一种超分辨SAR图像水域分割算法及其应用

陈嘉琪; 刘祥梅; 李宁; 张燕

doi:10.11999/JEIT200366

一种超分辨SAR图像水域分割算法及其应用

doi: 10.11999/JEIT200366

陈嘉琪^1, ,,
刘祥梅¹,
李宁^{2, 3},
张燕¹

1.
河海大学计算机与信息学院南京 211100
2.
河南大学计算机与信息工程学院开封 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: 国家自然科学基金(61771183, 61601437)，中央高校基础研究基金(2016B07114)，河南省科技攻关计划项目(192102210082)，河南省青年人才托举工程(2019HYTP006)，中国博士后科学基金(2013M541035)

详细信息

作者简介:
陈嘉琪：男，1984年生，副教授，硕士生导师，研究方向为雷达信号处理及应用，计算电磁学

刘祥梅：男，1997年生，硕士生，研究方向为SAR图像处理

李宁：男，1987年生，教授，博士生导师，研究方向为SAR信号与图像处理

张燕：女，1994年生，硕士生，研究方向为遥感图像超分辨率重建

通讯作者:
陈嘉琪　cjq19840130@163.com

中图分类号: TN959.1
计量
- 文章访问数: 1434
- HTML全文浏览量: 368
- PDF下载量: 121
- 被引次数: 0
出版历程
- 收稿日期: 2020-05-08
- 修回日期: 2020-12-05
- 网络出版日期: 2020-12-17
- 刊出日期: 2021-03-22

A High-precision Water Segmentation Algorithm for SAR Image and its Application

Jiaqi CHEN^{1
, ,},
Xiangmei LIU¹,
Ning LI^{2, 3},
Yan ZHANG¹

1.
College of Computer and Information, Hohai University, Nanjing 211100, China
2.
School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
3.
Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China

Funds: The National Natural Science Foundation of China (61771183, 61601437), The Fundamental Research Funds for the Central University (2016B07114), The Plan of Science and Technology of Henan Province (192102210082), The Youth Talent Lifting Project of Henan Province (2019HYTP006), China Postdoctoral Science Foundation (2013M541035)

摘要

摘要: 合成孔径雷达(SAR)图像水域分割在湖泊、河流等陆地水文监测领域有重要的研究意义。由于SAR图像分辨率不足所导致的陆地与水域边界模糊, 会影响水域分割精度。该文以中国青藏高原地区的多庆错湖为研究对象，使用Sentinel-1A SAR图像数据，综合运用深度残差模型、通道注意力与亚像素卷积，提出一种基于亚像素卷积的增强型通道注意力深度残差超分辨网络，对滤波后的SAR图像进行重建、水域轮廓提取与精度分析。通过比较不同超分辨算法下的重建结果及水域轮廓提取精度，该文算法在重建效果与提取精度上都较传统方法有明显提升，并具有很好的鲁棒性。
- 合成孔径雷达 /
- 超分辨率重建 /
- 水域分割
Abstract: Water segmentation of Synthetic Aperture Radar (SAR) is of great significance in land hydrological monitoring, such as lakes and rivers. Water segmentation accuracy is influenced by the blurring of the boundary between land and water region because of the insufficient resolution of SAR image. Sentinel-1A SAR image is used to study the Duoqing Co in the Tibetan Plateau of China. This paper integrates the enhanced deep residual block, channel attention mechanism and sub-pixel convolution, an enhanced channel attention deep residual network is proposed based on sub-pixel to reconstruct the filtered SAR image, extract the water contour and analyze the accuracy. By comparing the reconstruction results of different super-resolution algorithms and the accuracy of water contour extraction, this algorithm, with great robustness, is obviously better than the traditional method in both reconstruction effect and extraction accuracy.
- Synthetic Aperture Radar (SAR) /
- Super-resolution reconstruction /
- Water segmentation

