Use of Gestalt Theory and Random Sets for Automatic Detection of Linear Geological Features

This paper presents the calibration and application of a Gestalt-based line segment method for automatic geological lineament detection from remote sensing images. This method involves estimation of the scale factor, the angle tolerance and a threshold on the false alarm rate. It identifies major lineaments as objects characterized by two edges on the image, which appear as transitions from dark to bright and vice versa. These objects were modelled as random sets with parameters drawn from their distributions. Following the geometry of detected segments, a novel validation method assesses the accuracy with respect to a linear vector reference. The methodology was applied to a study area in Kenya where lineaments are prominent in the landscape and are well identifiable from an ASTER image. Error rates were based on distance and local orientation, and the study showed that the existence and size of the objects were sensitive to parameter variation. False detection rate and missing detection rate were both equal to 0.50, which is better than values equal to 0.65 and 0.63, observed using the Canny edge detection. Modelling the uncertainty of geological lineaments with random sets further showed that no core set is formed, indicating that there is an inherent uncertainty in their existence and position, and that the variance is relatively high. Comparing the test area with four areas in the same region showed similar results. Despite some shortcomings in identifying full lineaments from partially observed lineaments, it is concluded that the procedure in this paper is well able to automatically extract lineaments from a remote sensing image and validate their existence.