Whether the selection of remote sensing features can be performed effectively will directly affect the quality of classification results. This paper proposes a multi-featured modeling strategy for the desert/grassland biome transition zone to improve the classification accuracy, taking Luoshan area of Ningxia province in China as the studying case and Landsat 8 OLI images of three periods as data source. The sparse representation based on dictionary learning is used as a classifier in order to select the combination of optimal features according to the multi-featured modeling strategy. It shows that the combination of spectrum, vegetation, terrain, architecture and water information can effectively improve the classification accuracy and reduce the classification uncertainty in the desert/grassland biome transition zone, and the best identified feature combinations includes b1 $sim$ b7, NDVI, DEM, NDBI, VAR(b5), MNDWI. Then, the statistical analysis of land cover changes in the study area from 2013 to 2015 was conducted.
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