Simulation Algorithms for Spatial Pattern Recognition

描述(申请人提供)：新一代卫星正在成像 地球表面具有前所未有的空间和光谱分辨率。使用 识别与环境暴露相关的本地特征的能力，这 高分辨率图像将给健康风险评估带来革命性变化。这个 这种潜力的实现关键取决于我们认识到 这些大图像上的空间模式。该项目将在空间上快速发展。 用于统计模式识别的空模型，将实现4 目标。 (1)根据数据的属性实现快速模拟算法， 在空间功能上； (2)通过评估这些项目的绩效来评估项目的可行性 现有高分辨率、高光谱图像的算法； (3)在2个商业空间分析中实现了模拟算法。 软件包； (4)应用软件和方法演示了方法和独到之处 风险评估的好处。 第一阶段的研究将针对前两个目标；目标三和目标四将 一旦证明了可行性，将在第二阶段完成。技术上的 预计该项目的科学创新将极大地增强 我们从高分辨率图像中提取知识的能力。 建议的商业应用： 十几颗能够提供高分辨率图像的卫星即将发射，这给了我们 健康研究人员为将环境特征与健康联系起来提供了强大的新数据 结果，但现有的软件包不能对这些结果进行空间分析 超大的数据集。这项研究的快速模拟算法将 集成到2个商业软件包中，提供高级空间 对大图像的分析。

DESCRIPTION (provided by applicant): A new generation of satellites is imaging the earth's surface with unprecedented spatial and spectral resolution. With the ability to identify local features related to environmental exposures, this high-resolution imagery is gong to revolutionize health risk assessment. The realization of this potential depends critically on our ability to recognize spatial patterns on these large images. This project will develop fast spatial null models for use in statistical pattern recognition, and will accomplish 4 aims. (1) Implement fast simulation algorithms conditioned on properties of the data, and on spatial functions; (2) Assess project feasibility by evaluating the performance of these algorithms on existing high-resolution, hyperspectral imagery; (3) Implement the simulation algorithms in 2 commercial spatial analysis software packages; (4) Apply the software and methods to demonstrate the approach and unique benefits for risk assessment. The phase 1 research will address the first two aims; aims three and four will be accomplished in phase 2 once feasibility is demonstrated. The technologic and scientific innovations from this project are expected to greatly enhance our ability to extract knowledge from high resolution imagery. PROPOSED COMMERCIAL APPLICATION: The imminent launch of over a dozen satellites capable of high-resolution imagery is giving health researchers powerful new data for relating environmental features to health outcomes, but existing software packages cannot undertake spatial analysis of these extraordinarly large data sets. The fast simulation algorithms from this research will be incorporated into 2 commercial software packages, providing advanced spatial analysis for large imagery.