|Chao Yang*,Guofeng Wu,Kai Ding,Tiezhu Shi,Qingquan Li,Jinliang Wang
Decision tree classification is one of the most efficient methods for obtaining land use/land
cover (LULC) information from remotely sensed imageries. However, traditional decision tree
classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed
to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed
pixel influence. The abundance and minimum noise fraction (MNF) results that were obtained from
mixed pixel decomposition were added to decision tree multi-features using a three-dimensional (3D)
Terrain model, which was created using an image fusion digital elevation model (DEM), to select
training samples (ROIs), and improve ROI separability. A Landsat-8 OLI image of the Yunlong
Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the
Kappa coefficient and the overall accuracy of integrated pixel unmixing and decision tree method
increased by 0.093% and 10%, respectively, as compared with the original decision tree method.
This proposed method could effectively eliminate the influence of mixed pixels and improve the
accuracy in complex LULC classifications.