Abstract: In this paper, a novel framework for remote sensing image annotation is proposed based on spatial constrained multi-feature joint sparse coding to extend the sparse representation-based classifier to multi-feature framework. The proposed framework imposed an l1,2 mixed-norm regularization on encode coefficients of multiple features. The regularization encourages the coefficients to share a common sparsity pattern, which preserves the cross-feature information. Inspired by the success of dictionary learning, a novel dictionary learning model is proposed to promote the performance of multi-feature joint sparse coding, while the cross-feature association is preserved by consistent transformation constraint. In addition, spatial dependencies between patches of remote sensing images are useful for annotation task but usually ignored of insufficiently exploited. In this paper, a spatial relation constrained classifier is designed to incorporate spatial coherence into multi-feature sparse coding model to annotate images more precisely. Experiments on public dataset and large satellite images show the discriminative power and effectiveness of the proposed framework.