A novel semisupervised framework for multiple change detection in hyperspectral images

This paper presents a novel semisupervised framework for detecting multi-class changes in bitemporal hyperspectral images. By taking advantages of the state-of-the-art unsupervised change representation technique and the advanced supervised classifiers, the proposed framework allows the generation of pseudo training samples associated with the no-change and each change class that learned from the multitemporal data and import them into the supervised classifiers. Thus multiple changes can be discriminated from the original or the transformed feature space. The proposed approach was validated on a pair of real bitemporal Hyperion hyperspectral images, and the obtained experimental results confirm its effectiveness in addressing the challenging multi-class change detection task in hyperspectral images.