一种基于Pauli分解和支持向量机的全极化合成孔径雷达监督分类算法
发布时间：2018-10-29 18:11
【摘要】：全极化合成孔径雷达(SAR)影像准确分类的一个重要前提是充分提取反映地物实际物理性质的特征。然而现有的全极化SAR特征提取算法和分类算法众多,却均存在各种各样的问题。无论极化特征提取方法还是分类算法,都会影响最终的分类精度。针对此问题,在多次实验的基础上,提出一种综合Pauli极化特征分解和支持向量机(SVM)的分类策略,简称为Pauli-SVM算法。首先通过经典的Pauli分解法提取全极化SAR影像的奇次散射、偶次散射、体散射等极化特征;并将这些信息组合成一个特征向量,然后引入高精度的SVM分类算法,选择训练样本后对全极化SAR影像进行监督分类。在江苏溧水和南京横溪镇两个研究区,以ALOS卫星的PALSAR影像为研究数据,进行监督Wishart分类算法、Freeman特征提取法结合SVM的分类算法、Yamaguchi特征提取法结合SVM的分类算法、Pauli-SVM算法的分类对比实验。结果表明,新提出的PauliSVM算法可以有效地提高分类的准确性。
[Abstract]:An important prerequisite for accurate classification of fully polarized synthetic Aperture Radar (SAR) images is to fully extract features that reflect the actual physical properties of ground objects. However, there are many existing full-polarization SAR feature extraction algorithms and classification algorithms, but there are a variety of problems. Whether polarization feature extraction method or classification algorithm will affect the final classification accuracy. In order to solve this problem, on the basis of many experiments, a classification strategy based on Pauli polarization feature decomposition and support vector machine (SVM) is proposed, which is called Pauli-SVM algorithm for short. Firstly, the odd scattering, even scattering, volume scattering and other polarization characteristics of fully polarized SAR images are extracted by classical Pauli decomposition method. The information is combined into a feature vector, and then a high-precision SVM classification algorithm is introduced. The training samples are selected and supervised classification of fully polarized SAR images is carried out. In two research areas of Lishui, Jiangsu Province and Hengxi Town, Nanjing, the supervised Wishart classification algorithm was carried out based on the PALSAR images of ALOS satellite, the Freeman feature extraction method combined with the SVM classification algorithm, and the Yamaguchi feature extraction method combined with SVM classification algorithm. Comparison experiment of Pauli-SVM algorithm. The results show that the proposed PauliSVM algorithm can effectively improve the accuracy of classification.
【作者单位】： 江苏省资源环境信息工程重点实验室(中国矿业大学);江苏省地理信息技术重点实验室;
【基金】：国家自然科学基金(41171323) 中国地质调查局地质调查工作项目(1212011120229) 江苏省自然科学基金(BK2012018) 地理空间信息工程国家测绘地理信息局重点实验室开放基金(201109)资助
【分类号】：TN957.52
本文编号：2298418
[Abstract]:An important prerequisite for accurate classification of fully polarized synthetic Aperture Radar (SAR) images is to fully extract features that reflect the actual physical properties of ground objects. However, there are many existing full-polarization SAR feature extraction algorithms and classification algorithms, but there are a variety of problems. Whether polarization feature extraction method or classification algorithm will affect the final classification accuracy. In order to solve this problem, on the basis of many experiments, a classification strategy based on Pauli polarization feature decomposition and support vector machine (SVM) is proposed, which is called Pauli-SVM algorithm for short. Firstly, the odd scattering, even scattering, volume scattering and other polarization characteristics of fully polarized SAR images are extracted by classical Pauli decomposition method. The information is combined into a feature vector, and then a high-precision SVM classification algorithm is introduced. The training samples are selected and supervised classification of fully polarized SAR images is carried out. In two research areas of Lishui, Jiangsu Province and Hengxi Town, Nanjing, the supervised Wishart classification algorithm was carried out based on the PALSAR images of ALOS satellite, the Freeman feature extraction method combined with the SVM classification algorithm, and the Yamaguchi feature extraction method combined with SVM classification algorithm. Comparison experiment of Pauli-SVM algorithm. The results show that the proposed PauliSVM algorithm can effectively improve the accuracy of classification.
【作者单位】： 江苏省资源环境信息工程重点实验室(中国矿业大学);江苏省地理信息技术重点实验室;
【基金】：国家自然科学基金(41171323) 中国地质调查局地质调查工作项目(1212011120229) 江苏省自然科学基金(BK2012018) 地理空间信息工程国家测绘地理信息局重点实验室开放基金(201109)资助
【分类号】：TN957.52
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