Hyperspectral Image Super-Resolution Based on Spatial Correlation-Regularized Unmixing Convolutional Neural Network

Xiaochen Lu,Fengde Jia,Dezheng Yang,Junping Zhang

doi:10.3390/rs13204074

Xiaochen Lu, Fengde Jia + Show 2 more

Open Access

https://doi.org/10.3390/rs13204074

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Journal: Remote Sensing	Publication Date: Oct 12, 2021
Citations: 10	License type: CC BY 4.0

Affiliation: Donghua University, Harbin Institute of Technology

Abstract

Super-resolution (SR) technology has emerged as an effective tool for image analysis and interpretation. However, single hyperspectral (HS) image SR remains challenging, due to the high spectral dimensionality and lack of available high-resolution information of auxiliary sources. To fully exploit the spectral and spatial characteristics, in this paper, a novel single HS image SR approach is proposed based on a spatial correlation-regularized unmixing convolutional neural network (CNN). The proposed approach takes advantage of a CNN to explore the collaborative spatial and spectral information of an HS image and infer the high-resolution abundance maps, thereby reconstructing the anticipated high-resolution HS image via the linear spectral mixture model. Moreover, a dual-branch architecture network and spatial spread transform function are employed to characterize the spatial correlation between the high- and low-resolution HS images, aiming at promoting the fidelity of the super-resolved image. Experiments on three public remote sensing HS images demonstrate the feasibility and superiority in terms of spectral fidelity, compared with some state-of-the-art HS image super-resolution methods.

Highlights

Hyperspectral (HS) remote sensing images always suffer from the deficiency of low spatial resolution and mixed pixels, which seriously deteriorate the performance of target detection and recognition in Earth observation areas
For the raised convolutional neural network (CNN)-based approaches, five spatial and spectral measurements, including the spectral angle mapper (SAM), relative dimensionless global error in synthesis (ERGAS), universal image quality index (UIQI), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM), were computed according to (10)–(14), and the average values of five independent Monte Carlo runs are listed in the following sub-sections, by random initialization of network weights
It can be seen that the CNN-based super-resolution approaches generally show higher image fidelity and quality, since, in all cases, the super-resolved images yield lower absolute reconstruction errors (ERGAS) and higher image similarities in both spectral and spatial (PSNR, UIQI, and SSIM) aspects

Summary

Introduction

Hyperspectral (HS) remote sensing images always suffer from the deficiency of low spatial resolution and mixed pixels, which seriously deteriorate the performance of target detection and recognition in Earth observation areas. The demand for potential spatial resolution enhancement has generated considerable interest in the area of remote sensing. HS image super-resolution (SR) techniques can be divided into two categories, namely single image super-resolution and image fusion techniques. The fusion of an HS image with a higher-resolution panchromatic (PAN) or multispectral (MS) image is the prevailing technology, which has been extensively studied since the beginning of this century. This technology is known as hyper-sharpening in the area of remote sensing image processing [1]. Numerous hyper-sharpening approaches have been investigated that can be roughly summarized into the following categories:

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