Proximal PanNet: A Model-Based Deep Network for Pansharpening
This work addresses the issue of limited interpretability in deep learning-based pansharpening methods, offering a hybrid approach that could enhance performance for remote sensing applications, though it appears incremental by combining existing techniques.
The paper tackles the problem of pansharpening, which fuses low-resolution multispectral and high-resolution panchromatic images to produce high-resolution multispectral images, by proposing Proximal PanNet, a model-based deep network that improves interpretability and achieves better performance than advanced methods on benchmark datasets.
Recently, deep learning techniques have been extensively studied for pansharpening, which aims to generate a high resolution multispectral (HRMS) image by fusing a low resolution multispectral (LRMS) image with a high resolution panchromatic (PAN) image. However, existing deep learning-based pansharpening methods directly learn the mapping from LRMS and PAN to HRMS. These network architectures always lack sufficient interpretability, which limits further performance improvements. To alleviate this issue, we propose a novel deep network for pansharpening by combining the model-based methodology with the deep learning method. Firstly, we build an observation model for pansharpening using the convolutional sparse coding (CSC) technique and design a proximal gradient algorithm to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as Proximal PanNet, by learning the proximal operators using convolutional neural networks. Finally, all the learnable modules can be automatically learned in an end-to-end manner. Experimental results on some benchmark datasets show that our network performs better than other advanced methods both quantitatively and qualitatively.