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Deep Convolutional Sparse Coding Network for Pansharpening with Guidance of Side Information

2021-03-10 09:06:33
Shuang Xu, Jiangshe Zhang, Kai Sun, Zixiang Zhao, Lu Huang, Junmin Liu, Chunxia Zhang
arXiv_CV
       
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 this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2103.05946

PDF

https://arxiv.org/pdf/2103.05946.pdf

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