Multi-scale classification error and uncertainty modeling on categorical maps from remotely sensed data

遥感数据分类图的多尺度分类误差和不确定性建模

• 批准号: 250400-2008
• 负责人: Chen, DongMei
• 金额: $ 1.09万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=394798
• 关键词: Multi; scale; classification; error; uncertainty

Environmental modeling often requires inputs of detailed land cover and vegetation data at different spatial and temporal scales. Airborne and satellite remotely-sensed data play a key role in generating categorical maps such as land cover because of its capacity of collecting data over broad expanses of land. With widespread use of remote sensing and geographical information system (GIS) technologies, a great deal of earth observation data has been accumulated along with a wide range of spatial, spectral and temporal scales. These developments demand significant changes in the approach of analyzing and integrating these earth observation data. The effects of spatial, spectral and temporal resolutions on the classification accuracy of land cover and other categorical maps have received growing attention. Compared with rapidly growing data availability, there is a clear lag in the development of analysis methods and efficient error models for handling multi-scale data of increasing complexity and quantity.

环境建模通常需要输入不同时空尺度的详细土地覆盖和植被数据。机载和卫星遥感数据由于能够收集大片土地的数据，因此在生成土地覆盖等分类地图方面发挥着关键作用。随着遥感和地理信息系统（GIS）技术的广泛应用，积累了大量的空间、光谱和时间尺度的对地观测数据。这些发展要求分析和整合这些地球观测数据的方法发生重大变化。空间、光谱和时间分辨率对土地覆盖和其他分类图分类精度的影响越来越受到关注。与快速增长的数据可用性相比，处理复杂性和数量不断增加的多尺度数据的分析方法和有效误差模型的开发明显滞后。