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: 0812372
• 负责人: Ricardo Vilalta
• 金额: $ 15.86万
• 依托单位: University of Houston
• 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 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812372&HistoricalAwards=false
• 关键词: III; CXT; Small; Collaborative; Research

Advances 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 Merit The 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 Impact Successful 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.

遥感技术的进步已经提供了与陆地和行星陆地表面有关的大量地形测量数据集。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了阻碍。该项目旨在开发一种系统，将地形数据快速、客观和透明地转换为有关陆地表面的知识。该项目有两个互补的目标：1)开发一种工具，可以自动生成地貌图，模仿传统的手动衍生地图的外观和内容；2)开发一种工具，将整个地形场景划分为特征景观类别。该映射工具基于面向对象的监督分类原理。提出了一些新的解决方案，包括半监督学习、元学习和耦合分类和分割的包装技术，以解决地形数据特殊性带来的挑战。场景分类工具基于信息理论度量，并结合了由地形数据集的栅格特征带来的问题的新解决方案。该项目采用了机器学习和计算机视觉技术的新颖融合，以开辟新的可能性。在构建映射和分类工具的过程中，将开发和测试新的机器学习方法。这项研究的成果将使陆地表面地形的一种新的定性分析成为可能：地形空间分布的大规模统计比较。广泛影响成功的制图和分类工具的影响将超出对自然景观的分析；它们也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不同学科的专家的兴趣和合作，如计算机科学、遥感、地貌学和水文学。这种联系将扩大每个学科的专门知识基础，并丰富来自贡献领域的参与者。