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.

遥感技术中的描述吸收量已提供与地形测量的大型数据集有关，与地形和行星地面有关。但是，由于缺乏有效的自动分析工具，这些数据集的科学利用受到了阻碍。该项目旨在开发一个系统，以将地形数据快速，客观和透明的转换为有关土地表面的知识。该项目有两个互补的目标：1）开发一种工具，该工具自主生产的地貌图，模仿传统的，手动派生的地图，以及2）2）开发一种将整个地形场景分类为特征景观类别的工具。映射工具基于面向对象的监督分类原则。提出了许多新颖的解决方案，包括半监督学习，元学习以及包装技术耦合分类和分割，以解决由地形数据的特殊性所带来的挑战。场景分类工具基于信息理论指标，并结合了地形数据集的栅格特征提出的问题的新颖解决方案。IntellectualFuret Turn Project使用机器学习和计算机视觉技术的新型融合来打开新的可能性。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。这项研究的产品将使土地表面地形的定性分析类型：地表的大规模统计比较地面分布。它们也可以应用于表面计量学研究（工业表面的数值表征）。该项目的性质将吸引与来自不同学科的专家（例如计算机科学，遥感，地貌和水文学）的兴趣和合作。这样的链接将扩大每个学科的专业知识基础，并丰富了贡献领域的参与者。