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Study on the comparison of the land cover classification for multitemporal MODIS images

Land cover classification is a complex process that may be affected by many factors. Since the first resource satellite was launched in 1972, the remote sensing community has witnessed the impressive progress in image classification methods, which is primarily driven by the advancement of remote sensing technology and computer technology. In recent years, non-parametric classifiers such as the neural network, the decision tree classifier and other classifiers have developed increasingly. Therefore, the four broadly used classification methods, which are Maximum Likelihood Classification (MLC), Self-Organized Neural Network (SONN), Support Vector Machine (SVM) and Decision Tree Classification (DTC) are firstly applied for the land cover remote sensing classification based on the multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) images of Heilongjiang area. The emphasis is placed on the comparison of the four classification methods and the techniques used for improving classification accuracy. Then, we compare the four classifiers through different aspects. Through the comparison, we got the conclusions: DTC is the best, and MLC as one of the classical methods is more stable than other three methods. Therefore, we make the land cover classification test over China using DTC and MLC methods and compare them again. We also believe that the conclusions we got in this paper are valuable for how to select an appropriate classifier in the similar applications.