Simulation Algorithms for Spatial Pattern Recognition

描述（由申请人提供）：新一代卫星正在成像 以前所未有的空间和光谱分辨率观测地球表面。与 识别与环境暴露有关的当地特征的能力， 高分辨率图像将彻底改变健康风险评估。的 这种潜力的实现关键取决于我们认识到 在这些大图像上的空间模式。该项目将发展快速空间 空模型用于统计模式识别，并将完成4 目标。 (1)实现以数据属性为条件的快速仿真算法， 空间功能; (2)评估项目的可行性，通过评估这些性能 现有高分辨率超光谱图像的算法; (3)在2个商业空间分析中实现模拟算法 软件包; (4)应用该软件和方法来演示该方法的独特性 风险评估的好处。 第一阶段研究将解决前两个目标;目标三和四将 在第二阶段完成，一旦可行性得到证明。的工艺 该项目的科学创新预计将大大提高 我们从高分辨率图像中提取知识的能力。 拟定商业应用： 即将发射的十几颗能够提供高分辨率图像的卫星， 健康研究人员将环境特征与健康联系起来的强大新数据 结果，但现有的软件包不能进行空间分析，这些 非常大的数据集。 从这项研究的快速仿真算法将 被纳入2个商业软件包，提供先进的空间 大图像分析。

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.