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https://doi.org/https://doi.org/10.5552/crojfe.2021.859

Green Infrastructure Mapping in Urban Areas Using Sentinel-1 Imagery

Mateo Gašparović   ORCID icon orcid.org/0000-0003-2345-7882 ; Chair of Photogrammetry and Remote Sensing Faculty of Geodesy, University of Zagreb Kačićeva 26 10000 Zagreb CROATIA
Dino Dobrinić ; Chair of Geoinformatics Faculty of Geodesy, University of Zagreb Kačićeva 26 10 000 Zagreb CROATIA

Puni tekst: engleski, pdf (10 MB) str. 337-353 preuzimanja: 0* citiraj
APA 6th Edition
Gašparović, M. i Dobrinić, D. (2021). Green Infrastructure Mapping in Urban Areas Using Sentinel-1 Imagery. Croatian Journal of Forest Engineering, 42 (2), 337-353. Preuzeto s https://hrcak.srce.hr/255272
MLA 8th Edition
Gašparović, Mateo i Dino Dobrinić. "Green Infrastructure Mapping in Urban Areas Using Sentinel-1 Imagery." Croatian Journal of Forest Engineering, vol. 42, br. 2, 2021, str. 337-353. https://hrcak.srce.hr/255272. Citirano 10.04.2021.
Chicago 17th Edition
Gašparović, Mateo i Dino Dobrinić. "Green Infrastructure Mapping in Urban Areas Using Sentinel-1 Imagery." Croatian Journal of Forest Engineering 42, br. 2 (2021): 337-353. https://hrcak.srce.hr/255272
Harvard
Gašparović, M., i Dobrinić, D. (2021). 'Green Infrastructure Mapping in Urban Areas Using Sentinel-1 Imagery', Croatian Journal of Forest Engineering, 42(2), str. 337-353. Preuzeto s: https://hrcak.srce.hr/255272 (Datum pristupa: 10.04.2021.)
Vancouver
Gašparović M, Dobrinić D. Green Infrastructure Mapping in Urban Areas Using Sentinel-1 Imagery. Croatian Journal of Forest Engineering [Internet]. 2021 [pristupljeno 10.04.2021.];42(2):337-353. Dostupno na: https://hrcak.srce.hr/255272
IEEE
M. Gašparović i D. Dobrinić, "Green Infrastructure Mapping in Urban Areas Using Sentinel-1 Imagery", Croatian Journal of Forest Engineering, vol.42, br. 2, str. 337-353, 2021. [Online]. Dostupno na: https://hrcak.srce.hr/255272. [Citirano: 10.04.2021.]

Sažetak
High temporal resolution of synthetic aperture radar (SAR) imagery (e.g., Sentinel-1 (S1) imagery) creates new possibilities for monitoring green vegetation in urban areas and generating land-cover classification (LCC) maps. This research evaluates how different pre-processing steps of SAR imagery affect classification accuracy. Machine learning (ML) methods were applied in three different study areas: random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB). Since the presence of the speckle noise in radar imagery is inevitable, different adaptive filters were examined. Using the backscattering values of the S1 imagery, the SVM classifier achieved a mean overall accuracy (OA) of 63.14%, and a Kappa coefficient (Kappa) of 0.50. Using the SVM classifier with a Lee filter with a window size of 5×5 (Lee5) for speckle reduction, mean values of 73.86% and 0.64 for OA and Kappa were achieved, respectively. An additional increase in the LCC was obtained with texture features calculated from a grey-level co-occurrence matrix (GLCM). The highest classification accuracy obtained for the extracted GLCM texture features using the SVM classifier, and Lee5 filter was 78.32% and 0.69 for the mean OA and Kappa values, respectively. This study improved LCC with an evaluation of various radiometric and texture features and confirmed the ability to apply an SVM classifier. For the supervised classification, the SVM method outperformed the RF and XGB methods, although the highest computational time was needed for the SVM, whereas XGB performed the fastest. These results suggest pre-processing steps of the SAR imagery for green infrastructure mapping in urban areas. Future research should address the use of multitemporal SAR data along with the pre-processing steps and ML algorithms described in this research.

Ključne riječi
grey-level co-occurrence matrix (GLCM), land-cover classification, machine learning, speckle filtering, Synthetic Aperture Radar (SAR)

Hrčak ID: 255272

URI
https://hrcak.srce.hr/255272

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