Analysis and application of lineaments extraction using GF-1 satellite images in loess covered

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Abstract

Faults, folds and other tectonics regions belong to the weak areas of geology, will form linear geomorphology as a result of erosion, which appears as lineaments on the earth surface. lineaments control the distribution of regional formation, groundwater, and geother-mal, etc., so it is an important indicator for the evaluation of the strength and stability of the geological structure. The current algorithms mostly are artificial visual interpretation and computer semi-automatic extraction, not only time-consuming, but labour-intensive. It is difficult to guarantee the accuracy due to the dependence on the expert's knowledge, experience, and the computer hardware and software. Therefore, an integrated algorithm is proposed based on the GF-1 satellite image data, taking the loess area in the northern part of Jinlinghe basin as an example. Firstly, the best bands with 4-3-2 composition is chosen using optimum index factor (OIF). Secondly, line edge is highlighted by Gaussian high-pass filter and tensor voting. Finally, the Hough Transform is used to detect the geologic lineaments. Thematic maps of geological structure in this area are mapped through the extraction of lineaments. The experimental results show that, influenced by the northern margin of Qinling Mountains and the declined Weihe Basin, the lineaments are mostly distributed over the terrain lines, and mainly in the NW, NE, NNE, and ENE directions. It provided a reliable basis for analysing tectonic stress trend because of the agreement with the existing regional geological survey. The algorithm is more practical and has higher robustness, less disturbed by human factors.

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APA

Han, L., Liu, Z., Zhao, Z., & Ning, Y. (2018). Analysis and application of lineaments extraction using GF-1 satellite images in loess covered. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 42, pp. 467–473). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLII-3-467-2018

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