A critical review on multi-sensor and multi-platform remote sensing data fusion approaches: current status and prospects

Numerous remote sensing (RS) systems currently collect data about Earth and its environments. However, each system provides limited data in terms of spatial resolution, spectral information, and other parameters. Given technological constraints, combining data from diverse sources can effectively en...

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Bibliographic Details
Published in:International journal of remote sensing Vol. 46; no. 3; pp. 1327 - 1402
Main Authors: Samadzadegan, Farhad, Toosi, Ahmad, Dadrass Javan, Farzaneh
Format: Journal Article
Language:English
Published: London Taylor & Francis 01.02.2025
Taylor & Francis Ltd
Subjects:
Data acquisition
Data collection
data fusion
Data integration
deep learning
Earth observation
Geophysical data
geophysics
Image acquisition
image fusion
Land acquisition
Land cover
Land use
land use and land cover maps
LiDAR
light
manned and unmanned aerial vehicles
multi-sensor remote sensing
Multisensor fusion
multispectral and hyperspectral imaging
pansharpening
Precision agriculture
Radar
Radar imaging
radiometry
Remote sensing
Satellite data
sensor integration
Sensors
Spatial data
Spatial resolution
Spectrometry
spectroscopy
Traction
Unmanned aerial vehicles
Wavelength
wavelengths
ISSN:0143-1161, 1366-5901, 1366-5901
Online Access:Get full text
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Summary:Numerous remote sensing (RS) systems currently collect data about Earth and its environments. However, each system provides limited data in terms of spatial resolution, spectral information, and other parameters. Given technological constraints, combining data from diverse sources can effectively enhance RS solutions through data enrichment. Many studies have investigated the fusion of RS data acquired through different sensors and platforms. This paper provides a comprehensive review of research on multi-platform and -sensor RS data fusion, encompassing visible-light images, multi/hyper-spectral images, RADAR images, LiDAR point clouds, thermal images, spectrometry samples, and geophysical data. An analysis of over 950 papers revealed that feature-level fusion of multi-sensor RS data was the most commonly employed technique, surpassing pixel- and decision-level approaches. Moreover, satellite data fusion was more prevalent than the fusion of data acquired from manned and unmanned aerial vehicles. The integration of multi-sensor RS data initially gained traction in applications such as precision agriculture before expanding to land use and land cover mapping. This paper addresses previously overlooked issues and presents a framework to facilitate the seamless fusion of multi-platform and multi-sensor RS data. Guidelines for this fusion include ensuring the data have the same acquisition time, spatial co-registration, true orthorectification, consistent spatial resolution or information content, radiometric consistency, and wavelength of spectral band coverage.
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ISSN:0143-1161
1366-5901
1366-5901
DOI:10.1080/01431161.2024.2429784