Applying optimum fusion method to improve lithological mapping of sedimentary rocks using sentinel-2 and ASTER satellite images

This study was set the yield a precise map of geological formations appliying satellite image fusion techniques. Geologic maps are one of the most valuable sources for understanding the geological conditions of a region used to show the spread of different rock and soil outcrops. In the present rese...

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Veröffentlicht in:Earth science informatics Jg. 15; H. 3; S. 1765 - 1778
Hauptverfasser: Kabolizadeh, Mostafa, Rangzan, Kazem, Mousavi, Seyyed Sajedin, Azhdari, Ehsan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022
Springer Nature B.V
Schlagworte:
Algorithms
ASTER (radiometer)
Computer vision
Discrete Wavelet Transform
Earth and Environmental Science
Earth Sciences
Earth System Sciences
Geologic mapping
Geological mapping
Geology
Image classification
Image processing
Information Systems Applications (incl.Internet)
Lithology
Ontology
Outcrops
Research Article
Satellite imagery
Sedimentary rocks
Simulation and Modeling
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
Spatial data
Support vector machines
Wavelet transforms
ASTER
Image fusion
Sentinel-2
Lithological mapping
ISSN:1865-0473, 1865-0481
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Zusammenfassung:This study was set the yield a precise map of geological formations appliying satellite image fusion techniques. Geologic maps are one of the most valuable sources for understanding the geological conditions of a region used to show the spread of different rock and soil outcrops. In the present research, the fusion of ASTER and Sentinel-2 images were applied using Brovey Transform (BT), Gram-Schmidt ( GS), Color Normalized (CN), Smoothing Filter-based Intensity Modulation (SFIM) and Discrete Wavelet Transform (DWT) methods to prepare the lithological map. Based on the results of the employed methods, DWT and BT methods are good in terms of providing spectral and spatial information, respectively. The results also showed that the SFIM method has appropriate spectral and spatial accuracy. At the classification stage, all fused images were processed through supervised classification algorithm of Support Vector Machine (SVM) to identify and separate the geological formations of the study area. The evaluation of the classifications results demonstrated that the SVM method used in the fused images by SFIM method, with 83.16 overall accuracy and 0.82 kappa coefficient has evidential results for lithological mapping.
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ISSN:1865-0473
1865-0481
DOI:10.1007/s12145-022-00836-1