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

时间序列遥感环境数据已广泛应用于地球表面变化和生物物理变量的监测。随着遥感技术的快速发展和广泛应用，积累了大量的空间、光谱和时间尺度的航空和卫星数据。这些发展要求对分析和整合这些数据的方法进行重大改变。传统的时间序列遥感数据变化检测方法有很多。理想情况下，变化检测是通过利用来自同一传感器的图像，按时间分开来进行的。然而，许多情况不可避免地会导致理想的时间变化检测情况的结束。许多传统方法对于多光谱和多空间分辨率图像的变化检测效果不佳。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在处理这些日益复杂和数量增加的多时相遥感数据方面存在明显滞后。

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.