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。
A new automatic classification model of remote sensing image using pixel information decomposition combined with neural network classification is proposed in this paper.
提出了一种新的基于像元信息分解和神经网络分类相结合的城市绿地遥感信息自动提取方法。
It has an important practical significance in offering essential information and data for the decision-maker with recognition and classification the drainage system from the image of remote sensing.
对各种遥感影象的水系加以识别和分类，为决策者提供必要的信息和数据，具有重要的现实意义。
In the hyperspectral remote sensing, the continuum removed methods is used only with the spectrum of a single pixel to analyze spectrum and extract the feature bands useful with the classification.
在高光谱遥感中，包络线消除法一般仅局限于对单个像元的光谱进行光谱分析，从中提取出有助于分类识别的特征波段。
In this method, the SVM classification model combined with texture analysis is established on the basis of texture extraction from SPOT5 remote sensing image.
该方法在对SPOT5遥感影像进行纹理特征提取的基础上，构建了结合多窗口纹理的SVM模型。
In this method, the SVM classification model combined with texture analysis is established on the basis of texture extraction from SPOT5 remote sensing image.
该方法在对SPOT5遥感影像进行纹理特征提取的基础上，构建了结合多窗口纹理的SVM模型。
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