Discriminant canonical correlation analysis for interactive satellite image change detection

Satellite image change detection consists in defining criteria that localize relevant changes while discard irrelevant ones. This problem is known to be challenging due to several factors including illumination changes, occlusion and also subjectivity of users. In this paper, we introduce a novel change detection algorithm based on relevance feedback and a new variant of canonical correlation analysis (CCA), referred to as discriminant CCA. The contribution of this work includes: i) a relevance feedback method that adapts change detection criteria to content and acquisition conditions of satellite images as well as the user´s intention, and ii) a novel discriminant CCA approach that further enhances the performance of relevance feedback by designing accurate and resilient change detection criteria. Conducted change detection experiments show a clear gain of our discriminant CCA approach compared to related methods.