excellence of training the model with mixed samples are confirmed.The potential of using a fusion of simulated and existing real samples instead of repeatedly acquiring new real samples by field experiment is demonstrated by this study,thereby improving detection efficiency,saving resources,and providing a new approach to the problem of multiple interpretations in ground penetrating radar(GPR)data.展开更多
Taking TM images, SPOT photos and DEM images as the basic information, this paper had not only put forward a kind of manual controlled computer-automatic extraction method, but also completed the task of extracting th...Taking TM images, SPOT photos and DEM images as the basic information, this paper had not only put forward a kind of manual controlled computer-automatic extraction method, but also completed the task of extracting the main types of ground objects in the Three Gorges Reservoir area under relatively high accuracy, after finishing such preprocessing tasks as correcting the topographical spectrum and synthesizing the data. Taking the specialized image analysis software-eCognition as the platform, the research achieved the goal of classifying through choosing samples, picking out the best wave bands, and producing the identifying functions. At the same time the extraction process partly dispelled the influence of such phenomena as the same thing with different spectrums, different things with the same spectrum, border transitions, etc. The research did certain exploration in the aspect of technological route and method of using automatic extraction of the remote sensing image to obtain the information of land cover for the regions whose ground objects have complicated spectrums.展开更多
文摘Training samples for deep learning networks are typically obtained through various field experiments,which require significant manpower,resource and time consumption.However,it is possible to utilize simulated data to augment the training samples.In this paper,by comparing the actual experimental model with the simulated model generated by the gprMax[1]forward simulation method,the feasibility of obtaining simulated samples through gprMax simulation is validated.Subsequently,the samples generated by gprMax forward simulation are used for training the network to detect objects in existing real samples.At the same time,aiming at the detection and intelligent recognition of road sub-surface defects,the Swin-YOLOX algorithm is introduced,and the excellence of the detection network,which is improved by augmenting the simulated samples with real samples,is further verified.By comparing the prediction performance of the object detection models,it is observed that the model trained with mixed samples achieved a recall of 94.74%and a mean average precision(maP)of 97.71%,surpassing the model trained only on real samples by 12.95%and 15.64%,respectively.The feasibility and excellence of training the model with mixed samples are confirmed.The potential of using a fusion of simulated and existing real samples instead of repeatedly acquiring new real samples by field experiment is demonstrated by this study,thereby improving detection efficiency,saving resources,and providing a new approach to the problem of multiple interpretations in ground penetrating radar(GPR)data.
基金Under the auspices of the Construction Committeeof Three GorgesR eservoirProject(No .SX [2002]00401) andChineseAcademy ofSciences(No .KZCX2-SW-319-01 )
文摘Taking TM images, SPOT photos and DEM images as the basic information, this paper had not only put forward a kind of manual controlled computer-automatic extraction method, but also completed the task of extracting the main types of ground objects in the Three Gorges Reservoir area under relatively high accuracy, after finishing such preprocessing tasks as correcting the topographical spectrum and synthesizing the data. Taking the specialized image analysis software-eCognition as the platform, the research achieved the goal of classifying through choosing samples, picking out the best wave bands, and producing the identifying functions. At the same time the extraction process partly dispelled the influence of such phenomena as the same thing with different spectrums, different things with the same spectrum, border transitions, etc. The research did certain exploration in the aspect of technological route and method of using automatic extraction of the remote sensing image to obtain the information of land cover for the regions whose ground objects have complicated spectrums.
