2008 International Workshop on Earth Observation and Remote Sensing Applications Study on the Comparison of the Land Cover Classification for Multitemporal Modis Images Therefore, the Four Broadly Used Classification Methods, Which Are Maximum Likelihood Classification (mlc), Self-organized Neural N
2008 International Workshop on Earth Observation and Remote Sensing Applications Study on the Comparison of the Land Cover Classification for Multitemporal Modis Images Therefore, the Four Broadly Used Classification Methods, Which Are Maximum Likelihood Classification (mlc), Self-organized Neural N
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2008 国际地球观测与遥感应用研讨会 多时相 Modis 影像土地覆盖分类比较研究 因此，四种广泛使用的分类方法，即最大似然分类（mlc）、自组织神经网络
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Jixian Zhang
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作者：
Jian Guo;Yonghong Zhang;Jixian Zhang
土地覆被分类是一个复杂的过程，可能受到许多因素的影响。自1972年第一颗资源卫星发射以来，遥感界见证了图像分类方法的巨大进步，这主要是由遥感技术和计算机技术的进步推动的。近年来，非参数分类器如神经网络、决策树分类器等得到了日益发展。（MODIS）图像。重点放在四种分类方法的比较和用于提高分类精度的技术。然后，我们从不同的角度对这四个分类器进行了比较。通过比较得出结论：直接转矩控制是最好的，而MLC作为经典的控制方法比其他三种方法稳定。因此，我们用DTC和MLC方法对中国的土地覆盖进行了分类试验，并再次进行了比较。我们也相信本文的结论对于在类似的应用中如何选择合适的分类器具有一定的参考价值。
—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. (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.