This paper deals with a fusion-based change detection (CD) framework for multi-band images with different spatial and spectral resolutions. The first step of the considered CD framework consists in fusing the two observed images. The resulting fused image is subsequently spatially or spectrally degraded to produce two pseudo-observed images, with the same resolutions as the two observed images. Finally, CD can be performed through a pixel-wise comparison of the pseudo-observed and observed images since they share the same resolutions. Obviously, fusion is a key step in this framework. Thus, this paper proposes to quantitatively and qualitatively compare state-of-the-art fusion methods, gathered into four main families, namely component substitution, multi-resolution analysis, unmixing and Bayesian, with respect to the performance of the whole CD framework evaluated on simulated and real images.