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Detection of sub-kilometer craters in high resolution planetary images using shape and texture features
- Source :
- Advances in Space Research. 49:64-74
- Publication Year :
- 2012
- Publisher :
- Elsevier BV, 2012.
-
Abstract
- Counting craters is a paramount tool of planetary analysis because it provides relative dating of planetary surfaces. Dating surfaces with high spatial resolution requires counting a very large number of small, sub-kilometer size craters. Exhaustive manual surveys of such craters over extensive regions are impractical, sparking interest in designing crater detection algorithms (CDAs). As a part of our effort to design a CDA, which is robust and practical for planetary research analysis, we propose a crater detection approach that utilizes both shape and texture features to identify efficiently sub-kilometer craters in high resolution panchromatic images. First, a mathematical morphology-based shape analysis is used to identify regions in an image that may contain craters; only those regions – crater candidates – are the subject of further processing. Second, image texture features in combination with the boosting ensemble supervised learning algorithm are used to accurately classify previously identified candidates into craters and non-craters. The design of the proposed CDA is described and its performance is evaluated using a high resolution image of Mars for which sub-kilometer craters have been manually identified. The overall detection rate of the proposed CDA is 81%, the branching factor is 0.14, and the overall quality factor is 72%. This performance is a significant improvement over the previous CDA based exclusively on the shape features. The combination of performance level and computational efficiency offered by this CDA makes it attractive for practical application.
- Subjects :
- Atmospheric Science
Boosting (machine learning)
Computer science
business.industry
Aerospace Engineering
High resolution
Astronomy and Astrophysics
Pattern recognition
Mars Exploration Program
Mathematical morphology
Panchromatic film
Geophysics
Impact crater
Image texture
Space and Planetary Science
General Earth and Planetary Sciences
Artificial intelligence
business
Shape analysis (digital geometry)
Remote sensing
Subjects
Details
- ISSN :
- 02731177
- Volume :
- 49
- Database :
- OpenAIRE
- Journal :
- Advances in Space Research
- Accession number :
- edsair.doi...........15083ccae4603ac33882aeb12a926e7d