A gradient-based approach for automated crest-line detection and analysis of sand dune patterns on planetary surfaces

Abstract:

Dune-field patterns are believed to behave as self-organizing systems, but what causes the patterns to form is still poorly understood. The most obvious (and in many cases the most significant) aspect of a dune system is the pattern of dune crest lines. Extracting meaningful features such as crest length, orientation, spacing, bifurcations, and merging of crests from image data can reveal important information about the specific dune-field morphological properties, development, and response to changes in boundary conditions, but manual methods are labor-intensive and time-consuming. We are developing the capability to recognize and characterize patterns of sand dunes on planetary surfaces. Our goal is to develop a robust methodology and the necessary algorithms for automated or semi-automated extraction of dune morphometric information from image data.

Our main approach uses image processing methods to extract gradient information from satellite images of dune fields. Typically, the gradients have a dominant magnitude and orientation. In many cases, the images have two major dominant gradient orientations, for the sunny and shaded side of the dunes. A histogram of the gradient orientations is used to determine the dominant orientation. A threshold is applied to the image based on gradient orientations which agree with the dominant orientation. The contours of the binary image can then be used to determine the dune crest-lines, based on pixel intensity values. Once the crest-lines have been extracted, the morphological properties can be computed. We have tested our approach on a variety of images of linear and crescentic (transverse) dunes and compared dune detection algorithms with manually-digitized dune crest lines, achieving true positive values of 0.57-0.99; and false positives values of 0.30-0.67, indicating that out approach is generally robust.