Pansharpening
21 papers with code • 10 benchmarks • 4 datasets
As a remote sensing image processing task, Pan-sharpening aims to increase the spatial resolution of the low-resolution multispectral image with the guidance of the corresponding panchromatic image.
Benchmarks
These leaderboards are used to track progress in Pansharpening
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WorldView-3 PAirMax
PanCollection
Most implemented papers
Full-resolution quality assessment for pansharpening
Both reference-based and no-reference indexes present critical shortcomings which motivate the community to explore new solutions.
HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening
wgcban/HyperTransformer
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CVPR 2022
Existing pansharpening approaches neglect using an attention mechanism to transfer HR texture features from PAN to LR-HSI features, resulting in spatial and spectral distortions.
Pansharpening via Frequency-Aware Fusion Network with Explicit Similarity Constraints
The process of fusing a high spatial resolution (HR) panchromatic (PAN) image and a low spatial resolution (LR) multispectral (MS) image to obtain an HRMS image is known as pansharpening.
U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image Fusion
psrben/u2net
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The U2Net utilizes a spatial U-Net and a spectral U-Net to extract spatial details and spectral characteristics, which allows for the discriminative and hierarchical learning of features from diverse images.
Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening Framework
matciotola/fast-z-pnn
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MDPI Remote Sensing 2023
In this work, we propose a variant of this method with an effective target-adaptation scheme that allows for the reduction in inference time by a factor of ten, on average, without accuracy loss.
Probability-based Global Cross-modal Upsampling for Pansharpening
Pansharpening is an essential preprocessing step for remote sensing image processing.
Local-Global Transformer Enhanced Unfolding Network for Pan-sharpening
lms-07/lgteun
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Moreover, we customize a Local-Global Transformer (LGT) to simultaneously model local and global dependencies, and further formulate an LGT-based prior module for image denoising.
Unsupervised Hyperspectral Pansharpening via Low-rank Diffusion Model
xyrui/plrdiff
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To address these issues, in this work, we propose a low-rank diffusion model for hyperspectral pansharpening by simultaneously leveraging the power of the pre-trained deep diffusion model and better generalization ability of Bayesian methods.
Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity
In latest years, deep learning has gained a leading role in the pansharpening of multiresolution images.
Band-wise Hyperspectral Image Pansharpening using CNN Model Propagation
Hyperspectral pansharpening is receiving a growing interest since the last few years as testified by a large number of research papers and challenges.
