Joint Convolutional Analysis and Synthesis Sparse Representation-Based Component Substitution Fusion Method for Remote Sensing Images

Aiming at the spectral distortion problem of traditional component substitution-based methods in remote sensing image fusion， an improved component substitution fusion method based on joint convolutional analysis and synthesis sparse representation is proposed. Different from the traditional component substitution methods， this method aims to improve the spatial detail extraction and detail injection strategy in the fusion process to generate remote sensing images with higher spectral and spatial qualities. Firstly， the joint convolutional analysis and synthesis sparse representation algorithm is used to decompose the intensity component and the histogram-matched panchromatic image to obtain the base layer and the detail layer， respectively. Secondly， the average fusion strategy and the choose-max fusion strategy are respectively applied to fuse the base layers and the detail layers， and the spatial detail image is obtained by subtracting the intensity component from the sum of the fused base layer and the fused detail layer. Then， the weighting average is employed to the obtained spatial detail image and the detail image obtained by the traditional component substitution method to acquire the optimal spatial detail image. Finally， the optimal spatial detail image is injected into the upsampled multispectral image to obtain the final fused image. Compared with the other seven fusion methods， the experimental results show that the fused images obtained by the proposed method have lower spectral distortion and higher spatial resolution， and the proposed method provides the best values in terms of the spectral distortion index， the spatial distortion， and the QNR index.