Automatic change detection is one of the remote sensing applications that has received an increasing attention during the last years. However, fully automatic solutions reach their limitation; on the one hand, it is difficult to design general decision criteria able to select area of changes for images under various acquisition conditions, and on the other hand, the relevance of changes may differ from one user to another. In this letter, we introduce an alternative change detection method based on relevance feedback. The proposed algorithm is iterative and based on a query and answer model that: 1) asks the user questions about the relevance of his targeted changes and 2) according to these answers, updates change detection results. Our method is also based on a new formulation of canonical correlation analysis (CCA), referred to as context-aware CCA, that learns transformations, which map data from different input spaces (related to multitemporal satellite images) into a common latent space, which is sensitive to relevant changes while being resilient to irrelevant ones. These CCA transformations correspond to the optimum of a particular constrained maximization problem that mixes an alignment term with a context-based regularization criterion. The particularity of this novel CCA approach resides in its ability to exploit spatial geometric context resulting into better performances compared with other CCA approaches, as shown in experiments.