Ground control point (GCP) is important for georeferencing remotely sensed images and topographic model. However, considering that GCP collection is sometimes a difficult, time-consuming and expensive task with high r...Ground control point (GCP) is important for georeferencing remotely sensed images and topographic model. However, considering that GCP collection is sometimes a difficult, time-consuming and expensive task with high resolution (HR) data in remote and harsh environments, today unmanned aerial vehicle based remote sensing (UAVRS) is frequently used in geological disaster emergency monitoring and rescuing for its great advantage in collecting timely onsite images. In this paper, for evaluating the feasibility of the UAVRS in disaster emergency and high cut slope safety monitoring, the digital surface model (DSM) without GCPs based on Structure from Motion (SfM) is accessed, and results showed that the geometric accuracy of DSM was smaller than 1 percent, which prove the usefulness of DSM based on UAVRS in emergency. Comparing to normal disaster emergency, the method without GCPs can be more efficient and save the disaster emergency time by neglecting GCPs measurement.展开更多
Poverty has always been one of the topics concerned by governments and researchers all over the world, especially in developing countries. Remote sensing image is widely used in poverty estimation because of its large...Poverty has always been one of the topics concerned by governments and researchers all over the world, especially in developing countries. Remote sensing image is widely used in poverty estimation because of its large area observation, timeliness and periodicity. In this study, we explore the applicability of convolution neural network (CNN) combined with remote sensing image in regional poverty estimation. In the 2016 economic indicators estimation of Guizhou Province, China, the Pearson coefficient of per capita GDP (PCGDP) reached 0.76, which means that the image features extracted by CNN can explain the change of PCGDP of county level economic indicators up to 76%. Compared with other methods, our method still has high precision. Based on these results, we found that convolutional neural network combined with remote sensing image can be used in regional poverty estimation.展开更多
Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has...Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has attracted much interest in many researches especially in the field of agriculture. On the other hand, deep learning (DL) based semantic segmentation shows high performance in remote sensing classification, and it requires large datasets in a supervised learning way. In the paper, a method of fusing multi-source remote sensing images with convolution neural networks (CNN) for semantic segmentation is proposed and applied to identify crops. Venezuelan Remote Sensing Satellite-2 (VRSS-2) and the high-resolution of Google Earth (GE) imageries have been used and more than 1000 sample sets have been collected for supervised learning process. The experiment results show that the crops extraction with an average overall accuracy more than 93% has been obtained, which demonstrates that data fusion combined with DL is highly feasible to crops extraction from satellite images and GE imagery, and it shows that deep learning techniques can serve as an invaluable tools for larger remote sensing data fusion frameworks, specifically for the applications in precision farming.展开更多
In the process of site selection and the facility construction in power engineering, the geological conditions of the foundation have an important impact on the displacement of completed power facilities. Usually, the...In the process of site selection and the facility construction in power engineering, the geological conditions of the foundation have an important impact on the displacement of completed power facilities. Usually, the conventional surface displacement has certain continuity in time and space. Therefore, in the initial stage of power line selection, the relatively stable geological conditions can greatly reduce the probability of major accidents due to ground deformation. As a new surface displacement monitoring method, InSAR can obtain the displacement monitoring results in long time series. This paper used 20 Sentinel-1A data to study the geological conditions of power line selection. Based on the fact that the vegetation coverage in the line selection area and the poor penetration of C-band data may cause serious body scattering correlation, we verified the possibility of obtaining accurate results in a long-time baseline with less influence on volume scattering decorrelation. Using this method, we obtained the surface history displacement line chart of the 220 kV power line to be erected in Laiyuan to Quanyu, Hebei Province. By analyzing 10 high coherence points on the line, we found that the largest historical surface displacement in the 18 months is less than 30mm, and the maximum cumulative deformation rate is only 12 mm/a, which meets the requirements of power line erection.展开更多
文摘Ground control point (GCP) is important for georeferencing remotely sensed images and topographic model. However, considering that GCP collection is sometimes a difficult, time-consuming and expensive task with high resolution (HR) data in remote and harsh environments, today unmanned aerial vehicle based remote sensing (UAVRS) is frequently used in geological disaster emergency monitoring and rescuing for its great advantage in collecting timely onsite images. In this paper, for evaluating the feasibility of the UAVRS in disaster emergency and high cut slope safety monitoring, the digital surface model (DSM) without GCPs based on Structure from Motion (SfM) is accessed, and results showed that the geometric accuracy of DSM was smaller than 1 percent, which prove the usefulness of DSM based on UAVRS in emergency. Comparing to normal disaster emergency, the method without GCPs can be more efficient and save the disaster emergency time by neglecting GCPs measurement.
文摘Poverty has always been one of the topics concerned by governments and researchers all over the world, especially in developing countries. Remote sensing image is widely used in poverty estimation because of its large area observation, timeliness and periodicity. In this study, we explore the applicability of convolution neural network (CNN) combined with remote sensing image in regional poverty estimation. In the 2016 economic indicators estimation of Guizhou Province, China, the Pearson coefficient of per capita GDP (PCGDP) reached 0.76, which means that the image features extracted by CNN can explain the change of PCGDP of county level economic indicators up to 76%. Compared with other methods, our method still has high precision. Based on these results, we found that convolutional neural network combined with remote sensing image can be used in regional poverty estimation.
文摘Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has attracted much interest in many researches especially in the field of agriculture. On the other hand, deep learning (DL) based semantic segmentation shows high performance in remote sensing classification, and it requires large datasets in a supervised learning way. In the paper, a method of fusing multi-source remote sensing images with convolution neural networks (CNN) for semantic segmentation is proposed and applied to identify crops. Venezuelan Remote Sensing Satellite-2 (VRSS-2) and the high-resolution of Google Earth (GE) imageries have been used and more than 1000 sample sets have been collected for supervised learning process. The experiment results show that the crops extraction with an average overall accuracy more than 93% has been obtained, which demonstrates that data fusion combined with DL is highly feasible to crops extraction from satellite images and GE imagery, and it shows that deep learning techniques can serve as an invaluable tools for larger remote sensing data fusion frameworks, specifically for the applications in precision farming.
文摘In the process of site selection and the facility construction in power engineering, the geological conditions of the foundation have an important impact on the displacement of completed power facilities. Usually, the conventional surface displacement has certain continuity in time and space. Therefore, in the initial stage of power line selection, the relatively stable geological conditions can greatly reduce the probability of major accidents due to ground deformation. As a new surface displacement monitoring method, InSAR can obtain the displacement monitoring results in long time series. This paper used 20 Sentinel-1A data to study the geological conditions of power line selection. Based on the fact that the vegetation coverage in the line selection area and the poor penetration of C-band data may cause serious body scattering correlation, we verified the possibility of obtaining accurate results in a long-time baseline with less influence on volume scattering decorrelation. Using this method, we obtained the surface history displacement line chart of the 220 kV power line to be erected in Laiyuan to Quanyu, Hebei Province. By analyzing 10 high coherence points on the line, we found that the largest historical surface displacement in the 18 months is less than 30mm, and the maximum cumulative deformation rate is only 12 mm/a, which meets the requirements of power line erection.
