CSLP: A novel pansharpening method based on compressed sensing and L-PNN

Abstract

To address spectral distortion and the loss of spatial detail information caused by full-resolution pansharpening, this study proposes an unsupervised method combining compressed sensing (CS) and a deeper attention-based network architecture (L-PNN), namely CSLP. First, in the compressed sensing module, we apply sparse theory for image compression and reconstruction to reduce detail loss and enhance spatial and spectral resolution. Specifically, we use convolutional neural networks to implement this process. It precisely extracts the inherent features of the image to optimize the spectral distortion issues in the pansharpened image and accelerate the model's convergence. Next, we employ the L-PNN module to further optimize and emphasize image features, thereby improving generalizability and stability of the model. The combined processing of these two modules significantly enhances the fidelity of both spatial and spectral resolution in pansharpening. To prove the effectiveness of the proposed method, 19 different methods are compared on four datasets. The results reveal that the proposed method achieves outstanding performance in terms of both subjective evaluation and objective assessment metrics. The code is available at https://github.com/ahsore/CSLP.