An Incremental Boolean Tensor Factorization approach to model Change Patterns of Objects in Images
This work addresses change detection in remote sensing images for applications like urbanization analysis and forest monitoring, but it appears to be an incremental improvement over existing tensor factorization methods.
The paper tackles the problem of interpreting change patterns in spatiotemporal data by proposing an incremental Boolean Tensor Factorization method to model object/class changes and their features. The results show substantial improvements over traditional Boolean Tensor Factorization when evaluated on different datasets.
Change detection process has recently progressed from a post-classification method to an expert knowledge interpretation process of the time-series data. The technique finds applications mainly in remote sensing images and can be utilized to analyze urbanization and monitor forest regions. In this paper, a framework to perform a knowledge based interpretation of the changes/no changes observed in a spatiotemporal domain using tensor based approaches is presented. An incremental approach to Boolean Tensor Factorization method is proposed in this work, which is adopted to model the change patterns of objects/classes as well as their associated features. The framework is evaluated under different datasets to visualize the performance for the dependency factors. The algorithm is also validated in comparison with the tradition Boolean Tensor Factorization method and the results are substantial.