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In order to realize the refined line inspection management of transmission lines' improve its operation and maintenance efficiency, realize satellite intelligent inspection, and accurately find the defects and hidden dangers of towers and transmission lines, the paper took the coordinates of transmission line towers in Kunming City, Yunnan Province as an example and proposed a method to calibrate the coordinates of transmission towers using satellite images. The method first uses the reference base - map data as the basis to match the control points and uses the digital elevation model % DEM) to perform geometric correction on the original remote sensing image. Then combined with such technologies as shadow detection and edge detection and visual interpretation, the calibrated tower coordinates are obtained. The experiment verified the geometric con'ection accuracy of the SuperView - 1 % SV1) and Gaofen -- 2 % GF2 ) sateHite images in the Kunming area, and the errors in the plane after conbection were 0.931 and 1.387 m, respectively. In addition, the experiment verified the calibration accuracy of the old tower coordinates on the two lines. The results show that the plane accuracy of the tower has increased from 13. 811 m and 8. 256 m to 5. 970 m and 5. 104 m, respectively, which meets the basic power grid requirements. This method can realize the calibration of the tower coordinates, reduce the workload of manual inspection, and improve the efficiency of line inspection. With the explosive growth of remote sensing image data, multi -- source images from the space and ground will continue to be combined, and the technology for the positioning of transmission towers based on satellite remote sensing images will have a broader development prospect. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel image enhancement algorithm via anti-degraded model and L L -based variational retinex (AD- L L VR) for non-uniform illumination endoscopic images. Firstly, a haze-free endoscopic image is obtained by an anti-degraded model named dark channel prior (DCP). For getting a more accurate transmission map, it is refined by using a guided image filtering. Secondly, the haze-free endoscopic image is decomposed into detail and naturalness components by light filtering. Thirdly, a logarithmic Laplacian-based gamma correction (LLGC) is added to the naturalness component for preventing color cast and uneven lighting. Fourthly, we assume that the error between the detail component of the haze-free image and the product of associated reflectance and background illumination follows Gaussian-Laplacian distribution. So, the associated reflectance component can be obtained by using the proposed L L -based variational retinex ( L L VR) model. Finally, the recombination of modified naturalness component and associated reflectance component become the final result. Experimental results demonstrate that the proposed algorithm reveals more details in the background regions as well as other interesting areas and can mostly prevent the color cast. It has a better performance on increasing diagnosis and reducing misdiagnosis than other existing enhancement methods. [ABSTRACT FROM AUTHOR]