- Tytuł:
- Forest Community Mapping Using Hyperspectral (CHRIS/PROBA) and Sentinel-2 Multispectral Images
- Autorzy:
-
Głowienka, Ewa
Zembol, Nicole - Powiązania:
- https://bibliotekanauki.pl/articles/2174650.pdf
- Data publikacji:
- 2022
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Tematy:
-
hyperspectral
pre-processing
multispectral
Sentinel-2
CHRIS/PROBA
machine learning - Opis:
- The possibility to use hyperspectral images (CHRIS/PROBA) and multispectral images (Sentinel-2) in the classification of forest communities is assessed in this article. The pre-processing of CHRIS/PROBA image included: noise reduction, radiometric correction, atmospheric correction, geometric correction. Due to MNF transformation the number of the hyperspectral image channels was reduced (to 10 channels) and smiling errors were removed. Sentinel-2 image (level 2A) did not require pre-processing. Three tree genera occurring in the study area were selected for the classification: pine (Pinus), alder (Alnus) and birch (Betula). Image classification was carried out with three methods: SAM (Spectral Angle Mapper ), MTMF (Mixture Tuned Matched Filtering), SVM (Support Vector Machine). For the CHRIS/PROBA image, the algorithm SVM turned out to be the best. Its overall accuracy (OA) was 72%. The poorest result (OA = 52%) was for the MTMF classifier. In the classification of Sentinel-2 multispectral image the best result was for the MTMF method: OA = 82%, kappa coefficient 0.7. For other methods, the overall accuracy exceeded 65%. Among the classified genera, the highest producer’s accuracy was obtained for pine (PA = 96%), and the broad-leaf genera: alder and birch had PA ranging from 42% to 85%.
- Źródło:
-
Geomatics and Environmental Engineering; 2022, 16, 4; 103--117
1898-1135 - Pojawia się w:
- Geomatics and Environmental Engineering
- Dostawca treści:
- Biblioteka Nauki
Artykuł