Deep Convolutional Sparse Coding Network For Pansharpening With Guidance Of Side Information
TL
TL;DR
This paper proposes a side information partially guided convolutional sparse coding (SCSC) model for pansharpening, generalized as a deep neural network, called as SCSC panshARPening neural network ( SCSC-PNN).
Abstract
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.
References14 items
1
Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion
2
Remote Sensing Image Fusion With Deep Convolutional Neural Network
3
A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening
4
PanNet: A Deep Network Architecture for Pan-Sharpening
5