An Analysis of Feature Detectors Usage in the Task of Lunar Crater Detection

Author(s):  
Nataliia Kuzmenko ◽  
Ivan Ostroumov
Author(s):  
Y. Wang ◽  
B. Wu

Abstract. Impact craters are the predominant geomorphological features on the lunar surface. They can be studied to infer the ages of the lunar surfaces, the generation processes of the geological units, and the sequences of its geological events. Lunar crater-related research is dependent on crater records, which are usually stored in the form of crater catalogues. In the past, various efforts have been dedicated to generating global lunar crater catalogues. All published global catalogues, however, either contain only relatively large craters or lack 3D morphological information. This paper first presents approaches for automatic crater detection and the extraction of crater morphological information. The approaches have been performed on the lunar global datasets, e.g., digital elevation models (DEMs) and images, resulting in a global catalogue of lunar craters. To guarantee the reliability of the crater detection results, intensive manual-checking processes have been performed to improve the correctness and completeness of the catalogue. The generated global catalogue contains entries on 1.31 million lunar craters. It extends the existing global catalogues to craters with diameters ≥1 km and enriched with 3D morphological information of craters. Global analyses of craters were conducted based on the newly generated catalogue, including the analysis of crater density and depth-to-diameter ratio. We re-examined the previously observed distributions and patterns to show its fidelity and further explored other global relationships, which have not been discovered in previous research. The results updated the clues on impact cratering process and terrain differences. The developed global catalogue of lunar craters can be utilised for different applications by the research community, and the relevant research will help to enrich the literature and facilitate the advancement of crater-related planetary science.


2021 ◽  
Vol 1 (1) ◽  
pp. 49-63
Author(s):  
Haingja Seo ◽  
Dongyoung Kim ◽  
Sang-Min Park ◽  
Myungjin Choi

1969 ◽  
Author(s):  
R.P. Snyder ◽  
E.B. Ekren ◽  
G.L. Dixon
Keyword(s):  

Author(s):  
Joel Z. Leibo ◽  
Tomaso Poggio

This chapter provides an overview of biological perceptual systems and their underlying computational principles focusing on the sensory sheets of the retina and cochlea and exploring how complex feature detection emerges by combining simple feature detectors in a hierarchical fashion. We also explore how the microcircuits of the neocortex implement such schemes pointing out similarities to progress in the field of machine vision driven deep learning algorithms. We see signs that engineered systems are catching up with the brain. For example, vision-based pedestrian detection systems are now accurate enough to be installed as safety devices in (for now) human-driven vehicles and the speech recognition systems embedded in smartphones have become increasingly impressive. While not being entirely biologically based, we note that computational neuroscience, as described in this chapter, makes up a considerable portion of such systems’ intellectual pedigree.


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