Image analysis algorithm for detection and measurement of Martian sand grains

Abstract

Image analysis methods are commonly employed to determine the size and shape of particles. Although commercial and noncommercial tools enable detection and measurement of grains from images, they do not provide good results in the case of images acquired during extensive in situ Martian investigations. Within the confines of the Mars Exploration Rover (MER) mission and the Mars Science Laboratory (MSL) mission thousands of images of sand grains were captured, and hitherto, they are the only source of ground-truth data on Martian sand particles. Therefore, a new approach is proposed to analyze such images. The semiautomatic algorithm allows fast detection and measurement of the size and shape of Martian grains from images obtained by the Microscopic Imager (MI) and the Mars Hand Lens Imager (MAHLI). The method was evaluated on 76 images of terrestrial and Martian deposits. The results for the terrestrial samples were compared to those from sieve analysis, as well as with ImageJ and Malvern Morphologi G3 systems. The method provides similar results to those from the other methods. It does not have any limitation on the size of grains, and permits separation of touching particles.

Re‐Evaluation of Large Martian Ripples in Gale Crater: Granulometric Evidence for an Impact Mechanism and Terrestrial Analogues

“…All theories of bedform development rest on data, and the GSD is of particular importance in this case. A number of methods exist for extracting GSDs from images, including manual digitizing (e.g., Ibbeken & Schleyer, 1986), automated pixel‐based methods (e.g., Buscombe, 2013), and automated segmentation methods (e.g., Butler et al., 2001; Kozakiewicz, 2018). Methods for extracting grain size data from rover images used to date have been some form of grid‐by‐numbers (e.g., Jerolmack et al., 2006 or Ewing et al., 2017 methodology adapted from Kellerhals & Bray, 1971) or clustered/targeted sampling strategy (Banham et al., 2018; Weitz et al., 2018) paired with a manually digitized line of the user‐estimated short and/or long axis of grains as they appear in the image.…”

Morphometric Parameters of Krumbein Grain Shape Charts—A Critical Approach in Light of the Automatic Grain Shape Image Analysis

“…In addition, Resentini et al [31] stated that, despite technological progress, an entirely satisfactory automatic method for determining the angularity of detrital particles had not yet been found. Despite this, the Morphologi G3SE apparatus is often used in sedimentological studies of grains physically placed in the apparatus [28,32,[48][49][50] and images of mineral particles [30,51,52].…”

Bullet Impact Detection in Silhouettes Using Mask R-CNN

“…To avoid distorting the image, the same percentage is applied to both sides of the image and that percentage is calculated by reference to the width of the image. Just like [21], [22], [41]- [43], the resizing is part of the object detection techniques.…”