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: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=496982
• 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.

环境模拟往往需要输入不同空间和时间尺度上的详细土地复盖和植被数据。机载和卫星遥感数据在生成土地复盖等分类地图方面发挥着关键作用，因为它能够在大片土地上收集数据。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对地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地图分类精度的影响越来越受到重视。与快速增长的数据可用性相比，分析方法和有效的误差模型的发展明显滞后，以处理日益复杂和数量越来越多的多尺度数据。