Deep Convolutional Sparse Coding Network For Pansharpening With Guidance Of Side Information

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.

1. INTRODUCTION

With the launch of satellites, the satellite imagery has played an important role in agriculture, environment and mining. The satellite is usually equipped with two kinds of imaging devices, multispectral (MS) sensors and panchromatic (PAN) sensors. MS images are with high spectral resolution and low spatial resolution, while PAN images are with low spectral resolution and high spatial resolution. To obtain the images with both high spectral and high spatial resolutions, pansharpening that fuses the low resolution MS (LRMS) images and PAN images has received much attention from the remote sensing field.