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Image fusion methods are designed to combine multiple input images with complementary information from a scene that can increase the interpretation capabilities of the objects. The spatial or spectral quality of a fusion method highly depends on the employed fusion method and prior information exploited from input data. Spectral distortion and spatial artifacts are common drawbacks in many image fusion methods. In this study, we propose a model-based image fusion approach using sparse representation (SR) with fuzzy prior knowledge, which incorporates three constraints as prior information for better modeling of the spatial and spectral characteristics of the fusion results. These prior constraints include sparsity of coefficients, patch-wise spectral similarity, and improvement of edge details extracted from a precise unsupervised classification based on a fuzzy inference system (FIS). Edge pixels are vulnerable to spectral distortions due to the spectral mixture of neighboring pixels. Therefore, an objects-based fuzzy classification was used to separate edge pixels from background information and smooth fusion results over edge pixels to prevent spectral distortion. Moreover, in the optimization step of sparse coding, the spectral basis was estimated along with sparse coefficients in an iterative procedure. Results from the proposed fusion approach over real satellite data revealed the promising performance of the proposed SR approach based on its quantitative and qualitative results. Objective and subjective enhancement of the proposed approach compared to other well-known fusion methods included pixel-wise detail preservation and object-based spectral injection to the fused image.
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