TY  -  
T1  - Auxiliary datasets improve accuracy of object-based land use/land cover classification in heterogeneous savanna landscapes 
SN  -  /  
UR  - http://hdl.handle.net/10138/305233 
T3  -  
A1  - Hurskainen, Pekka; Adhikari, Hari; Siljander, Mika; Pellikka, Petri; Hemp, Andreas 
A2  -  
PB  -  
Y1  - 2019 
LA  - eng 
AB  - Classifying land use/land cover (LULC) with sufficient accuracy in heterogeneous landscapes is challenging using only satellite imagery. To improve classification accuracy inclusion of features from auxiliary geospatial datasets in classification models is applied since 1980s. However, the method is mostly limited to pixel-based classifications, and the coverage, accuracy and resolution of free and open-access auxiliary datasets have been poor until recent years. We evaluated how recent global c... 
VO  -  
IS  -  
SP  -  
OP  -  
KW  - 1171 Geosciences; image segmentation; OBIA; Land use; Land cover classification; Auxiliary data; Random Forest; Satellite Time Series; Image segmentation; OBIA; Land use/land cover mapping; Auxiliary data; Random Forest; Satellite Time Series; IMAGE-ANALYSIS; RANDOM FOREST; MT. KILIMANJARO; TIME-SERIES; SURFACE TEMPERATURE; SOUTHERN SLOPES; ANCILLARY DATA; VEGETATION; MULTISOURCE; SELECTION 
N1  -   
PP  -  
ER  -