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.
