Research on Hyperspectral Remote Sensing Image Classification Based on MNF and SVM
Research on Hyperspectral Remote Sensing Image Classification Based on MNF and SVM
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发表时间：
2007
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通讯作者：
L. Hai-tao
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作者：
L. Hai-tao
在分析高光谱遥感图像分类现状和难点的基础上，提出了一种基于OMIS 1数据的最小噪声分数变换和支持向量机的高光谱遥感图像分类方法。与传统的最大似然分类法（MLC）相比，该方法克服了Hughes现象，提高了分类速度，总的分类准确率达到94.85%。该方法在高光谱遥感图像分类中具有一定的优越性和实用性。
On the basis of analyzing the actuality and difficulty of Hyperspectral image classification, a method of applying Minimum Noise Fraction Transformation and Support Vector Machine to Hyperspectral remote sensing image classification is presented in this paper where OMIS 1 data is used. Compared with the traditional Maximum Likelihood Classification (MLC) method, the results show that this method overcomes the Hughes phenomenon, boosts classification speed, and has total accuracy of about 94.85%. Thus this method demonstrated its superiority and practicability in classifying Hyperspectral remote sensing image.