Supervised-unsupervised combined deep convolutional neural networks for high-fidelity pansharpening-Reference-Cited by-同舟云学术

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Supervised-unsupervised combined deep convolutional neural networks for high-fidelity pansharpening

Published:2023-01 Issue: Volume:89 Page:292-304
ISSN:1566-2535
Container-title:Information Fusion
language:en
Short-container-title:Information Fusion

Author:

Liu Qiang^ORCID,Meng Xiangchao,Shao Feng,Li Shutao

Funder

China Postdoctoral Science Foundation

Ningbo University

Natural Science Foundation of Zhejiang Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Hardware and Architecture,Information Systems,Signal Processing,Software

Reference46 articles.

1. Review of the pansharpening methods for remote sensing images based on the idea of meta-analysis: Practical discussion and challenges;Meng;Inf. Fusion,2019

2. A critical comparison among pansharpening algorithms;Vivone;IEEE Trans. Geosci. Remote Sens.,2015

3. Understanding image fusion;Zhang;Photogramm. Eng. Remote Sens.,2004

4. Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details;Liu;Int. J. Remote Sens.,2000

5. Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods;Otazu;IEEE Trans. Geosci. Remote Sens.,2005

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1. Unsupervised multi-level spatio-spectral fusion transformer for hyperspectral image super-resolution;Optics & Laser Technology;2024-09

2. Towards Robust Pansharpening: A Large-Scale High-Resolution Multi-Scene Dataset and Novel Approach;Remote Sensing;2024-08-08

3. Global-Local Collaborative Learning Network for Optical Remote Sensing Image Change Detection;Remote Sensing;2024-06-27

4. UPGAN: An Unsupervised Generative Adversarial Network Based on U-Shaped Structure for Pansharpening;ISPRS International Journal of Geo-Information;2024-06-26

5. SSTNet: spatial-spectral transformer network for panchromatic sharpening;International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024);2024-06-13

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