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利用多尺度特征与深度网络对遥感影像进行场景分类
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Journal Article
利用多尺度特征与深度网络对遥感影像进行场景分类
2016
Overview
针对因样本量小而导致的遥感图像场景分类精度不高的问题,结合非下采样Contourlet变换（NSCT）、深度卷积神经网络（DCNN）和多核支持向量机（MKSVM）,提出了一种基于多尺度深度卷积神经网络（MS-DCNN）的遥感图像场景分类方法。首先利用非下采样Contourlet变换方法对遥感图像多尺度分解,然后对分解后的高频子带和低频子带分别用DCNN训练得到了不同尺度的图像特征,最后采用MKSVM综合多尺度特征并实现遥感图像场景分类。对标准遥感图像分类数据集的试验结果表明,本算法能够结合低频和高频子带对不同类别场景的识别优势,对遥感图像场景取得较好的分类结果。
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