HTML全文

图 1 算法整体流程图

下载: 全尺寸图片幻灯片

图 2 通道注意力机制示意图

下载: 全尺寸图片幻灯片

图 3 IEDSR网络损失值变化曲线

下载: 全尺寸图片幻灯片

图 4 3种超分辨算法对多庆错湖影像处理结果对比

下载: 全尺寸图片幻灯片

图 5 多庆错湖轮廓提取结果

下载: 全尺寸图片幻灯片

图 6 2015年9月至2016年8月应用IEDSR水域提取结果

下载: 全尺寸图片幻灯片

表 1 8组多庆错湖影像重建质量评估(PSNR(dB)/SSIM)

日期	Bicubic	SRCNN	本文IEDSR
20150902	27.41/0.76	28.46/0.82	28.22/0.85
20151016	24.64/0.68	25.38/0.75	26.39/0.85
20151125	27.05/0.78	28.21/0.84	28.28/0.86
20160401	25.86/0.78	26.90/0.84	27.43/0.89
20160417	27.09/0.83	28.46/0.88	27.93/0.89
20160612	24.12/0.76	24.99/0.83	24.90/0.86
20160730	27.71/0.79	28.94/0.85	29.04/0.88
20160823	28.61/0.81	29.55/0.86	29.88/0.88
平均值	26.56/0.77	27.61/0.83	27.76/0.87

下载: 导出CSV

表 2 不同时期轮廓提取精度比较(OM(%)/COM(%)/Dis像素)

日期	原图	Bicubic	SRCNN	本文IEDSR
20150902	0.39/0.29/0.73	0.26/0.13/0.44	0.22/0.11/0.35	0.14/0.13/0.29
20151016	0.26/1.14/1.03	0.22/0.56/0.61	0.18/0.64/0.60	0.05/0.78/0.60
20151125	0.12/2.83/2.32	0.17/1.14/1.03	0.08/1.24/1.01	0.02/1.24/0.97
20160401	0.12/2.74/2.25	0.19/0.88/0.83	0.08/0.93/0.77	0.01/0.64/0.51
20160417	0.28/10.57/2.96	0.50/3.84/1.80	0.43/3.24/1.50	0.02/2.44/0.99
20160612	0.62/12.93/2.70	0.61/4.41/0.89	0.48/4.43/0.86	0.04/3.15/0.55
20160730	0.23/0.54/0.77	0.13/0.28/0.41	0.05/0.51/0.57	0.01/0.28/0.30
20160823	0.16/0.30/0.52	0.01/0.11/0.22	0.09/0.05/0.15	0.05/0.08/0.10

下载: 导出CSV

参考文献(19)

朱立平, 张国庆, 杨瑞敏, 等. 青藏高原最近40年湖泊变化的主要表现与发展趋势[J]. 中国科学院院刊, 2019, 34(11): 1254–1263. doi: 10.16418/j.issn.1000-3045.2019.11.008

ZHU Liping, ZHANG Guoqing, YANG Ruimin, et al. Lake variations on Tibetan plateau of recent 40 years and future changing tendency[J]. Bulletin of Chinese Academy of Sciences, 2019, 34(11): 1254–1263. doi: 10.16418/j.issn.1000-3045.2019.11.008

SONG Kaishan, LIU Ge, WANG Qiang, et al. Quantification of lake clarity in China using Landsat OLI imagery data[J]. Remote Sensing of Environment, 2020, 243: 111800. doi: 10.1016/j.rse.2020.111800

李春升, 王伟杰, 王鹏波, 等. 星载SAR技术的现状与发展趋势[J]. 电子与信息学报, 2016, 38(1): 229–240. doi: 10.11999/JEIT151116

LI Chunsheng, WANG Weijie, WANG Pengbo, et al. Current situation and development trends of spaceborne SAR technology[J]. Journal of Electronics &Information Technology, 2016, 38(1): 229–240. doi: 10.11999/JEIT151116

GUO Yaru and ZHANG Jixian. A new 2D Otsu for water extraction from SAR image[J]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, XLⅡ-2/W7: 733–736. doi: 10.5194/isprs-archives-XLⅡ-2-W7-733-2017.

