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computer-vision-3

Pansharpening

3260 papers • 126 benchmarks • 313 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.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in pansharpening-3

Trend

Dataset

Best Model

Actions

WorldView-3 Adelaide

WorldView-3 Adelaide

PanCollection

PanCollection

Reduced WorldView-3 PanCollection

Libraries

i

Use these libraries to find pansharpening-3 models and implementations

Datasets

WorldView-3 PAirMax

WorldView-3 PAirMax

WorldView-2 PairMax

WorldView-2 PairMax

GeoEye-1 PairMax

GeoEye-1 PairMax

PanCollection

Subtasks

No subtasks available.

Most implemented papers

Pansharpening by convolutional neural networks in the full resolution framework

G. Poggi, Matteo Ciotola, S. Vitale, Antonio Mazza, G. Scarpa•Mon Nov 15 2021

This paper proposes a full-resolution training framework for deep learning-based pansharpening, which is fully general and can be used for any deep learning -based pansHarpening model, and defines a suitable two-component loss to ensure spectral and spatial fidelity.

95

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Reduced WorldView-3 PanCollection

Reduced QuickBird PanCollection

Reduced QuickBird PanCollection

Full WorldView-3 PanCollection

Full WorldView-3 PanCollection

WorldView-3 PAirMax

WorldView-3 PAirMax

WorldView-2 Washington

WorldView-2 Washington

WorldView-2 PairMax

WorldView-2 PairMax

GeoEye-1 Genoa

GeoEye-1 Genoa

GeoEye-1 PairMax

GeoEye-1 PairMax

0

Pansharpening via Detail Injection Based Convolutional Neural Networks

A. Plaza, J. Chanussot, Jun Yu Li, Lin He, Yizhou Rao, Jiawei Zhu, Bo Li•Thu Jun 21 2018

This paper designs a new detail injection based convolutional neural network (DiCNN) framework for pansharpening with the MS details being directly formulated in end-to-end manners, where the first detail injections based CNN mines MS details through the PAN image and the MS image, and the second one utilizes only the PAN picture.

183 0

Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs

Furkan Ozcelik, U. Algancı, Elif Sertel, Gozde Unal•Mon Jun 29 2020

A new self-supervised learning framework is proposed, where pansharpening is treated as a colorization problem, which brings an entirely novel perspective and solution to the problem compared with the existing methods that base their solution solely on producing a super-resolution version of the multispectral image.

81 0

Guided Deep Decoder: Unsupervised Image Pair Fusion

D. Hong, N. Yokoya, Tatsumi Uezato, Wei He•Wed Jul 22 2020

A guided deep decoder network is proposed as a general prior that can achieve state-of-the-art performance in various image fusion problems and allows the network parameters to be optimized in an unsupervised way without training data.

97 0

Learning deep multiresolution representations for pansharpening

Hannan Adeel, S. Ali, M. Riaz, S. Kirmani, M. I. Qureshi, J. Imtiaz•Thu Dec 31 2020

A pyramid based deep fusion framework that preserves spectral and spatial characteristics at different scales and outperforms state of the art pansharpening models is proposed.

1 0

Deep Convolutional Sparse Coding Network For Pansharpening With Guidance Of Side Information

Shuang Xu, Jiangshe Zhang, Chunxia Zhang, Zixiang Zhao, Junmin Liu, Kai Sun, Lu Huang•Tue Mar 09 2021

This paper proposes a side information partially guided convolutional sparse coding (SCSC) model for pansharpening, generalized as a deep neural network, called as SCSC panshARPening neural network ( SCSC-PNN).

6 0

Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic Indexes

Maria Kremezi, Viktoria Kristollari, V. Karathanassi, K. Topouzelis, P. Kolokoussis, Nicolò Taggio, A. Aiello, G. Ceriola, E. Barbone, P. Corradi•Thu Dec 31 2020

An index combining methodology has been developed, which enables the detection of plastic objects by exploiting spectra derived from the pan-sharpened hyperspectral images, and successfully detected the plastic targets and differentiated them from other materials.

52 0

Sentinel-2 Sharpening Using a Single Unsupervised Convolutional Neural Network With MTF-Based Degradation Model

M. Mura, J. Sveinsson, M. Ulfarsson, H. Nguyen•Thu Dec 31 2020

This article proposes a novel unsupervised DL-based S2 sharpening method using a single convolutional neural network (CNN) to sharpen the 20 and 60 m bands at the same time at full resolution.

13 0

Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction

Vishal M. Patel, Jeya Maria Jose Valanarasu, W. G. C. Bandara•Mon Jul 05 2021

A novel over-complete network, called HyperKite, which focuses on learning high-level features by constraining the receptive from increasing in the deep layers is proposed, which demonstrates the superiority of the DIP-HyperKite over the state-of-the-art pansharpening methods.

69 0

Full-resolution quality assessment for pansharpening

Matteo Ciotola, G. Scarpa•Thu Aug 12 2021

This work proposes an alternative no-reference full-resolution assessment framework, and introduces a protocol, namely the reprojection protocol, to take care of the spectral consistency issue and a new index of the spatial consistency between the pansharpened image and the panchromatic band at full resolution.

44 0

Adding a benchmark result helps the community track progress.

Pansharpening | State-of-the-Art