基于深度卷积神经网络和条件随机场模型的PolSAR图像地物分类方法

胡涛; 李卫华; 秦先祥; 王鹏; 余旺盛; 李军

doi:10.12000/JR18065

基于深度卷积神经网络和条件随机场模型的PolSAR图像地物分类方法

DOI: 10.12000/JR18065

胡涛^1,2,,
李卫华^1,,
秦先祥^1, ,,
王鹏^1,,
余旺盛^1,,
李军^1,

①.
空军工程大学信息与导航学院西安 710077
②.
国防科技大学电子对抗学院合肥 230037

基金项目: 国家自然科学基金(41601436, 61403414, 61703423)，陕西省自然科学基础研究计划(2018JM4029)

详细信息

作者简介:
胡　涛(1994–)，男，湖南浏阳人，空军工程大学信息与导航学院硕士，研究方向为计算机视觉。E-mail: 1862965@163.com

李卫华(1964–)，男，空军工程大学信息与导航学院教授，研究方向为指挥信息系统。E-mail: lwh_kgd@163.com

秦先祥(1986–)，男，广西阳朔人，空军工程大学信息与导航学院讲师，研究方向为SAR图像处理与分析。E-mail: qinxianxiang@126.com

王　鹏(1985–)，男，空军工程大学信息与导航学院副教授，硕士生导师，研究方向为信息融合处理与分布式协同控制。E-mail: wangpeng@163.com

余旺盛(1985–)，男，湖南平江人，空军工程大学信息与导航学院讲师，研究方向为计算机视觉与图像处理。E-mail: 853994682@qq.com

李　军(1983–)，男，湖南邵阳人，空军工程大学信息与导航学院讲师，研究方向为信息处理技术。E-mail: 108857769@qq.com

通讯作者:
秦先祥 qinxianxiang@126.com

中图分类号: TP391
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出版历程
- 收稿日期: 2018-08-31
- 修回日期: 2018-12-26
- 网络出版日期: 2019-08-28

Terrain Classification of Polarimetric Synthetic Aperture Radar Images Based on Deep Learning and Conditional Random Field Model

HU Tao^{1,2
,},
LI Weihua^1
,,
QIN Xianxiang^{1
, ,},
WANG Peng^1
,,
YU Wangsheng^1
,,
LI Jun^1
,

①.
College of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
②.
College of Electronic Countermeasure, National University of Defense Technology, Hefei 230037, China

Funds: The National Natural Science Foundation of China (41601436, 61403414, 61703423), The Natural Science Foundation Research Project of Shaanxi Province (2018JM4029)

More Information

Corresponding author: QIN Xianxiang, qinxianxiang@126.com

摘要

摘要: 近年来，极化合成孔径雷达(PolSAR)图像地物分类得到了深入研究。传统的PolSAR图像地物分类方法采用的特征往往需要针对具体问题进行设计，特征表征性不强。因此，该文提出一种基于卷积神经网络(CNN)和条件随机场(CRF)模型的PolSAR图像地物分类方法。利用预训练好的实现图像分类任务的卷积神经网络模型(VGG-Net-16)提取表征能力更强的图像特征，再通过CRF模型对多特征及上下文信息的有效利用来实现图像的地物分类。实验结果表明，与3种利用传统经典特征的方法相比，该方法能够提取更有效的特征，取得了更高的总体分类精度和Kappa系数。
- 极化SAR /
- 地物分类 /
- 条件随机场 /
- 卷积神经网络
Abstract: In recent years, Polarimetric Synthetic Aperture Radar (PolSAR) image classification has been investigated extensively. The traditional PolSAR image terrain classification methods result in a weak feature representation. To overcome this limitation, this study aims to propose a terrain classification method based on deep Convolutional Neural Network (CNN) and Conditional Random Field (CRF). The pre-trained VGG-Net-16 model was used to extract more powerful image features, and then the terrain from the images was classified through the efficient use of multiple features and context information by conditional random fields. The experimental results show that the proposed method can extract more features effectively in comparison with the three methods using traditional classical features and it can also achieve a higher overall accuracy and Kappa coefficient.
- Polarimetric Synthetic Aperture Radar (PolSAR) /
- Terrain classification /
- Conditional Random Field (CRF) /
- Convolutional Neural Network (CNN)

HTML全文

图 1 深度CRF模型流程图

Figure 1. The flow chart of deep CRF model

下载: 全尺寸图片幻灯片

图 2 Flevoland数据分类结果对比图

Figure 2. Comparison of Flevoland data classification results

下载: 全尺寸图片幻灯片

图 3 Oberpfaffenhofen数据分类结果对比图

Figure 3. Comparison of Oberpfaffenhofendata classification results

下载: 全尺寸图片幻灯片

图 4 不同层特征分类精度对比图

Figure 4. Accuracy comparison results of different layer classification results

下载: 全尺寸图片幻灯片

表 1 传统方法中用到的特征

Table 1. The features used in the traditional methods

Cloude分解	Freeman分解	协方差矩阵对角线
$H,\alpha ,A,{\lambda _{1}},{\lambda _{{2}}},{\lambda _{{3}}}$	Ps, Pd, Pv	C11, C22, C33

下载: 导出CSV

表 2 Flevoland数据分类精度

Table 2. The classification accuracy of Flevoland data

类别	方法1	方法2	方法3	方法4	方法5	本文方法
豆类	0.971	0.833	0.967	0.863	0.920	0.808
森林	0.759	0.940	0.733	0.943	0.945	0.868
土豆	0.680	0.840	0.821	0.578	0.872	0.808
苜蓿	0.609	0.892	0.719	0.781	0.932	0.990
小麦	0.934	0.881	0.864	0.792	0.936	0.981
裸地	0.514	0.871	0.903	0.980	0.998	0.899
甜菜	0.913	0.903	0.895	0.905	0.897	0.978
油菜籽	0.572	0.782	0.627	0.758	0.934	0.964
豌豆	0.589	0.821	0.820	0.801	0.901	0.854
草地	0.962	0.774	0.838	0.912	0.802	0.968
水体	0.701	0.970	0.526	0.703	0.988	0.888
总精度	0.751	0.870	0.778	0.797	0.933	0.905
Kappa系数	0.720	0.854	0.752	0.774	0.911	0.890
训练(s)	798	771	877	1211	7066	1052
测试(s)	2.9	2.7	3.0	4.1	8.4	3.8

下载: 导出CSV

表 3 Oberpfaffenhofen数据分类精度

Table 3. The classification accuracy of Oberpfaffenhofen data

类别	方法1	方法2	方法3	本文方法
建筑区域	0.696	0.645	0.712	0.903
林地	0.895	0.896	0.700	0.777
开放区域	0.622	0.843	0.874	0.947
总精度	0.691	0.804	0.800	0.903
Kappa系数	0.529	0.680	0.668	0.834

下载: 导出CSV

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