Lourenço Bandeira

TL;DR: This paper presents a methodology that brings together a number of techniques in the fields of image processing and pattern recognition with the purpose of achieving the automated detection of impact craters on images of planetary surfaces.

TL;DR: A crater detection approach that utilizes both shape and texture features to identify efficiently sub-kilometer craters in high resolution panchromatic images is proposed, which is a significant improvement over the previous CDA based exclusively on the shape features.

TL;DR: An integrated framework on autodetection of subkilometer craters with boosting and transfer learning that can achieve an F1 score above 0.85, a significant improvement over the other crater detection algorithms.

TL;DR: The new MA130301GT catalogue presented in this paper is the result of the overall merger of MA115225GT and MA75919T and contains 85 783 such craters, while the smallest D is 0.924 km, a considerable improvement in comparison with the completeness of the Rodionova, Barlow, and Stepinski catalogues.

TL;DR: In this article, the authors proposed an integrated CDA, consisting of: (1) utilization of DEM (digital elevation map)-based CDA; (2) utilisation of an optical-based CDA; (3) re-projection of used datasets and crater coordinates from normal to rotated view and back; (4) correction of the brightness and contrast of a used optical image; and (5) tile generation for the optical based CDA and an assembling of results with an elimination of multiple detections, in combination with a pyramid approach down to the resolution of