Support Vector Machines(SVM) is a potential hyperspectral remote sensing classification method because it is advantageous to deal with problems with high dimensions, small samples and uncertainty.
支持向量机因其适用高维特征、小样本与不确定性问题的优越性，是一种极具潜力的高光谱遥感分类方法。
The optimum selection of bands and its combinations in remote sensing classification and the method for expanding feature space are presented.
本文结合第四纪沉积物类型的划分，讨论了遥感分类中最佳波段和波段组合的选取以及特征空间的扩充方法。
Concluded that the object-oriented classification than the pixel-based classification more precise, more suitable for high-resolution remote sensing classification.
得出结论，面向对象分类法比基于像元的分类法精度更高，更适合于高分辨率遥感分类。
Classification and pattern recognition of high dimensional remote sensing data are distinctly different from traditional multi-channel remote sensing classification techniques.
高维遥感数据的分类与识别与传统的多光谱遥感分类技术具有明显的区别。
Finally, the vector format data of remote sensing classification result was used to update the original forest stand layer in order to get the available inputs of fire visualization modeling.
最后，利用矢量格式的遥感分类结果对原森林小班图层进行更新，获得新的可燃物类型图层，作为林火可视化模型的输入图层。
The interpretation characters of every catalogue were also demonstrated. The system can satisfy national remote sensing classification applications.
研究结论：该系统基本可以满足在国家尺度上的遥感分类应用要求。
Remote sensing classification is one of indispensable parts of forest resource survey and supervision. The classification accuracy directly influences the applicable level and practical value.
遥感分类是森林资源调查和监测不可缺少的内容，分类的精度直接影响遥感数据的应用水平和实用价值。
Remote sensing classification is one of indispensable parts of forest resource survey and supervision. The classification accuracy directly influences the applicable level and practical value.
遥感分类是森林资源调查和监测不可缺少的内容，分类的精度直接影响遥感数据的应用水平和实用价值。
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