Pregled bibliografske jedinice broj: 929795
The effect of fusing Sentinel-2 bands on land-cover classification
The effect of fusing Sentinel-2 bands on land-cover classification // International journal of remote sensing, 39 (2018), 3; 822-841 doi:10.1080/01431161.2017.1392640 (međunarodna recenzija, članak, znanstveni)
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Naslov
The effect of fusing Sentinel-2 bands on land-cover classification
Autori
Gašparović, Mateo ; Jogun, Tomislav
Izvornik
International journal of remote sensing (0143-1161) 39
(2018), 3;
822-841
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
Image fusion ; Sentinel-2 ; Classification ; Accuracy ; Land cover
Sažetak
The Sentinel-2 satellite currently provides freely available multispectral bands at relatively high spatial resolution but does not acquire the panchromatic band. To improve the resolution of 20 m bands to 10 m, existing pansharpening methods (Brovey transform [BT], intensity–hue–saturation [IHS], principal component analysis [PCA], the variational method [P + XS], and the wavelet method) required adjustment, which was achieved using higher resolution multispectral bands in the role of a panchromatic band to fuse bands at a lower spatial resolution. After preprocessing, six bands at lower resolution were divided into two groups because some image fusion methods (e.g. BT, IHS) are limited to a maximum of three input bands of a lower resolution at a time. With respect to the spectral range, the higher resolution band for the first group was synthesized from bands 4 and 8, and band 8 was selected for the second group. Given that one of the main remote sensing applications is land-cover classification, the classification accuracy of the fusion methods was assessed as well as the comparison with reference bands and pixels. The supervised classification methods were Maximum Likelihood Classifier, artificial neural networks, and object-based image analysis. The classification scheme contained five classes: water, built-up, bare soil, low vegetation, and forest. The results showed that most of the fusion methods, particularly P + XS and PCA, improved the overall classification accuracy, especially for the classes of forest, low vegetation, and bare soil and in the detection of coastlines. The least satisfying results were obtained from the wavelet method.
Izvorni jezik
Engleski
Znanstvena područja
Geodezija
Citiraj ovu publikaciju:
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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