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/).

该奖项由2009年美国恢复和再投资法案(公法111-5)资助。该项目开发了整合图像和非图像地理空间数据的新方法，以(1)促进自动遥感图像分析的最新水平，并反过来，(2)提高非图像存储库的覆盖率和保真度。遥感图像的一个尚未被分析界充分利用的特点是，图像可以与广泛的非图像地理空间数据存储库(如地图和地理词典)进行地理参照。特别是，这些关联代表了训练分析算法所需的标签数据的丰富来源。该项目的第一部分开发了一个框架，用于学习由广泛的地名词典以非监督方式索引的大量地理空间对象的外观模型。除了模型的特征和形式的选择等标准挑战外，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的事实使这一问题变得有趣。探索了同时估计模型参数和已知对象的空间范围的方法。第二部分研究了利用学习的模型从图像中更新地名词典。这包括估计已知物体实例的空间范围以及检测未知或新实例。该项目与PI在新的加州大学默塞德分校建立的空间分析研究中心具有研究和教育协同效应。PI计划开放图像，以便通过项目网站(http://vision.ucmerced.edu/projects/integrating/).公开提供评估数据集