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Pregled bibliografske jedinice broj: 1067500

Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery


Gašparović, Mateo; Dobrinić, Dino
Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery // Remote sensing, 12 (2020), 12; 1952, 22 doi:10.3390/rs12121952 (međunarodna recenzija, članak, znanstveni)


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Naslov
Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery

Autori
Gašparović, Mateo ; Dobrinić, Dino

Izvornik
Remote sensing (2072-4292) 12 (2020), 12; 1952, 22

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
speckle filtering ; land-cover classification ; multitemporal ; Sentinel-1 ; synthetic aperture radar (SAR) ; urban vegetation

Sažetak
Mapping of green vegetation in urban areas using remote sensing techniques can be used as a tool for integrated spatial planning to deal with urban challenges. In this context, multitemporal (MT) synthetic aperture radar (SAR) data have not been equally investigated, as compared to optical satellite data. This research compared various machine learning methods using single-date and MT Sentinel-1 (S1) imagery. The research was focused on vegetation mapping in urban areas across Europe. Urban vegetation was classified using six classifiers—random forests (RF), support vector machine (SVM), extreme gradient boosting (XGB), multi-layer perceptron (MLP), AdaBoost.M1 (AB), and extreme learning machine (ELM). Whereas, SVM showed the best performance in the single-date image analysis, the MLP classifier yielded the highest overall accuracy in the MT classification scenario. Mean overall accuracy (OA) values for all machine learning methods increased from 57% to 77% with speckle filtering. Using MT SAR data, i.e., three and five S1 imagery, an additional increase in the OA of 8.59% and 13.66% occurred, respectively. Additionally, using three and five S1 imagery for classification, the F1 measure for forest and low vegetation land-cover class exceeded 90%. This research allowed us to confirm the possibility of MT C-band SAR imagery for urban vegetation mapping.

Izvorni jezik
Engleski



POVEZANOST RADA


Projekt / tema
HRZZ-IP-2016-06-5621 - Geoprostorno praćenje zelene infrastrukture na temelju terestričkih, zračnih i satelitskih snimaka (Damir Medak, )
RS4ENVIRO

Ustanove
Geodetski fakultet, Zagreb

Profili:

Avatar Url Mateo Gašparović (autor)

Avatar Url Dino Dobrinić (autor)

Citiraj ovu publikaciju

Gašparović, Mateo; Dobrinić, Dino
Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery // Remote sensing, 12 (2020), 12; 1952, 22 doi:10.3390/rs12121952 (međunarodna recenzija, članak, znanstveni)
Gašparović, M. & Dobrinić, D. (2020) Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery. Remote sensing, 12 (12), 1952, 22 doi:10.3390/rs12121952.
@article{article, year = {2020}, pages = {22}, DOI = {10.3390/rs12121952}, chapter = {1952}, keywords = {speckle filtering, land-cover classification, multitemporal, Sentinel-1, synthetic aperture radar (SAR), urban vegetation}, journal = {Remote sensing}, doi = {10.3390/rs12121952}, volume = {12}, number = {12}, issn = {2072-4292}, title = {Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery}, keyword = {speckle filtering, land-cover classification, multitemporal, Sentinel-1, synthetic aperture radar (SAR), urban vegetation}, chapternumber = {1952} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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