Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods

Remote Sensing

Jinliang Wang¹  Qingquan Li²  Tiezhu Shi²  Chao Yang²  Guofeng Wu²  Kai Ding² 

[1] College of Tourism and Geographical Sciences, Yunnan Normal University, Kunming 650500, China;Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China;

关键词: LULC;    improved decision tree;    mixed pixel decomposition;    3D terrain;    fusion;   

DOI  :  10.3390/rs9121222

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown