Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Multidimensional binary search trees used for associative searching
Communications of the ACM
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining of Topographic Feature from Heterogeneous Imagery and Its Application to Lunar Craters
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Small Impact Craters
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Automatic mapping of valley networks on Mars
Computers & Geosciences
Automated classification of landforms on Mars
Computers & Geosciences
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
Machine Learning Tools for Automatic Mapping of Martian Landforms
IEEE Intelligent Systems
Machine learning for automatic mapping of planetary surfaces
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
Lunar image classification for terrain detection
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
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Mars probes send back to Earth enormous amount of data. Automating the analysis of this data and its interpretation represents a challenging test of significant benefit to the domain of planetary science. In this study, we propose combining terrain segmentation and classification to interpret Martian topography data and to identify constituent landforms of the Martian landscape. Our approach uses unsupervised segmentation to divide a landscape into a number of spatially extended but topographically homogeneous objects. Each object is assigned a 12 dimensional feature vector consisting of terrain attributes and neighborhood properties. The objects are classified, based on their feature vectors, into predetermined landform classes. We have applied our technique to the Tisia Valles test site on Mars. Support Vector Machines produced the most accurate results (84.6% mean accuracy) in the classification of topographic objects. An immediate application of our algorithm lies in the automatic detection and characterization of craters on Mars.