RT Journal Article
T1 Panchromatic and multispectral image fusion for remote sensing and earth observation: 												Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead
A1 Zhang, Kai
A1 Zhang, Feng
A1 Wan, Wenbo
A1 Yu, Hui
A1 Sun, Jiande
A1 Del Ser, Javier
A1 Elyan, Eyad
A1 Hussain, Amir
AB Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks compared with any of the source images. It has been widely applied to image interpretation and pre-processing of various applications. A large number of methods have been proposed to achieve better fusion results by considering the spatial and spectral relationships among panchromatic and multispectral images. In recent years, the fast development of artificial intelligence (AI) and deep learning (DL) has significantly enhanced the development of pan-sharpening techniques. However, this field lacks a comprehensive overview of recent advances boosted by the rise of AI and DL. This paper provides a comprehensive review of a variety of pan-sharpening methods that adopt four different paradigms, i.e., component substitution, multiresolution analysis, degradation model, and deep neural networks. As an important aspect of pan-sharpening, the evaluation of the fused image is also outlined to present various assessment methods in terms of reduced-resolution and full-resolution quality measurement. Then, we conclude this paper by discussing the existing limitations, difficulties, and challenges of pan-sharpening techniques, datasets, and quality assessment. In addition, the survey summarizes the development trends in these areas, which provide useful methodological practices for researchers and professionals. Finally, the developments in pan-sharpening are summarized in the conclusion part. The aim of the survey is to serve as a referential starting point for newcomers and a common point of agreement around the research directions to be followed in this exciting area.
SN 1566-2535
YR 2023
FD 2023-05
LA eng
NO Zhang , K , Zhang , F , Wan , W , Yu , H , Sun , J , Del Ser , J , Elyan , E & Hussain , A 2023 , ' Panchromatic and multispectral image fusion for remote sensing and earth observation : Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead ' , Information Fusion , vol. 93 , pp. 227-242 . https://doi.org/10.1016/j.inffus.2022.12.026
NO Publisher Copyright: © 2022 The Author(s)
NO This work was supported in part by the Natural Science Foundation of China (61901246), the China Postdoctoral Science Foundation, China Grant (2019TQ0190, 2019M662432), the Scientific Research Leader Studio of Ji'nan (2021GXRC081), and Joint Project for Smart Computing of Shandong Natural Science Foundation, China (ZR2020LZH015). Amir Hussain acknowledges the support of the UK Engineering and Physical Sciences Research Council (EPSRC)-Grants Ref. EP/M026981/1, EP/T021063/1, EP/T024917/1. Hui Yu acknowledges the support of Royal Society, UK (NIF/R1/180909). J. Del Ser would like to thank the Spanish Centro para el Desarrollo Tecnologico Industrial (CDTI, Ministry of Science and Innovation) through the “Red Cervera” Programme (AI4ES project), as well as by the Basque Government, Spain through the ELKARTEK program and the Consolidated Research Group MATHMODE (Ref. IT1456-22). This work was supported in part by the Natural Science Foundation of China ( 61901246 ), the China Postdoctoral Science Foundation, China Grant ( 2019TQ0190 , 2019M662432 ), the Scientific Research Leader Studio of Ji’nan ( 2021GXRC081 ), and Joint Project for Smart Computing of Shandong Natural Science Foundation, China ( ZR2020LZH015 ). Amir Hussain acknowledges the support of the UK Engineering and Physical Sciences Research Council (EPSRC) -Grants Ref. EP/M026981/1 , EP/T021063/1 , EP/T024917/1 . Hui Yu acknowledges the support of Royal Society, UK ( NIF/R1/180909 ). J. Del Ser would like to thank the Spanish Centro para el Desarrollo Tecnologico Industrial (CDTI, Ministry of Science and Innovation) through the “Red Cervera” Programme (AI4ES project), as well as by the Basque Government, Spain through the ELKARTEK program and the Consolidated Research Group MATHMODE (Ref. IT1456-22 ).
DS TECNALIA Publications
RD 1 sept 2024