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Machine Vision and Advanced Image Processing in Remote Sensing (Third Edition)

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Special Issue Information

Dear Colleagues,

With sensor technology development, we can acquire more remote sensing images from the sensors installed on satellites, aircraft, etc. By acquiring these remote sensing data, people can observe the objects clearly and discover the ground's underlying materials, which opens a new window for us to understand the world. In particular, machine vision and image processing in remote sensing have recently been widely researched. We believe this trend will continue expectantly in the future; thus, the advancement of excellent approaches and techniques for machine vision and image processing in remote sensing plays a more critical role. In this Special Issue, we intend to collect several papers centred around machine vision and advanced image processing in remote sensing. With this Special Issue, we hope to promote machine vision and image processing on several remote sensing tasks, e.g., fusion, restoration, classification, unmixing, detection, segmentation, etc. For the methodology, there are no limitations if your approach can effectively deal with the mentioned tasks.

Prof. Dr. Liang-Jian Deng
Dr. Gemine Vivone
Dr. Xiangyong Cao
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

machine vision in remote sensing
image processing in remote sensing
multispectral and hyperspectral images and their processing
algorithms and modelling in remote sensing
data fusion
image restoration
multispectral and hyperspectral image denoising
hyperspectral image classification
diffusion based remote sensing image processing
other vision and image tasks in remote sensing

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Research

18 pages, 6989 KiB

Open AccessArticle

A Deep Unfolding Network for Multispectral and Hyperspectral Image Fusion

by Bihui Zhang, Xiangyong Cao and Deyu Meng

Remote Sens. 2024, 16(21), 3979; https://doi.org/10.3390/rs16213979 - 26 Oct 2024

Viewed by 515

Abstract

Multispectral and hyperspectral image fusion (MS/HS fusion) aims to generate a high-resolution hyperspectral (HRHS) image by fusing a high-resolution multispectral (HRMS) and a low-resolution hyperspectral (LRHS) images. The deep unfolding-based MS/HS fusion method is a representative deep learning paradigm due to its excellent performance and sufficient interpretability. However, existing deep unfolding-based MS/HS fusion methods only rely on a fixed linear degradation model, which focuses on modeling the relationships between HRHS and HRMS, as well as HRHS and LRHS. In this paper, we break free from this observation model framework and propose a new observation model. Firstly, the proposed observation model is built based on the convolutional sparse coding (CSC) technique, and then a proximal gradient algorithm is designed to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as MHF-CSCNet, where the proximal operators are learned using convolutional neural networks. Finally, all trainable parameters can be automatically learned end-to-end from the training pairs. Experimental evaluations conducted on various benchmark datasets demonstrate the superiority of our method both quantitatively and qualitatively compared to other state-of-the-art methods. Full article

(This article belongs to the Special Issue Machine Vision and Advanced Image Processing in Remote Sensing (Third Edition))

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