基于DEM和地貌计量的月球形貌类型自动制图（英文）

发布时间：2018-03-27 23:36

本文选题：automatic　切入点：classification　出处：《Journal of Geographical Sciences》2017年11期

【摘要】：Developing approaches to automate the analysis of the massive amounts of data sent back from the Moon will generate significant benefits for the field of lunar geomorphology.In this paper,we outline an automated method for mapping lunar landforms that is based on digital terrain analysis.An iterative self-organizing(ISO)cluster unsupervised classification enables the automatic mapping of landforms via a series of input raster bands that utilize six geomorphometric parameters.These parameters divide landforms into a number of spatially extended,topographically homogeneous segments that exhibit similar terrain attributes and neighborhood properties.To illustrate the applicability of our approach,we apply it to three representative test sites on the Moon,automatically presenting our results as a thematic landform map.We also quantitatively evaluated this approach using a series of confusion matrices,achieving overall accuracies as high as 83.34% and Kappa coefficients(K)as high as 0.77.An immediate version of our algorithm can also be applied for automatically mapping large-scale lunar landforms and for the quantitative comparison of lunar surface morphologies.
[Abstract]:Developing approaches to automate the analysis of the massive amounts of data sent back from the Moon will generate significant benefits for the field of lunar geomorphology.In this paper,we outline an automated method for mapping lunar landforms that is based on digital terrain analysis.An iterative self-organizing(ISO)cluster unsupervised classification enables the automatic mapping of landforms via a series of input raster bands that utilize six geomorphometric parameters.These parameters divide landforms into a number of spatially extended,topographically homogeneous segments that exhibit similar terrain attributes and neighborhood properties.To illustrate the applicability of our approach,we apply it to three representative test sites on the Moon,automatically presenting our results as a thematic landform map.We also quantitatively evaluated this approach using a series of confusion matrices,achieving overall accuracies as high as 83.34% and Kappa coefficients(K)as high as 0.77.An immediate version of our algorithm can also be applied for automatically mapping large-scale lunar landforms and for the quantitative comparison of lunar surface morphologies.
【作者单位】： School
【基金】：National Natural Science Foundation of China,No.41571388 National Special Basic Research Fund,No.2015FY210500
【分类号】：P184

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