  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.
