III-CXT-Small: Collaborative Research: Automatic Geomorphic Mapping and Analysis of Land Surfaces Using Pattern Recognition

III-CXT-Small：协作研究：利用模式识别自动地貌测绘和地表分析

• 批准号: 0812271
• 负责人: Tomasz Stepinski
• 金额: $ 28.49万
• 依托单位: Universities Space Research Association
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2011-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812271&HistoricalAwards=false
• 关键词: III; CXT; Small; Collaborative; Research

DescriptionAdvances in remote sensing techniques have made available large datasets of topographic measurements pertaining to terrestrial and planetary land surfaces. However, the scientific utilization of these datasets is hampered by a lack of tools for effective automated analysis. This project seeks to develop a system for fast, objective and transparent conversion of topographic data into knowledge about land surfaces. The project has two complementary goals: 1) to develop a tool that autonomously produces geomorphic maps mimicking traditional, manually derived maps in their appearance and content, and 2) to develop a tool that classifies entire topographic scenes into characteristic landscape categories. The mapping tool is based on the object-oriented supervised classification principle. A number of novel solutions, including semi-supervised learning, meta-learning, and a wrapping technique coupling classification and segmentation, are proposed to address challenges posed by the specificity of topographic data. The scene classification tool is based on information-theoretic metrics and incorporates novel solutions to problems posed by the raster character of topographic datasets.Intellectual MeritThe project employs a novel fusion of machine learning and computer vision techniques to open new possibilities. In the process of constructing the mapping and classifying tools, novel machine learning methodologies will be developed and tested. The products of this research will enable a qualitatively new type of analysis of land surface topography: the large scale statistical comparison of spatial distribution of landforms.Broad ImpactSuccessful mapping and classifying tools will have impact beyond the analysis of natural landscapes; they can be also be applied to the study of surface metrology (the numerical characterization of industrial surfaces). The nature of this project will attract interest and collaboration with specialists from diverse disciplines, such as computer science, remote sensing, geomorphology and hydrology. Such links will broaden the base of expertise for each discipline, as well as enrich participants from contributing domains.

描述遥感技术的进步已经提供了与陆地和行星陆地表面有关的地形测量的大型数据集。然而，由于缺乏有效的自动化分析工具，这些数据集的科学利用受到阻碍。该项目旨在开发一个系统，将地形数据快速、客观和透明地转换为有关地表的知识。该项目有两个互补的目标：1）开发一种自动生成地貌图的工具，在外观和内容上模仿传统的手动生成的地图；2）开发一种将整个地形场景分类为特征景观类别的工具。该映射工具基于面向对象的监督分类原理。提出了许多新颖的解决方案，包括半监督学习、元学习以及耦合分类和分割的包装技术，以解决地形数据的特殊性带来的挑战。场景分类工具基于信息论度量，并结合了针对地形数据集的栅格特征所带来的问题的新颖解决方案。智力优点该项目采用机器学习和计算机视觉技术的新颖融合来开辟新的可能性。在构建映射和分类工具的过程中，将开发和测试新颖的机器学习方法。这项研究的产品将实现一种新型的地表地形分析：地貌空间分布的大规模统计比较。广泛的影响成功的绘图和分类工具将产生超出自然景观分析的影响；它们还可以应用于表面计量学（工业表面的数值表征）的研究。该项目的性质将吸引来自计算机科学、遥感、地貌学和水文学等不同学科的专家的兴趣和合作。这种联系将扩大每个学科的专业知识基础，并丰富贡献领域的参与者。