Abstract
ABSTRACTImpact craters help scientists to understand the geological history of planetary bodies. The aim of this paper is to improve the existing methodology for impact craters detection in images of planetary surfaces using a new approach based on morphological image processing (MIP). The improved methodology uses MIP followed by template matching based on fast Fourier transform (FFT). In this phase, a probability volume is generated based on the correlation between templates and images. The analysis of this probability volume allows the detection of different size of impact craters. We have applied the improved methodology to detect impact craters in a set of images from Thermal Emission Imaging System onboard the 2001 Mars Odyssey Space probe. The improved methodology has achieved a crater detection rate of 92.23% which can be considered robust, since results were obtained based on geomorphological features, different illumination conditions and low spatial resolution. The achieved results proved the viability of using MIP and template matching by FFT, to detect impact craters from planetary surfaces.
Highlights
Exploration of the solar system has been intensified in recent years
Equation (14) considers the false negative (FN) pixels that are the number of craters present in Ground Truth (GT) which was not detected by methodology
We have presented an improved methodology for automatic impact crater detection on Mars, based on morphological image processing (MIP) and template matching
Summary
Exploration of the solar system has been intensified in recent years. More than 100 probes were sent in the space for planetary exploration; recent probes were sent to Mars. Besides Earth, Mars has been the most studied planet, and so huge and wide-range space-imagery data-sets dedicated to Mars are available (Alves & Vaz 2007). These images have shown the patterns, distributions and morphological structures that compose the Martian surface. Counting craters in remotely sensed images is the only tool that provides relative dating of remote planetary surfaces (Urbach & Stepinski 2009) These investigations are monotonous and time-consuming tasks for humans, because they need to examine manually a wide range of information available in the constantly growing imagery data-sets. There is the need of efficient and reliable algorithms to detect these structures automatically
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