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

该奖项是根据2009年美国复苏和再投资法案资助的（公法111-5）。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）推进自动遥感图像分析的最新技术，（2）提高非遥感图像的一个特点是可以进行地理参考，但这一特点尚未被分析界充分利用到广泛的非图像地理空间数据储存库，如地图和称为地名录的地理词典。特别是，这些协会代表了一个丰富的标记数据源需要训练的分析algorithm.The第一部分，这个项目开发了一个框架，学习的外观模型的一个大的一组地理空间对象索引的广泛的地名索引在一个无监督的方式。除了选择特征和模型形式等标准挑战外，这个问题之所以令人感兴趣，是因为目前的地名录只使用一个单一点位置来说明索引对象的空间足迹。第二部分探讨了利用学习模型从影像中更新地名录的方法。这包括估计已知对象实例的空间范围以及检测未知或新的实例。该项目与PI在默塞德的新加州大学建立的空间分析研究中心具有研究和教育协同作用。项目执行人计划开放图像，以便通过项目网站（http：//vision.ucmerced.edu/projects/integrating/）向公众提供评价数据集。

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/).