杜兰, 魏迪, 李璐, 等. 基于半监督学习的SAR目标检测网络[J]. 电子与信息学报, 2020, 42(1): 154–163. doi: 10.11999/JEIT190783

DU Lan, WEI Di, LI Lu, et al. SAR target detection network via semi-supervised learning[J]. Journal of Electronics &Information Technology, 2020, 42(1): 154–163. doi: 10.11999/JEIT190783

冷英, 刘忠玲, 张衡, 等. 一种改进的ACM算法及其在鄱阳湖水域监测中的应用[J]. 电子与信息学报, 2017, 39(5): 1064–1070. doi: 10.11999/JEIT160870

LENG Ying, LIU Zhongling, ZHANG Heng, et al. Improved ACM algorithm for Poyang lake monitoring[J]. Journal of Electronics &Information Technology, 2017, 39(5): 1064–1070. doi: 10.11999/JEIT160870

KASS M, WITKIN A, and TERZOPOULOS D. Snakes: Active contour models[J]. International Journal of Computer Vision, 1988, 1(1): 321–332. doi: 10.1007/BF00133570

LI Ning, WANG R, DENG Yunkai, et al. Waterline mapping and change detection of Tangjiashan dammed lake after Wenchuan earthquake from multitemporal high-resolution airborne SAR imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(8): 3200–3209. doi: 10.1109/JSTARS.2014.2345417

李宁, 牛世林. 基于局部超分辨重建的高精度SAR图像水域分割方法[J]. 雷达学报, 2020, 9(1): 174–184. doi: 10.12000/JR19096

LI Ning and NIU Shilin. High-precision water segmentation from synthetic aperture radar images based on local super-resolution restoration technology[J]. Journal of Radars, 2020, 9(1): 174–184. doi: 10.12000/JR19096

王钢, 周若飞, 邹昳琨. 基于压缩感知理论的图像优化技术[J]. 电子与信息学报, 2020, 42(1): 222–233. doi: 10.11999/JEIT190669

WANG Gang, ZHOU Ruofei, and ZOU Yikun. Research on image optimization technology based on compressed sensing[J]. Journal of Electronics &Information Technology, 2020, 42(1): 222–233. doi: 10.11999/JEIT190669

TSAI R Y and HUANG T S. Multiframe image restoration and registration[J]. Advances in Computer Vision and Image Processing, 1984, 1(2): 317–339.

HARDIE R C, BARNARD K J, and ARMSTRONG E E. Joint MAP registration and high-resolution image estimation using a sequence of undersampled images[J]. IEEE Transactions on Image Processing, 1997, 6(12): 1621–1633. doi: 10.1109/83.650116

KIM J, LEE J K, and LEE K M. Accurate image super-resolution using very deep convolutional networks[C]. 2016 IEEE on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 1646–1654. doi: 10.1109/CVPR.2016.182.

吴中海, 哈广浩, 赵根模, 等. 西藏亚东-谷露裂谷南段多庆错2016年4月异常干涸的构造成因[J]. 地球科学, 2018, 43(S2): 243–255. doi: 10.3799/dqkx.2018.204

WU Zhonghai, HA Guanghao, ZHAO Genmo, et al. Tectonic analysis on abnormal dried up of Duoqing co lake of southern section of Yadong-Gulu rift in South Tibet during April, 2016[J]. Earth Science, 2018, 43(S2): 243–255. doi: 10.3799/dqkx.2018.204

CHEN Jiaqi, WANG Qingwei, WANG Jian, et al. Change detection of water index in Danjiangkou reservoir using mixed log-normal distribution based active contour model[J]. IEEE Access, 2019, 7: 95430–95442. doi: 10.1109/ACCESS.2019.2929178

DONG Chao, LOY C C, HE Kaiming, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 295–307. doi: 10.1109/TPAMI.2015.2439281

张金松, 邢孟道, 孙光才. 一种基于密集深度分离卷积的SAR图像水域分割算法[J]. 雷达学报, 2019, 8(3): 400–412. doi: 10.12000/JR19008

ZHANG Jinsong, XING Mengdao, and SUN Guangcai. A water segmentation algorithm for SAR image based on dense depthwise separable convolution[J]. Journal of Radars, 2019, 8(3): 400–412. doi: 10.12000/JR19008

HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778. doi: 10.1109/CVPR.2016.90.

SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[C]. Proceedings of the 31st AAAI Conference on Artificial Intelligence, 2016.

施引文献

资源附件(0)

访问统计