基于遥感影像边界特性对南海海洋内波检测算法研究 *

doi:10.11978/2019120

热带海洋学报 ›› 2020, Vol. 39 ›› Issue (6): 41-56.doi: 10.11978/2019120CSTR: 32234.14.2019120

基于遥感影像边界特性对南海海洋内波检测算法研究 ^*

郑应刚(), 张洪生(), 李晓恋, 张周昊

上海海事大学海洋科学与工程学院, 上海 201306

收稿日期:2019-11-23 修回日期:2020-01-22 出版日期:2020-10-10 发布日期:2020-04-09
通讯作者: 张洪生
作者简介:郑应刚(1995—), 男, 四川省梓潼县人, 博士研究生, 从事海洋遥感及水波动力学理论及其应用研究。email: ingopro@qq.com
基金资助:
国家自然科学基金项目(51679132);国家自然科学基金项目(51079082);上海市地方高校基地能力建设项目(17040501600)

Detection algorithm of internal waves in the South China Sea based on boundary characteristics of remote sensing image

ZHENG Yinggang(), ZHANG Hongsheng(), LI Xiaolian, ZHANG Zhouhao

College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 201306, China

Received:2019-11-23 Revised:2020-01-22 Online:2020-10-10 Published:2020-04-09
Contact: ZHANG Hongsheng
Supported by:
National Natural Science Foundation of China(51679132);National Natural Science Foundation of China(51079082);Capacity Construction Project of Local University Base of Shanghai Science and Technology Commission(17040501600)

摘要/Abstract

摘要：

海洋内波在海洋活动中扮演重要角色。海洋内波研究对我国海洋科学的理论研究、海洋资源的保护、开发和利用以及海洋军事等方面均具有重要意义。为了及时发现海洋内波的发生地点以及对海洋内波参数进行定量分析, 本研究基于合成孔径雷达(synthetic aperture radar, SAR)影像中内波明暗条纹的边界特性, 提出了一种集成的海洋内波检测算法: 主要运用列分离邻域处理和Canny算子边缘检测算法对条纹进行检测, 并利用海洋内波的轮廓长度、面积比值及传播方向三个特征对条纹进行筛选, 并将该算法应用于南海发生的多起海洋内波, 以验证算法的鲁棒性和适用性。研究结果表明, 该算法能够较好地识别出海洋内波的明暗条纹, 不仅能够除去非内波条纹的轮廓, 而且能够去除一些细小且不明显的内波条纹轮廓。利用余弦函数逐像素地对识别后的条纹进行拟合, 然后根据拟合结果找出明暗条纹所在位置以及相邻明暗条纹之间的间距, 从而判定出内波发生的位置。

关键词: 海洋内波, SAR, Canny算子, 轮廓分类, 面积比, 方向特性, 余弦拟合

Abstract:

Ocean internal waves play an important role in the activities of ocean. The study of ocean internal waves is of great significance to theoretical research of marine science, the protection, development and utilization of marine resources, and maritime military in China. In order to find the location of ocean internal waves in time and quantitatively analyze the parameters of ocean internal waves, an integrated detection algorithm is proposed on the basis of the boundary characteristics of light and dark stripes of ocean internal waves in Synthetic Aperture Radar (SAR) images. The stripes are detected through the process of column separation neighborhood and the edge detection algorithm of Canny operator, and selected by the use of the three features of ocean internal waves including contour length, area ratio, and propagation direction. The algorithm is applied to several internal waves in the South China Sea to verify its robustness and applicability. The results show that the proposed algorithm can effectively identify the light and dark stripes of ocean internal waves, and can remove not only the contour of the non-internal wave stripes but also the small and unobvious stripes contours of internal waves. The detected stripes are fitted pixel by pixel with cosine function, and the locations of the light and dark stripes and the distance between the light and dark stripes are found based on the fitted results. The location of the internal waves is thus determined.

Key words: ocean internal waves, SAR, Canny operator, contour classification, area ratio, directional characteristic, cosine fitting

中图分类号:

P731.24

郑应刚, 张洪生, 李晓恋, 张周昊. 基于遥感影像边界特性对南海海洋内波检测算法研究 ^*[J]. 热带海洋学报, 2020, 39(6): 41-56.

ZHENG Yinggang, ZHANG Hongsheng, LI Xiaolian, ZHANG Zhouhao. Detection algorithm of internal waves in the South China Sea based on boundary characteristics of remote sensing image[J]. Journal of Tropical Oceanography, 2020, 39(6): 41-56.

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	均值	方差	等效视数	运行时间/s
原始图像	57.81	376.75	8.87	10.61
窗口2×2	17.46	68.56	4.45	52.49
窗口5×5	44.75	359.10	5.58	18.15
窗口8×8	54.74	495.51	6.05	14.01

$k$	${{x}_{0}}$	${{y}_{0}}$
1.61	114.17	808.82
1.44	736.52	684.87
-5.33	66.95	337.77
1.69	601.60	528.59

皮尔逊系数	相关性
0.8~1.0	极强相关
0.6~0.8	强相关
0.4~0.6	中等程度相关
0.2~0.4	弱相关
0~0.2	极弱相关或无相关

皮尔逊系数	百分比
>0.5	87.7%
>0.6	80.8%
>0.7	72.1%
>0.8	59.3%
>0.9	43.2%

样本	像素	发生时间	中心经纬度	经纬度范围
A	8415×9558	2005-05-09, 02:16	21°45′N, 116°28′E	21°06′—22°18′N, 115°52′—117°05′E
B	7913×8647	2002-06-24, 02:45	20°57′N, 116°13′E	20°21′—21°26′N, 115°40′—116°47′E
C	8395×9813	2007-09-12, 02:13	21°16′N, 117°06′E	20°37′—21°54′N, 116°29′—117°28′E
D	4899×6121	2005-08-22, 02:45	21°21′N, 116°18′E	20°43′—21°59′N, 115°45′—116°55′E
E	6649×9830	2009-06-12, 02:31	20°28′N, 112°30′E	19°51′—20°05′N, 112°01′—113°01′E

基于遥感影像边界特性对南海海洋内波检测算法研究 ^*

Detection algorithm of internal waves in the South China Sea based on boundary characteristics of remote sensing image

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参考文献 27

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样本数	>0.5	>0.6	>0.7	>0.8	>0.9
4858	80.2%	72.4%	62.1%	50.8%	39.4%
8380	89.7%	84.6%	77.0%	66.3%	49.8%
3966	84.1%	75.3%	64.1%	51.7%	36.9%
9443	90.1%	84.4%	75.8%	63.0%	45.1%
2862	78.5%	68.7%	58.4%	48.2%	38.7%

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