III:Small:RUI:Integrating Image and Non-Image Geospatial Data

III:Small:RUI:集成图像和非图像地理空间数据

• 批准号: 0917069
• 负责人: Shawn Newsam
• 金额: $ 39.67万
• 依托单位: University of California - Merced
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0917069&HistoricalAwards=false
• 关键词: III; Small; RUI; Integrating; Image

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This project develops novel methods for integrating image and non-image geospatial data to (1) advance the state-of-the-art of automated remote sensed image analysis and, in turn, (2) improve the coverage and fidelity of the non-image repositories.A characteristic of remote sensed imagery which has not been sufficiently exploited by the analysis community is that the images can be georeferenced to extensive repositories of non-image geospatial data such as maps and geographic dictionaries termed gazetteers. In particular, these associations represent a rich source of labeled data needed to train the analysis algorithms.The first part of this project develops a framework for learning appearance models for a large set of geospatial objects indexed by an extensive gazetteer in an unsupervised fashion. Besides the standard challenges such as choice of features and form of the model, this problem is made interesting by the fact that current gazetteers only specify the spatial footprint of indexed objects using a single point location. Methods are explored for simultaneously estimating the model parameters and the spatial extents of the known objects.The second part investigates using the learned models to update the gazetteer from imagery. This includes estimating the spatial extents of known object instances as well as detecting unknown or novel instances.The project has research and educational synergies with a Spatial Analysis Research Center that the PI is establishing at the new University of California at Merced. The PI plans to open imagery so that evaluation datasets can be made publicly available through the project website (http://vision.ucmerced.edu/projects/integrating/).

该奖项是根据2009年的《美国恢复和再投资法》（公法111-5）资助的。该项目开发了将图像和非图像地理空间数据整合到（1）自动远程感官图像分析的最新时间的新方法社区是，这些图像可以被授予非图像地理空间数据的广泛存储库，例如地图和所谓的地理词典。特别是，这些关联代表了培训分析算法所需的标记数据的丰富来源。该项目的第一部分为学习外观模型开发了一个框架，用于以无监督的方式由广泛的Gazetteer索引的大型地理空间对象。除了标准挑战（例如特征的选择和模型形式）之外，由于当前的地名词典仅使用单点位置指定索引对象的空间足迹，因此使此问题变得有趣。探索了同时估算已知对象的模型参数和空间扩展的方法。第二部分使用学习的模型研究以从图像中更新Gazetteer。这包括估计已知对象实例的空间范围以及检测未知或新颖实例。该项目具有与空间分析研究中心的研究和教育协同作用，该研究中心是PI在加利福尼亚州新大学Merced建立的。 PI计划打开图像，以便可以通过项目网站（http://vision.ucmerced.edu/projects/integrating/）公开获得评估数据集。