Deep Convolutional Sparse Coding Network for Pansharpening with Guidance of Side Information
This is an incremental improvement for remote sensing applications, enhancing pansharpening accuracy.
The paper tackled pansharpening in remote sensing by proposing a side information partially guided convolutional sparse coding model, which was generalized into a deep neural network (SCSC-PNN) and shown to outperform 13 other methods on three satellites.
Pansharpening is a fundamental issue in remote sensing field. This paper proposes a side information partially guided convolutional sparse coding (SCSC) model for pansharpening. The key idea is to split the low resolution multispectral image into a panchromatic image related feature map and a panchromatic image irrelated feature map, where the former one is regularized by the side information from panchromatic images. With the principle of algorithm unrolling techniques, the proposed model is generalized as a deep neural network, called as SCSC pansharpening neural network (SCSC-PNN). Compared with 13 classic and state-of-the-art methods on three satellites, the numerical experiments show that SCSC-PNN is superior to others. The codes are available at https://github.com/xsxjtu/SCSC-PNN.