为了提高超谱图像分类的精度，提出了模糊最大似然分类算法。
In order to improve the classification accuracy of hyperspectral images, a fuzzy maximum likelihood classification method is proposed.
结合实地调查数据与最大似然分类算法进行对比实验，表明该模型比最大似然总体分类精度高16%。
In comparison with the maximum likelihood classification by field survey data, the classification precision of this model heightens 16%.
最大似然法分是常规遥感图像最常用、最有效的分类方法。
Maximum likelihood classifier (MLC) is the most used and effective classification method.
与最大似然法相比，神经网络方法可以方便地加入地理辅助数据进行分类。
Neural network method is more convenient to join the assistance data to classify than maxim likelihood method.
对高维输入向量具有高的推广能力；比单源信息的SVM和最大似然方法图像分类精度更高，适合高空间分辨率遥感图像分类。
It has more accuration than the maximum likelihood method and SVM based on the single source data, adapts to the high spatial resolution RS Image classification.
对高维输入向量具有高的推广能力；比单源信息的SVM和最大似然方法图像分类精度更高，适合高空间分辨率遥感图像分类。
It has more accuration than the maximum likelihood method and SVM based on the single source data, adapts to the high spatial resolution RS Image classification.
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