The knowledge of linear structures consists of faults and fractures is useful for geological and hydrogeological studies and the most importantly for exploration of mineral resources. Lineation map is usually extracted from spatial data such as satellite images and aerial photos. Although visual extracting is the popular method, user errors are highly reduced by automatic extraction methods. The best methods are the ones which combine edge enhancement filters and feature extraction algorithms. In this study, an algorithm is presented based on Gaussian kernel derivative operator as an edge enhancer and Radon transform as the linear shape extractor in a gridded image. This method was tested on a simple image with high level of noises which produced promising results. This was also implemented on a Landsat image (TM) from a sandstone plateau in northern state of Australia, an aerial photo from southern Nevada and a manual photo from Mozaeic Canyon, California and its results compared with another method on the same database. Results show that this algorithm is lonely effective enough to detect lineation. These results, even with a very simple algorithm with low computation cost, are highly comparative with the results of a complicated method. It should be mentioned that this method produces discontinued results for continuous lineation, but it is able to capture non-linear structures.
Karimpouli, S. (2016). Automatic detection of lineation in satellite images and aerial photos using Radon transform. Journal of Mining Engineering, 10(29), 41-49.
MLA
Sadegh Karimpouli. "Automatic detection of lineation in satellite images and aerial photos using Radon transform". Journal of Mining Engineering, 10, 29, 2016, 41-49.
HARVARD
Karimpouli, S. (2016). 'Automatic detection of lineation in satellite images and aerial photos using Radon transform', Journal of Mining Engineering, 10(29), pp. 41-49.
VANCOUVER
Karimpouli, S. Automatic detection of lineation in satellite images and aerial photos using Radon transform. Journal of Mining Engineering, 2016; 10(29): 41-49.
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