Bi-Temporal Texton Forest for Land Cover Transition Detection on Remotely Sensed Imagery

With the advancement of machine learning, classification methods have been increasingly used in change (or transition) detection. The texton forest (TF)-based method has received increasing research attention because of its speed, good generalization characteristics, stability, and especially its ability to capture spatial contextual information. In this paper, we propose a TF-based method for transition detection in remotely sensed imagery. We investigate a maximal joint-information gain criterion for random forests to better capture combined information in the bi-temporal images in transition detection, which is implemented by a natural extension of binary-trees in traditional methods into a quad-decision tree structure. We also utilize color-invariant gradient as a feature to help alleviate the impact of difference in imaging conditions on bi-temporal transition detection. The experimental results for transition detection show that our bi-temporal TF classifier achieves better performance than a post-classification comparison method and several other alternative methods.