PanNet: A Deep Network Architecture for Pan-Sharpening

By Junfeng Yang, Xueyang Fu, Yuwen Hu, Yue Huang, Xinghao Ding, John William Paisley. IEEE International Conference on Computer Vision, 2017

758 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/aa7c7b462dd4657c727b0651018bc566a958bdfb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. José M. Bioucas-Dias
2. Konrad Schindler
3. Jocelyn Chanussot
4. Danfeng Hong
5. Silvano Galliani
6. Andreas Geiger
7. L. Leal-Taix'e

In Computer Vision and Pattern Recognition

1. Jocelyn Chanussot
2. Gemine Vivone
3. Giuseppe Scarpa
4. Danfeng Hong
5. Shutao Li
6. M. Dalla Mura
7. Andrea Garzelli
8. Rocco Restaino

In Media Technology

1. Jocelyn Chanussot
2. Shutao Li
3. Gemine Vivone
4. Giuseppe Scarpa
5. Danfeng Hong
6. M. Dalla Mura
7. Jing Yao
8. Andrea Garzelli

Citations in the corpus

• Detail Injection-Based Deep Convolutional Neural Networks for Pansharpening (2021, methodology): “the state-of-the-art CNN-based method for pansharpening deep residual PNN (DRPNN) [44],4 the state-of-the-art CNN-based method for pansharpening (PanNet) [1],5 the state-of-the-art CNN-based method for pansharpening (DiCNN1) [2],6 the state-of-the-art CNN-based method with dilated convolution for pa”
• Deep Hyperspectral Image Sharpening (2018, neutral): “Pan-sharpening methods mainly include component substitution methods [31], variational methods [32], [33], and deep learning-based methods [34], and so on.”
• Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network (2018, methodology): “Yang et al. (2017) introduced PanNet, based on the high-performance ResNet architecture (He et al., 2016).”
• Remote Sensing Image Fusion Based on Two-Stream Fusion Network (2017, methodology): “Most of the previous restoration tasks employed (cid:96) 2 norm as loss function [19, 20, 23, 35], however this will produce images su ff er from blurring e ff ect.”
• A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods (2021, background): “As a matter of fact, most recent DL pansharpening models, including the advanced versions of PNN (A-PNN/-FT) [95], embed residual modules [92], [94], [95], [97], [99], [100], although they can be very different in terms of complexity.”
• Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022, methodology): “Later, numerous methods inspired by this pioneer work are presented, in which residual learning and dense connection are commonly used (Wei et al., 2017; Yang et al., 2017; Scarpa et al., 2018; Yuan et al., 2018; Peng et al., 2020; Fu et al., 2020; Lei et al., 2021).”
• SelfS2: Self-Supervised Transfer Learning for Sentinel-2 Multispectral Image Super-Resolution (2023, background): “However, as pointed out in [11] and [23], the super-resolution of S2 images differs from the conventional pansharpening problem [20], [24], [25], [26], [27], [28] and the hyperspectral image/MSI fusion problem [28], [29], [30], [31] mainly for the high-spatial-resolution bands (i.”