Methods and uncertainty modeling for land cover change detection from multi-resolution remotely sensed data

多分辨率遥感数据土地覆盖变化检测方法和不确定性建模

• 批准号: 250400-2013
• 负责人: Chen, DongMei
• 金额: $ 2.4万
• 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: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=525047
• 关键词: Methods; uncertainty; modeling; land; cover

Time series remotely sensed environmental data has been widely used in monitoring changes in earth surfaces and biophysical variables. With rapid development and widespread use of remote sensing technologies, a great deal of airborne and satellite data with a wide range of spatial, spectral and temporal scales has been accumulated. These developments demand significant changes in the approach of analyzing and integrating these data. Many traditional change detection methods in time series remotely sensed data have been developed in the past. Change detection is ideally conducted by utilizing images, separated by time, from the same sensor. However, many conditions will inevitably bring an end to the ideal temporal change detection situation. Many traditional approaches do not work well in change detection involving images with multiple spectral and spatial resolutions. Compared with rapidly growing data availability, there is a clear lag in the development of efficient change detection models and multi-scale analysis framework for handling these multi-temporal remotely sensed data of increasing complexity and quantity.

时间序列遥感环境数据已被广泛用于监测地球表面和生物物理变量的变化。 随着遥感技术的迅速发展和广泛应用，积累了大量的空间、光谱和时间尺度广泛的航空和卫星数据。这些发展要求对分析和整合这些数据的方法进行重大变革。传统的时间序列遥感数据变化检测方法已有很多发展。理想情况下，通过利用来自同一传感器的按时间分离的图像来进行变化检测。然而，许多条件将不可避免地结束理想的时间变化检测情况。许多传统的方法不能很好地工作在变化检测涉及多光谱和空间分辨率的图像。与快速增长的数据可用性相比，有效的变化检测模型和多尺度分析框架的开发明显滞后，以处理这些日益复杂和数量的多时相遥感数据。