A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods
By Gemine Vivone, M. Dalla Mura, Andrea Garzelli, Rocco Restaino, Giuseppe Scarpa, M. O. Ulfarsson, Luciano Alparone, Jocelyn Chanussot. IEEE Geoscience and Remote Sensing Magazine, 2021
298 citations recorded by the source corpus.
Pansharpening refers to the fusion of a multispectral (MS) image and panchromatic (PAN) data aimed at generating an outcome with the same spatial resolution of the PAN data and the spectral resolution of the MS image. In the last 30 years, several approaches to deal with this issue have been proposed. However, the reproducibility of these methods is often limited, making the comparison with the state of the art hard to achieve. Thus, to fill this gap, we propose a new benchmark consisting of recent advances in MS pansharpening. In particular, optimized classical approaches [multiresolution analysis (MRA) and component substitution (CS)] are compared with methods belonging to the third generation of pansharpening, represented by variational optimization-based (VO) and machine learning (ML) techniques. The benchmark is tested on different scenarios (from urban to rural areas) acquired by different commercial sensors [i.e., IKONOS (IK), GeoEye-1 (GE-1), and WorldView-3 (WV-3)]. Both quantitative and qualitative assessments and the computational burden are analyzed in this article, and all of the implementations have been collected in a MATLAB toolbox that is made available to the community.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/d9e820c6b2b3e5caa3d6c63836775d82ebbffb33
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In Computer Vision and Pattern Recognition
In Media Technology
Citations in the corpus
- Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022, methodology): “, 2019) Introducing the methods belonging to CS, MAR, and VO from the idea of meta-analysis (Vivone et al., 2020) Giving a systematic introduction and evaluation of the methods in the category of CS, MAR, VO, and ML HS pansharpening (Loncan et al.”