Automatic detection of impact craters on Mars using a modified adaboosting method

The accurate recognition of impact craters is important to analyze and understand the relative dating of Martian surface. Since manually identifying small craters in a deluge of high-resolution Martian images is a tremendous task, a robust automatic detection algorithm of the crater is needed, but subject to lots of uncertainties and low successful detection rates. In this paper, a modified adaboosting approach is developed to detect small size craters on Mars. First, we construct a dual-threshold weak classifier based on the characteristics of the feature value distribution instead of the single threshold classifier. Second, we adjust the criterion of updating weights in the process of training. The small craters on Mars are autamatically detected based on the modified algorithm using the images from the High Resolution Stereo Camera (HRSC) onboard Mars Express with a resolution of 12.5 m/pixel. A high threshold with 0.85 is determined, and the true detection rate of small size craters on Mars is improved by almost 10% when compared to the original method. The true detection rate can be obtained as high as 85% with only 10% false detection rate. Therefore, the modified adaboosting method has greatly improved the detecting performance of the crater and reduced the detection time.

[1]  E. A. Whitaker,et al.  NASA catalogue of lunar nomenclature , 1982 .

[2]  Pedro Pina,et al.  Impact Crater Recognition on Mars Based on a Probability Volume Created by Template Matching , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Brian D. Bue,et al.  Machine cataloging of impact craters on Mars , 2009 .

[4]  Jon Atli Benediktsson,et al.  Automatic Extraction of Ellipsoidal Features for Planetary Image Registration , 2012, IEEE Geoscience and Remote Sensing Letters.

[5]  Brian M. Hynek,et al.  A new global database of Mars impact craters ≥1 km: 2. Global crater properties and regional variations of the simple‐to‐complex transition diameter , 2012 .

[6]  Kim,et al.  Impact crateer detection on optical images and DEMs , 2003 .

[7]  Jefferey A. Shufelt,et al.  Performance Evaluation and Analysis of Monocular Building Extraction From Aerial Imagery , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Sven Loncaric,et al.  Crater Detection Algorithms: A Survey of the First Decade of Intensive Research , 2012 .

[9]  Qiang Yang,et al.  Boosting for transfer learning , 2007, ICML '07.

[10]  Sven Loncaric,et al.  Open framework for objective evaluation of crater detection algorithms with first test-field subsystem based on MOLA data , 2008 .

[11]  H. J. Moore,et al.  Standard techniques for presentation and analysis of crater size-frequency data , 1978 .

[12]  Shuanggen Jin,et al.  New results and questions of lunar exploration from SELENE, Chang’E-1, Chandrayaan-1 and LRO/LCROSS , 2013 .

[13]  N. Barlow.  Crater size-frequency distributions and a revised Martian relative chronology , 1988 .

[14]  Sven Lončarić,et al.  GT-57633 catalogue of Martian impact craters developed for evaluation of crater detection algorithms , 2008 .

[15]  Tomasz F. Stepinski,et al.  Automatic detection of sub-km craters in high resolution planetary images , 2009 .

[16]  Xindong Wu,et al.  Subkilometer crater discovery with boosting and transfer learning , 2011, TIST.

[17]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[18]  Larry H. Matthies,et al.  Crater detection for autonomous landing on asteroids , 2001, Image Vis. Comput..

[19]  Wei Ding,et al.  Detection of Sub-Kilometer Craters in High Resolution Planetary Images Using Shape and Texture Features , 2012 .

[20]  Kenneth L. Tanaka.  The stratigraphy of Mars , 1986 .

[21]  Rie Honda,et al.  Learning to Detect Small Impact Craters , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[22]  Brian D. Bue,et al.  Machine Detection of Martian Impact Craters From Digital Topography Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[24]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[25]  Pedro Pina,et al.  Crater Detection by a Boosting Approach , 2009, IEEE Geoscience and Remote Sensing Letters.