River-Net：面向河道提取的Refined-Lee Kernel深度神经网络模型

李宁; 郭志顺; 毋琳; 赵建辉

doi:10.12000/JR21148

River-Net：面向河道提取的Refined-Lee Kernel深度神经网络模型

doi: 10.12000/JR21148

李宁^{1, 2, 3},
郭志顺¹,
毋琳^{1, 2, 3, ,},
赵建辉^{1, 2, 3}

1.
河南大学计算机与信息工程学院开封 475004
2.
河南省智能技术与应用工程技术研究中心开封 475004
3.
河南省大数据分析与处理重点实验室开封 475004

基金项目: 河南省重大公益项目(201300311300)，河南省科技攻关计划项目(212102210101, 212102210093)，国家自然科学基金(61871175)

详细信息

作者简介:
李　宁(1987–)，男，安徽人，于中国科学院电子学研究所获得博士学位，现为河南大学教授，博士生导师，研究方向为多模式合成孔径雷达成像及其应用技术。担任《雷达学报》客座编辑、《电子与信息学报》青年编委等学术兼职

郭志顺(1995–)，男，河南人，河南大学计算机与信息工程学院在读硕士研究生，主要研究方向为合成孔径雷达图像处理及其应用技术

毋　琳(1978–)，女，河南人，河南大学副教授，硕士生导师，主要研究方向为SAR图像处理技术、水环境SAR图像应用

赵建辉(1980–)，男，河南人，河南大学副教授，硕士生导师，主要研究方向为SAR图像处理

通讯作者:
毋琳 henuwl@henu.edu.cn

责任主编：匡纲要 Corresponding Editor: KUANG Gangyao
中图分类号: TN959.1; TP183
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- 被引次数: 0
出版历程
- 收稿日期: 2021-10-09
- 修回日期: 2021-12-06
- 网络出版日期: 2021-12-27
- 刊出日期: 2022-06-28

River-Net: A Novel Neural Network Model for Extracting River Channel Based on Refined-Lee Kernel

LI Ning^{1, 2, 3},
GUO Zhishun¹,
WU Lin^{1, 2, 3
, ,},
ZHAO Jianhui^{1, 2, 3}

1.
College of Computer and Information Engineering, Henan University, Kaifeng 475004, China
2.
Henan Engineering Research Center of Intelligent Technology and Application, Kaifeng 475004, China
3.
Henan Key Laboratory of Big Data Analysis and Processing, Kaifeng 475004, China

Funds: Major Public Welfare Projects in Henan Province (201300311300), The Plan of Science and Technology of Henan Province (212102210101, 212102210093), The National Natural Science Foundation of China (61871175)

More Information

Corresponding author: WU Lin, henuwl@henu.edu.cn

摘要

摘要: 高精度提取合成孔径雷达(SAR)图像中的河流边界，对河流水势监测具有重要意义。以检测郑州7·20暴雨后黄河的健康状况为实施例，该文融合精致Lee滤波思想与卷积操作的滤波特性，提出了基于河道几何特性的优化内部权值卷积核Refined-Lee Kernel，进而提出了一种新型河道提取深度神经网络模型，即River-Net。为验证所提模型的有效性，该文获取了郑州7·20暴雨前后两景欧空局Sentinel-1卫星20 m分辨率干涉宽幅(IW)影像数据，利用暴雨前的影像对模型进行训练，用于提取暴雨后的黄河河道，分析黄河在暴雨后的涨势情况。实验结果表明，相比主流语义分割模型，所提模型能够更精确地在SAR图像中提取河道，对洪水灾害的检测与评估有重要应用价值。
- 合成孔径雷达(SAR) /
- Refined-Lee Kernel /
- 精致Lee滤波 /
- 神经网络 /
- 河道提取
Abstract: High-precision extraction of river boundaries in Synthetic Aperture Radar (SAR) images is of great significance in monitoring rivers. In this paper, the detection of the health of the Yellow River after the rainstorm in 20 July, 2021 in Zhengzhou is the focus of this paper. The refined-Lee filtering concept and the filtering characteristics of the convolution operation are combined, and an optimized internal weight convolution kernel Refined-Lee Kernel is proposed according to the geometric characteristics of the river channel. A novel river extraction deep neural network model, the River-Net, is also proposed. To verify the effectiveness of the proposed model, this article utilized 20 m resolution Interferometric Wideswath (IW) image data obtained from the European Space Agency Sentinel-1 satellite before and after the 20 July rainstorm in Zhengzhou, employing the images before the rainstorm to train the model. The model, after training, was used to extract the Yellow River channel and analyze the rise of the river after the rainstorm. Experimental results show that the proposed model can extract river channels from SAR images more accurately than trendy semantic segmentation models. The model has important application value for flood disaster detection and evaluation.
- Synthetic Aperture Radar (SAR) /
- Refined-Lee Kernel /
- Refined-Lee filter /
- Neural network /
- River channel extraction

HTML全文

图 1 一般卷积与空洞卷积对比图

Figure 1. Comparison diagram of convolution and dilated convolution

下载: 全尺寸图片幻灯片

图 2 金字塔池化操作示意图

Figure 2. The schematic diagram of spatial pyramid pooling

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图 3 精致 Lee 滤波 8 种模板示意图

Figure 3. Schematic diagram of refined Lee filter template

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图 4 RLK 模块

Figure 4. RLK module

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图 5 River-Net 结构示意图

Figure 5. River-Net structure

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图 6 研究区域示意图

Figure 6. Region of interest

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图 7 数据集制作示意图

Figure 7. Schematic diagram of generating data set

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图 8 不同网络的特征图提取与对比

Figure 8. Feature map extraction and comparison of different networks

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图 9 不同网络分割结果对比

Figure 9. Comparison with segmentation results of different networks

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图 10 郑州 7·20 暴雨前后部分黄河提取结果

Figure 10. Part extraction results of the Yellow River before and after the Zhengzhou 7·20 rainstorm

下载: 全尺寸图片幻灯片

表 1 混淆矩阵

Table 1. Confusion matrix

混淆矩阵		真实值
混淆矩阵		河道	背景
预测值	河道	TP	FP
预测值	背景	FN	TN

下载: 导出CSV

表 2 分割结果评价

Table 2. Evaluation of segmentation results

	Algorithms/Models	Precision (%)	Recall (%)	IoU (%)	F1-score (%)
传统方法	OTSU	93.05	84.74	81.99	88.70
	K-means	95.10	87.18	86.67	90.97
	ACM	92.08	84.08	81.99	87.90
深度学习	U-Net	95.70	91.03	88.00	93.30
	U-Net+RLK	96.22	93.18	91.92	94.68
	DeepLab	95.42	91.13	89.40	93.23
	PSPNet	96.06	92.73	90.04	94.36
	PSPNet+RLK	97.17	93.24	93.36	95.16
	River-Net without RLK	96.33	93.36	91.03	94.82
	River-Net	97.32	94.40	92.93	95.84

下载: 导出CSV

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