一种基于张量积扩散的非监督极化SAR图像地物分类方法

邹焕新; 李美霖; 马倩; 孙嘉赤; 曹旭; 秦先祥

doi:10.12000/JR19057

一种基于张量积扩散的非监督极化SAR图像地物分类方法

DOI: 10.12000/JR19057

邹焕新^1, ,,
李美霖^1,,
马倩^1,,
孙嘉赤^1,,
曹旭^1,,
秦先祥^2,

①.
国防科技大学电子科学学院长沙 410073
②.
空军工程大学信息与导航学院西安 710077

基金项目: 国家自然科学基金(61331015, 41601436)

详细信息

作者简介:
邹焕新(1973–)，男，广东人，现任国防科技大学电子科学学院教授，硕士生导师，主要研究方向为SAR图像解译、多源信息融合、计算机视觉、图像处理、模式识别等。E-mail: hxzou2008@163.com

李美霖(1995–)，女，山西人，现为国防科技大学电子科学学院硕士，主要研究方向为极化SAR图像地物分类、模式识别等。E-mail: summit_mll@qq.com

马　倩(1996–)，女，湖南人，现为国防科技大学电子科学学院硕士，主要研究方向为多源遥感数据变化检测。E-mail: 2233809618@qq.com

孙嘉赤(1996–)，男，山西人，现为国防科技大学电子科学学院硕士，主要研究方向为SAR图像和光学图像目标检测分类与识别。E-mail: 445219733@qq.com

曹　旭(1996–)，男，天津人，现为国防科技大学电子科学学院硕士，主要研究方向为SAR图像和光学图像目标检测分类与识别。E-mail: 1135459767@qq.com

秦先祥(1986–)，男，广西人，现任空军工程大学信息与导航学院讲师，主要研究方向为SAR图像解译。E-mail: qinxianxiang@126.com

通讯作者:
邹焕新　hxzou2008@163.com

中图分类号: TN957
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- 被引次数: 2
出版历程
- 收稿日期: 2019-05-14
- 修回日期: 2019-07-19
- 网络出版日期: 2019-08-01

An Unsupervised PolSAR Image Classification Algorithm Based on Tensor Product Graph Diffusion

ZOU Huanxin^{1
, ,},
LI Meilin^1
,,
MA Qian^1
,,
SUN Jiachi^1
,,
CAO Xu^1
,,
QIN Xianxiang^2
,

①.
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
②.
School of Information and Navigation, Air Force Engineering University, Xi’an 710077, China

Funds: The National Natural Science Foundation of China (61331015, 41601436)

More Information

Corresponding author: Zou Huanxin, hxzou2008@163.com

摘要

摘要: 针对相似度表达的困难性以及极化SAR图像中固有的相干斑噪声问题，该文提出了一种基于张量积(TPG)扩散的非监督极化SAR图像地物分类算法。张量积扩散一般用于光学图像的分割或检索，目前研究表明，其已可用于极化SAR(PolSAR)图像地物分类。基于张量积扩散可以稳健地度量数据点之间的测地线距离，因此能够更好地挖掘数据点之间内在的相似度信息。首先，将极化SAR图像进行分割，生成许多超像素；其次，基于超像素提取7种特征并生成一个特征向量，进而利用高斯核构建相似度矩阵；再次，基于已构建的相似度矩阵，利用张量积扩散沿着数据点的内在流形结构进行相似度的传播，实现全局的相似性度量，从而获得一个具有更强判别能力的相似度矩阵；最后，基于此相似度矩阵进行谱聚类以得到地物分类结果。该文在仿真和实测极化SAR图像上均进行了大量实验，并与4种经典算法进行对比，结果表明该方法可以有效地结合空间邻域相似度信息并取得更高的分类精度。
- 极化SAR图像 /
- 非监督分类 /
- 张量积图 /
- 扩散 /
- 超像素 /
- 谱聚类
Abstract: To overcome the difficulty of similarity expression and the effects of speckle noise in unsupervised classification of Polarimetric Synthetic Aperture Radar (PolSAR) images, a novel unsupervised PolSAR image terrain classification algorithm based on Tensor Product Graph (TPG) diffusion has been developed herein. Generally, TPG diffusion is usually utilized for optical image segmentation or image retrieval. In the present study, it can be used for PolSAR image terrain classification. TPG diffusion can robustly estimate geodesic distances ; therefore, it can be used for mining the intrinsic affinity between data points. First, the PolSAR image is over-segmented into many superpixels. Second, seven features are extracted based on the segmented superpixels to form a feature vector and construct a similarity matrix by using the Gaussian kernel. Third, TPG diffusion is performed on this similarity matrix to obtain another similarity matrix with stronger discriminability by propagating affinity information along the mainfold structure of data to achieve the global affinity measure. Finally, spectral clustering based on the diffused similarity matrix is adopted to perform terrain classification. Extensive experiments conducted on both simulated and real-world PolSAR images demonstrate that our approach can effectively combine neighborhood information and achieve higher classification accuracy, compared to four other competitive state-of-the-art methods.
- PolSAR image /
- Unsupervised classification /
- Tensor Product Graph (TPG) /
- Diffusion /
- Superpixel /
- Spectral clustering

