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测绘学报  2015 

高光谱影像光谱-空间多特征加权概率融合分类

DOI: 10.11947/j.AGCS.2015.20140544, PP. 909-918

Keywords: 光谱-空间特征,概率融合,支持向量机,高光谱,分类

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Abstract:

提出了一种基于光谱-空间多特征加权概率融合的高光谱影像分类方法。首先,利用最小噪声分离(minimumnoisefraction,MNF)方法对高光谱影像进行降维和特征提取,并以得到的MNF特征影像作为光谱特征,联合灰度共生矩阵(graylevelco-occurrencematrix,GLCM)提取的纹理特征、基于OFC算子建立的多尺度形态学特征以及采用连续最大角凸锥(sequentialmaximumangleconvexcone,SMACC)提取的端元组分特征,组成3组光谱-空间特征;然后利用支持向量机(supportvectormachine,SVM)对每一组光谱-空间特征进行分类,得到每组特征的概率输出结果;最后,建立多特征加权概率融合模型,应用该模型将不同特征的概率输出结果进行加权融合,得到最终分类结果。为了验证该方法的有效性,利用ROSIS和AVIRIS影像进行试验,总体分类精度分别达到97.65%和96.62%。结果表明本文的方法不但较好地克服了传统基于单一特征高光谱影像分类的局限性,而且其分类效果也优于常规矢量叠加(vectorstacking,VS)和概率融合的多特征分类方法,有效地改善了高光谱影像的分类结果。

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