Algorithms for clustering data
Algorithms for clustering data
High-resolution landform classification using fuzzy k-means
Fuzzy Sets and Systems - Special issue on Uncertainty in geographic information systems and spatial data
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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
Automatic recognition of landforms on mars using terrain segmentation and classification
DS'06 Proceedings of the 9th international conference on Discovery Science
Regional morphometric and geomorphologic mapping of Martian landforms
Computers & Geosciences
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We propose a numerical method for classification and characterization of landforms on Mars. The method provides an alternative to manual geomorphic mapping of the Martian surface. Digital elevation data is used to calculate several topographic attributes for each pixel in a landscape. Unsupervised classification, based on the self-organizing map technique, divides all pixels into mutually exclusive and exhaustive landform classes on the basis of similarity between attribute vectors. The results are displayed as a thematic map of landforms and statistics of attributes are used to assign semantic meaning to the classes. This method is used to produce a geomorphic map of the Terra Cimmeria region on Mars. We assess the quality of the automated classification and discuss differences between results of automated and manual mappings. Potential applications of our method, including crater counting, landscape feature search, and large scale quantitative comparisons of Martian surface morphology, are identified and evaluated.