Study on Ejina Oasis Land Cover Using Decision Tree Classification

The automatic recognition of images has been always one of preceding issues in the filed of remote sensing. From traditional algorithm of K-Means, maximum likelihood to new decision tree, neural networks, wavelet transform and fuzzy recognition system, the classification accuracy has been improved. In this work, based on Decision Tree Classification (DTC) and Landsat ETM+ data, Ejina Oasis land cover is classified. And the applications of NDVI, K-T transformation and Principal Component Analysis (PCA) into the decision tree classification are mainly studied. Finally, the accuracy of the classification results is analyzed. The results indicate that the built decision tree model is reasonable; the overall accuracy is up to 93.28%. It can provide scientific basis for the ecological health dynamic monitoring and regional sustainable development of Ejina Oasis.