HTML全文

图 1 本文算法框架流程图

Figure 1. The flowchart of the proposed method

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图 2 张量积图简易示例

Figure 2. An example of tensor product graph

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图 3 仿真极化SAR图像

Figure 3. The simulated PolSAR image

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图 4 实测极化SAR图像

Figure 4. The real-world PolSAR image

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图 5 仿真极化SAR图像的分类结果

Figure 5. Classification results of the simulated PolSAR image

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图 6 实测极化SAR图像的分类结果

Figure 6. Classification results of the real-world PolSAR image

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图 7 实测极化SAR图像超像素分割实验结果

Figure 7. The results of the superpixel segmentation for the real-world PolSAR image

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图 8 实测极化SAR图像在不同参数值S，k和 $\mu$ 时的整体精度

Figure 8. The OAs for the real-world PolSAR image under different parameters of S, k and $\mu$

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图 9 仿真极化SAR图像5种算法的分类结果

Figure 9. Classification results of five methods for the simulated PolSAR image

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图 10 实测极化SAR图像5种算法的分类结果

Figure 10. Classification results of five methods for the real-world PolSAR image

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表 1 OM方法基于仿真数据的5种评价度量结果

Table 1. The five evaluation criteria of the OM method for the simulated PolSAR image

类别	类别1	类别2	类别3	类别4	UA
类别1	7822	26	19	2758	0.7362
类别2	3	9751	9	237	0.9751
类别3	12	47	9228	88	0.9843
类别4	14	96	11	9879	0.9879
PA	0.9963	0.9830	0.9958	0.7622
OA: 91.70%, K: 0.8894

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表 2 PM方法基于仿真数据的5种评价度量结果

Table 2. The five evaluation criteria of the PM method for the simulated PolSAR image

类别	类别1	类别2	类别3	类别4	UA
类别1	10308	26	19	272	0.9701
类别2	3	9751	9	237	0.9751
类别3	16	47	9228	84	0.9843
类别4	15	96	11	9878	0.9878
PA	0.9967	0.9830	0.9958	0.9434
OA: 97.91%, K: 0.9722

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表 3 两种算法基于实测极化SAR图像的整体精度和Kappa系数

Table 3. The OAs and Ks of two methods for the real-world PolSAR image

算法	精度
算法	K	OA (%)
OM算法	0.6193	78.51
PM算法	0.8097	89.36

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表 4 5种算法基于仿真极化SAR图像的3种评价度量结果

Table 4. The three evaluation criteria of five methods for the simulated PolSAR image

算法	精度
算法	类别1	类别2	类别3	类别4	K	OA (%)
UCSC	0.9400	0.9857	0.9919	0.9504	0.9541	96.56
UKWC	0.5012	0.0000	0.9919	0.9459	0.6194	71.62
GDWC	0.5071	0.2242	0.9958	0.9808	0.6470	73.68
CPWC	0.5007	1.0000	0.9312	0.1633	0.6118	71.05
PM	0.9967	0.9830	0.9958	0.9434	0.9722	97.91

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表 5 5种算法基于实测极化SAR图像的3种评价度量结果

Table 5. The three evaluation criteria of five methods for the real-world PolSAR image

算法	精度
算法	林地	开放区1	开放区2	K	OA (%)
UCSC	0.9638	0.5477	0.0004	0.5940	77.09
UKWC	0.9657	0.8154	0.5011	0.6666	79.81
GDWC	0.9514	0.6030	0.7830	0.6645	80.74
CPWC	0.9984	0.4307	0.0014	0.4407	65.73
PM	0.9707	0.7461	0.9226	0.8097	89.36

下载: 导出CSV

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