Automatic Extraction of Objects/Features from Imagery of Electrical Substations

Substations are among the most important components of electrical transmission and distribution systems in the world. Unfortunately, some electrical equipment in substations is suitable place for animals to perch, roost, and hunt leading to their electrocution and possible power outages. Researchers have proposed different solutions to protect these animals from electrocution such as covering the electrical equipment. This solution requires field work to determine the dimensions of electrical equipment within high voltage environments which is potentially dangerous. Remote sensing methods can provide a suitable means to obtain measurements while mitigating such danger, because the field worker can stay a safe distance away from hazardous equipment such as power lines and hot connections. In this work, analysis of images taken from electrical substations is used to obtain measurements of equipment of interest. The main idea of this work is that hot connections are placed somewhere between insulators and power lines or insulators and bus pipes. Thus, if centerlines of insulators and power lines are extracted, the position of the hot connections will be known. Therefore, different feature-based algorithms are developed to extract insulators and power lines which will help in extracting hot connections. These algorithms include: 1) an automated texture/edge-based algorithm for insulator extraction, 2) an automatic edge-based power line extraction, and 3) a semi-automatic snake-based power line extraction. Then, the main objective of this thesis is to extract insulators and power lines from imagery of electrical substations. The extraction of bus pipes is not directly addressed; however, the power line extraction algorithms can be used to extract some of bus pipes, too

A deterministic descriptive regularization-based method for SAR tomography in urban areas

In recent years, Synthetic Aperture Radar (SAR) Tomography (TomoSAR) has ascertained great potential for the three-dimensional (3-D) reconstruction of observed scenes, especially in urban areas. However, the number of proceed snapshots (observations) is usually less than that of slant height samples (unknowns) in TomoSAR inversion processes. This impairs the quality of the reconstructed vertical information. To cope with this issue and improve the reliability of reconstructed vertical information, this paper investigates the possible potential of a deterministic descriptive regularization-based method. Deterministic descriptive regularization is a well-conditioned optimization framework based on the descriptive idea of a regularization solution. This strategy can help to mitigate the effect of the ill-posed problem. Thus, it can assist SAR tomography to deal with the possible impairing issues arising from low numbers and the distribution of baselines. For this purpose, the result of the proposed strategy is compared with the outcomes from the standard TomoSAR techniques, including Beamforming, Capon, and Minimum Norm. The proposed method for reconstruction of the reflectivity function of the observed scene has been performed on a dataset acquired by the Sentinel-1 sensor in 2022 over Tehran City, Iran. The experimental results indicate that the proposed algorithm can estimate building heights with a vertical accuracy of better than 91%. These results demonstrate the great potential of the proposed method for reconstructing the full 3-D images of urban area