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Pregled bibliografske jedinice broj: 1058922

Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers


Rumora, Luka; Miler, Mario; Medak, Damir
Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers // ISPRS International Journal of Geo-Information, 9 (2020), 4; 277, 23 doi:10.3390/ijgi9040277 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1058922 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers

Autori
Rumora, Luka ; Miler, Mario ; Medak, Damir

Izvornik
ISPRS International Journal of Geo-Information (2220-9964) 9 (2020), 4; 277, 23

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

Ključne riječi
atmospheric correction ; Sentinel-2 ; land cover classification ; machine learning ; radiometric indices ; SVM ; Sen2cor ; STDSREF

Sažetak
Atmospheric correction is one of the key parts of remote sensing preprocessing because it can influence and change the final classification result. This research examines the impact of five different atmospheric correction processing on land cover classification accuracy using Sentinel-2 satellite imagery. Those are surface reflectance (SREF), standardized surface reflectance (STDSREF), Sentinel-2 atmospheric correction (S2AC), image correction for atmospheric effects (iCOR), dark object subtraction (DOS) and top of the atmosphere (TOA) reflectance without any atmospheric correction. Sentinel-2 images corrected with stated atmospheric corrections were classified using four different machine learning classification techniques namely extreme gradient boosting (XGB), random forests (RF), support vector machine (SVM) and catboost (CB). For classification, five different classes were used: bare land, low vegetation, high vegetation, water and built-up area. SVM classification provided the best overall result for twelve dates, for all atmospheric corrections. It was the best method for both cases: when using Sentinel-2 bands and radiometric indices and when using just spectral bands. The best atmospheric correction for classification with SVM using radiometric indices is S2AC with the median value of 96.54% and the best correction without radiometric indices is STDSREF with the median value of 96.83%.

Izvorni jezik
Engleski

Znanstvena područja
Geodezija



POVEZANOST RADA


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

Ustanove
Geodetski fakultet, Zagreb

Profili:

Avatar Url Mario Miler (autor)

Avatar Url Damir Medak (autor)

Avatar Url Luka Rumora (autor)

Citiraj ovu publikaciju

Rumora, Luka; Miler, Mario; Medak, Damir
Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers // ISPRS International Journal of Geo-Information, 9 (2020), 4; 277, 23 doi:10.3390/ijgi9040277 (međunarodna recenzija, članak, znanstveni)
Rumora, L., Miler, M. & Medak, D. (2020) Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers. ISPRS International Journal of Geo-Information, 9 (4), 277, 23 doi:10.3390/ijgi9040277.
@article{article, year = {2020}, pages = {23}, DOI = {10.3390/ijgi9040277}, chapter = {277}, keywords = {atmospheric correction, Sentinel-2, land cover classification, machine learning, radiometric indices, SVM, Sen2cor, STDSREF}, journal = {ISPRS International Journal of Geo-Information}, doi = {10.3390/ijgi9040277}, volume = {9}, number = {4}, issn = {2220-9964}, title = {Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers}, keyword = {atmospheric correction, Sentinel-2, land cover classification, machine learning, radiometric indices, SVM, Sen2cor, STDSREF}, chapternumber = {277} }

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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