Abstract

Hyperspectral image (HSI) super-resolution has gained great attention in remote sensing, due to its effectiveness in enhancing the spatial information of the HSI while preserving the high spectral discriminative ability, without modifying the imagery hardware. In this paper, we proposed a novel HSI super-resolution method via a gradient-guided residual dense network (G-RDN), in which the spatial gradient is exploited to guide the super-resolution process. Specifically, there are three modules in the super-resolving process. Firstly, the spatial mapping between the low-resolution HSI and the desired high-resolution HSI is learned via a residual dense network. The residual dense network is used to fully exploit the hierarchical features learned from all the convolutional layers. Meanwhile, the gradient detail is extracted via a residual network (ResNet), which is further utilized to guide the super-resolution process. Finally, an empirical weight is set between the fully obtained global hierarchical features and the gradient details. Experimental results and the data analysis on three benchmark datasets with different scaling factors demonstrated that our proposed G-RDN achieved favorable performance.

Highlights

  • When applied to hyperspectral image (HSI), the SR HSI is designed to improve the spatial resolution of the HSIs while preserving their spectral information, which is of great significance for accurate classification and precise detection [8]

  • Multispectral, accessed on 19 May 2021) contains 32 indoor HSIs of high quality, which were obtained by a cooled CCD camera at 10 nm steps from 400–700 nm

  • A novel guided residual dense network (G-residual dense network (RDN)) was proposed for SR HSIs based on an end-to-end mapping between low- and high-spatial-resolution HSIs under the guidance of gradient information

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Summary

Introduction

The HSI can be treated as a combination of the bands sampled at different wavelengths. It can be formulated as a collection of spectral curves with discrimination ability, in which different curves correspond to different materials [2]. Super-resolution (SR) aims at improving the spatial detail of the input image through some image reconstruction technologies, without modifying the equipment. It has attracted much attention because of its convenience and flexibility. When applied to HSIs, the SR HSI is designed to improve the spatial resolution of the HSIs while preserving their spectral information, which is of great significance for accurate classification and precise detection [8]

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