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

Abstract

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

CLC Number:

P731.24

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.

Figures/Tables 28

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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

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

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References 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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