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

遥感技术的进步使有关陆地和行星陆地表面的地形测量的大型数据集成为可能。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。该项目旨在开发一种将地形数据快速、客观和透明地转换为关于地表的知识的系统。该项目有两个相辅相成的目标：1)开发一种工具，自动生成在外观和内容上模仿传统手动地图的地形图；2)开发一种工具，将整个地形场景归类为具有特色的景观类别。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、元学习和结合分类和分割的包装技术，以应对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解决方案。智能优点该项目采用了机器学习和计算机视觉技术的新融合，以打开新的可能性。在构建绘图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一种定性的新型陆地表面地形分析成为可能：对地貌的空间分布进行大规模统计比较。广泛影响成功的测绘和分类工具将产生的影响超出了对自然景观的分析；它们还可以应用于表面计量研究(工业表面的数字表征)。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，并丰富来自贡献领域的参与者。

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.