Target-based multiple-scale change detection from time series remotely sensed environmental data

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Remotely sensed (RS) data has been widely used in environmental monitoring and health applications. Linking the spectral and pattern changes among multi-temporal RS data sets to changes in surface features and biophysical variables has long been an important application of remote sensing technology. Although various change detection methods have been developed, they are mainly based on image analysis on multi-temporal images acquired from the same or similar sensor. With the rapid development of remote sensing technology and the increasing availability of large multi-scale and multi-sensor earth observation data sets, change detection has been forced to incorporate those data sets, and more efficient change detection methods and robust tools are needed to meet the challenge. This research is based on the innovative concept of target-based change detection we have proposed and is aimed to improve the accuracy of change detection by (i) evaluating the performance of different spectral unmixing and sub-pixel methods for mapping targeted land cover and its change across different scales; (ii) developing intelligent change detection procedures through data mining and deep learning from multi-spectral, multi-scale, multi-temporal RS data cubes for different targets on different landscapes; and (iii) developing an evaluation framework for hybrid change detection when multi-scale and multi-sensor temporal images are involved. The long-term goal of the proposed research is to provide theoretical frameworks and robust tools for better understanding the error and uncertainty in change detection and detecting patterns and changes from the large amount of multi-sensor time series RS images and products. The innovative change detection methods and evaluation framework from the proposed research will significantly contribute to the maximum use of RS data and products in many applications. Within the proposed budget and research scope at least ten graduate students and ten undergraduate students will be trained.