Curvelet-Based Synthetic Aperture Radar Image Classification
Curvelet-Based Synthetic Aperture Radar Image Classification
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DOI：
10.1109/lgrs.2013.2286089
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发表时间：
2014-06
影响因子：
4.8
通讯作者：
E. Uslu;S. Albayrak
中科院分区：
文献类型：
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作者：
E. Uslu;S. Albayrak
曲波变换(CT)是一种多尺度方向变换，能够使用纹理和空间局部性信息。在合成孔径雷达(SAR)成像中，CT主要用于相干斑噪声抑制。本文将CT用于土地利用分类中的特征提取。实现了两种基于曲波的合成孔径雷达特征提取方法。第一种是基于广义高斯分布参数估计的基于内容的图像检索算法。第二种实现是一种真正的方法，它利用曲波子带直方图，即曲线直方图(HOC)。将该方法应用于合成孔径雷达数据，与原始数据和H/A/α分解特征相比，分类正确率高达99.56%。与去除相干斑噪声的数据分类结果相比，基于曲线波的特征提取方法对相干斑噪声也具有较强的鲁棒性。
Curvelet transform (CT) is a multiscale directional transform that enables the use of texture and spatial locality information. In synthetic aperture radar (SAR) imaging, CT is mostly used in speckle noise reduction. This letter utilizes CT for feature extraction in land use classification. Two types of curvelet-based feature extraction methods are implemented for SAR. The first one is defined and used in content-based image retrieval and is based on generalized Gaussian distribution parameter estimation for each curvelet subband. The second implementation is a genuine method that utilizes the use of curvelet subband histograms, namely, histogram of curvelets (HoC). Using the proposed curvelet-based feature extraction method (HoC) on SAR data, better classification accuracies up to 99.56% are achieved compared to original data and H/A/α decomposition features. Compared to speckle-noise-reduced data classification results, it can be said that curvelet-based feature extraction is also robust against speckle noise.