Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network
By Frosti Palsson, Johannes R. Sveinsson, M. O. Ulfarsson. IEEE Geoscience and Remote Sensing Letters, 2017
317 citations recorded by the source corpus.
In this letter, we propose a method using a 3-D convolutional neural network to fuse together multispectral and hyperspectral (HS) images to obtain a high resolution HS image. Dimensionality reduction of the HS image is performed prior to fusion in order to significantly reduce the computational time and make the method more robust to noise. Experiments are performed on a data set simulated using a real HS image. The results obtained show that the proposed approach is very promising when compared with conventional methods. This is especially true when the HS image is corrupted by additive noise.
Subfields: Aerospace Engineering, Media Technology
View Paper: https://www.semanticscholar.org/paper/354b602884b488a693c01f287893318306daf709
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
In Media Technology
- Jocelyn Chanussot
- Liangpei Zhang
- Gemine Vivone
- Giuseppe Scarpa
- Danfeng Hong
- Huanfeng Shen
- Qiangqiang Yuan
- Jing Yao
Citations in the corpus
- Boosting the Accuracy of Multispectral Image Pansharpening by Learning a Deep Residual Network (2017, background): “Convolutional Neural Network (CNN): CNN is one of the most impressive branch of deep learning models in the field of computer vision.”
- Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022, methodology): “Facing this problem, Yin et al. (2021b) propose a LST-oriented methods by considering the temporal consistency, where two final outputs generated by multiscale CNN are fused together according to a novel weight function.”
- A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods (2021, background): “, the fusion of PAN and hyperspectral (HS) images [5], [6] and the combination of MS and HS data [7]–[10].”