一种自适应的混合Freeman/Eigenvalue极化分解模型

doi:10.6046/gtzyyg.2017.02.02

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摘要

全极化 SAR 数据的极化分解在土地利用分类、目标检测与识别以及地表参数反演等领域得到了广泛应用。目前,主要有基于特征值分解和基于模型分解2类极化分解方法。混合Freeman/Eigenvalue极化分解结合了两者的优势,避免了基于模型的极化分解中负功率问题并且能够利用已知的散射机制解释分解后的散射分量。为了进一步拓展该分解在不同地表类型中的应用,通过引入参数Neumann一般化体散射模型,提出了一种自适应的极化分解模型。利用德国Black Forest地区的L波段AirSAR(airborne synthetic aperture Radar)全极化数据进行实验,并与现有的Yamaguchi三分量模型和自适应非负分解(adaptive nonnegative eigenvalue decomposition,ANNED)对比分析,以验证模型的有效性。研究表明,自适应的混合Freeman/Eigenvalue极化分解模型保证了分解能量的非负性及完全分解,适应于不同类型的地表,能有效地区分不同地类。

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	陈洁
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	韩亚超
	高子弘

关键词 ：高光谱降维, HySime, 噪声白化, 信号相关矩阵

Abstract：

Polarimetric decomposition of fully polarimetric SAR data has been extensively used in land use classification, target detection and identification, and land surface parameter retrieval. At present, two main categories of polarimetric decomposition approaches can be identified, i.e., model-based decomposition and eigenvalue-based decomposition. By combining the advantages of the above two decomposition methods, the hybrid Freeman/Eigenvalue method can deal with the negative power problems, and the decomposed components can be interpreted in terms of known scattering mechanisms. In order to extend the applicability of the hybrid Freeman/Eigenvalue to different types of land cover, the authors propose a novel adaptive polarimetric decomposition method in this paper by coupling the hybrid Freeman/Eigenvalue decomposition and an adaptive volume scattering model proposed by Neumann et al. The performance and advantages of the proposed method were demonstrated and evaluated with AirSAR L-band data over Black Forest in Germany. Comparative studies were also carried out with previous Yamaguchi three-component decomposition and adaptive nonnegative eigenvalue decomposition (ANNED). The results show that the proposed method can effectively avoid negative power problems and is applicable to different types of land cover. Moreover, different types of land cover can be well discriminated by the proposed technique.

Key words： Hyperspectral dimension reduction HySime noise whitening signal correlation matrix

收稿日期: 2015-11-23 出版日期: 2017-05-03

基金资助:

国家自然科学基金重点项目“农田遥感监测机理与生态过程关键参数反演”(编号: 41230747)、高分辨率对地观测系统重大专项“基于GF-4卫星数据的特征参数反演技术”(编号: 11-Y20A05-9001-15/16)和中国博士后基金特别资助项目“中国区域高空间分辨率发射率产品生成与应用”(编号: 2015T80012)共同资助

通讯作者: 秦其明(1952-),男,教授,博士生导师,主要从事定量遥感与地理信息系统方面的研究。Email: qmqin@pku.edu.cn。

作者简介: 何连(1986-),男,博士,主要从事微波遥感和定量遥感研究。Email: helianpku@pku.edu.cn。

引用本文:

何连, 秦其明, 任华忠. 一种自适应的混合Freeman/Eigenvalue极化分解模型[J]. 国土资源遥感, 2017, 29(2): 8-14.
HE Lian, QIN Qiming, REN Huazhong. An adaptive hybrid Freeman/Eigenvalue polarimetric decomposition model. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 8-14.

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https://www.gtzyyg.com/CN/10.6046/gtzyyg.2017.02.02 或 https://www.gtzyyg.com/CN/Y2017/V29/I2/8

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[1]	陈洁, 杜磊, 李京, 韩亚超, 高子弘. 基于噪声白化的高光谱数据子空间维数算法[J]. 国土资源遥感, 2017, 29(2): 60-66.

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