AUTOMATED IDENTIFICATION OF MARTIAN CRATERS USING IMAGE PROCESSING M. Magee 1, C.R. Chapman 2, S.W. Dellenback 1, B. Enke 2, W.J. Merline 2, M.P. Rigney 1 WHY AUTOMATE CRATER DETECTION ?  Process vast image archives  Count/measure craters objectively  First-cut assistance to human analyst  Determine crater morphology parameters  Step toward science-based spacecraft sequencing WHY MARS ?  Over 100,000 MGS images archived  Odyssey THEMIS images streaming in  Complex, varied terrain  Geological history calibrated by craters  Erosion/filling/exhumation processes Southwest Research Institute, 1 Automation and Data Systems Division (San Antonio), 2 Dept. of Space Studies (Boulder) 1. Cross-Correlation Based Template Matching 2. Directional Edge Based Detection 3. Convolution with an Annular Crater Kernel 4. Circular Hough Transform Analysis of Crater Detections, iteration of algorithm parameters TEST IMAGE OF CRATERED TERRAIN Merger of identitications by 4 techniques: AUTOMATICALLY IDENTIFIED CRATERS Template craters extracted from image (scaled to a range of sizes, above) are cross-correlated at every spot in the image. As a crater-area-based scheme, it is especially effective for small, bowl-shaped craters. Gaussian operator sensitive to solar direction is applied to yield gradient strengths (left); edge detector is applied, then thinned; annular kernels of varying radii are applied to find circular craters. The image is convolved with an annular crater kernel (also sensitive to solar illumination direction) to find craters (purple). Edge detections are processed into circular arcs and parameterized into “Hough space” (x,y,r, above); accumulation and arbitration processes yield craters. Take-away Conclusions: Application of multiple detection methods (each with rather strict, high- confidence parameters), and combining the results, maximizes detection of impact craters on planetary images while minimizing “false positives”. The enormous imaging data bases that have been obtained from planetary missions, especially for Mars, contain a wealth of information that will never be measured and synthesized by graduate students and post-docs at currently funding levels. Automated techniques cannot yet fully replace human analysts, but our research demonstrates that pre-processing by a variety of sophisticated techniques can find over ~80% of recognizable craters with few false positives. This can be a “first stage” of image processing that could then be handed off to a human analyst for correction and augmentation, markedly increasing the efficiency of image analysis. Careful comparison of the identified craters above with the original image to the left shows generally good results. But several “craters” may, in fact, be unusual junctures of valleys and other non- impact features. At some point, even the human image interpreter cannot be sure…but we believe that considerable further improvements in the automatic techniques are possible. We are not yet sure that they can eventually replace human analysts or assist autonomous, real-time sequencing decisions on-board spacecraft. This research has been supported by an Internal Research and Development grant from Southwest Research Institute, through its “SwIM Initiative”