Detail Injection-Based Deep Convolutional Neural Networks for Pansharpening

By Liang-Jian Deng, Gemine Vivone, Cheng Jin, Jocelyn Chanussot. IEEE Transactions on Geoscience and Remote Sensing, 2021

311 citations recorded by the source corpus.

The fusion of high spatial resolution panchromatic (PAN) data with simultaneously acquired multispectral (MS) data with the lower spatial resolution is a hot topic, which is often called pansharpening. In this article, we exploit the combination of machine learning techniques and fusion schemes introduced to address the pansharpening problem. In particular, deep convolutional neural networks (DCNNs) are proposed to solve this issue. The latter is combined first with the traditional component substitution and multiresolution analysis fusion schemes in order to estimate the nonlinear injection models that rule the combination of the upsampled low-resolution MS image with the extracted details exploiting the two philosophies. Furthermore, inspired by these two approaches, we also developed another DCNN for pansharpening. This is fed by the direct difference between the PAN image and the upsampled low-resolution MS image. Extensive experiments conducted both at reduced and full resolutions demonstrate that this latter convolutional neural network outperforms both the other detail injection-based proposals and several state-of-the-art pansharpening methods.

Subfields: Biomedical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/9462f322f262b1d2b19e7a9a2a11118e25bc7dea

How researchers see this paper

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

In Biomedical Engineering

1. Jocelyn Chanussot
2. Danfeng Hong
3. Simone Scardapane
4. Valerio Marsocci

In Media Technology

1. Danfeng Hong
2. Jocelyn Chanussot
3. Jing Yao
4. Bing Zhang
5. Lianru Gao
6. Jiaxin Li
7. Ke-xin Zheng
8. Xile Zhao

Citations in the corpus

• Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022, methodology): “, DI-based methods (He et al., 2019a; Deng et al., 2020) and VO-based methods (Shen et al.”
• 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.”