Simulation analysis for remote sensing inversion of ocean wavelength and water depth by the Complex Morlet Wavelet method

doi:10.3969/j.issn.1000-5641.2021.04.015

Abstract

Abstract:

Using the wave-shaped features of remote sensing images, the wavelength of ocean waves can be determined based on the wavelet method. Shallow water depths can then be estimated from the wavelength because the wavelength becomes shorter as the water depth decreases. In this paper, remote sensing data were replaced by ideal elevation data, and numerical simulation data were used to study the performance of the Complex Morlet Wavelet method in estimating wavelength and water depth. In particular, the effects of data resolution and sub-image size on water depth estimation were explored. The results from the ideal elevation data shows that: when the wavelength has no spatial change and the size of the sub-image is greater than the wavelength, the data resolution has no substantial effect on the wavelength estimation if there are more than nine evenly distributed data grids in one image. This phenomenon can be explained by the wavelength-energy spectrum. When the wavelength changes spatially, accurate estimation of the wavelength requires that the sub-image size is larger than twice the wavelength and there are four data grids in one wavelength. The estimation of wavelength by numerical simulated data requires a similar size for sub-images and the data number. The error of water depth estimation increases slightly if the sub-image size is too large, and also increases slightly as the resolution of the data decreases.

Key words: Complex Morlet Wavelet method, wavelength inversion, water depth inversion, simulation analysis

CLC Number:

P714

Shanling CHENG, Shouxian ZHU, Gui ZHANG, Wenjing ZHANG. Simulation analysis for remote sensing inversion of ocean wavelength and water depth by the Complex Morlet Wavelet method[J]. Journal of East China Normal University(Natural Science), 2021, 2021(4): 134-144.

Figures/Tables 14

Table 1

Fig.1

Fig.2

Table 2

Fig.3

Table 3

Fig.4

Table 4

Fig.5

Fig.6

Table 5

Fig.7

Table 6

Table 7

References 17

1	曹彬才. 遥感测深数据处理方法研究 [D]. 郑州: 战略支援部队信息工程大学, 2017.
2	周高伟, 李英成, 任延旭, 等. 低空无人机双介质水下礁盘深度测量试验与分析. 测绘学报, 2015, 44 (5): 548- 554.
3	翟国君, 吴太旗, 欧阳永忠, 等. 机载激光测深技术研究进展. 海洋测绘, 2012, 32 (2): 67- 71. doi: 10.3969/j.issn.1671-3044.2012.02.021
4	郭晓雷, 邱振戈, 沈蔚, 等. 基于WorldView-2遥感影像的龙湾港浅海水深反演. 海洋学研究, 2017, 35 (3): 27- 33. doi: 10.3969/j.issn.1001-909X.2017.03.004
5	张晓冬, 张文静, 朱首贤, 等. 海口湾可见光遥感测深方法研究. 海洋通报, 2016, 35 (1): 54- 63.
6	黄韦艮, 傅斌, 周长宝, 等. 星载SAR遥感浅海水下地形的最佳海况模拟仿真. 自然科学进展: 国家重点实验室通讯, 2000, 10 (7): 642- 649. doi: 10.3321/j.issn:1002-008X.2000.07.008
7	范开国, 黄韦艮, 贺明霞, 等. SAR浅海水下地形遥感研究进展. 遥感技术与应用, 2008, 23 (4): 479- 485.
8	滕惠忠, 熊显名, 李海滨, 等. 遥感水深反演海图修测应用研究. 海洋测绘, 2009, 29 (6): 21- 25. doi: 10.3969/j.issn.1671-3044.2009.06.006
9	郭晓雷. 基于卫星多光谱影像的浅海水深反演研究 [D]. 上海: 上海海洋大学, 2017.
10	BELL P. Shallow water bathymetry derived from an analysis of X-band marine radar images of waves. Coastal Engineering, 1999, 37, 513- 527. doi: 10.1016/S0378-3839(99)00041-1
11	LEU L, CHANG H. Remotely sensing in detecting the water depths and bed load of shallow waters and their changes. Ocean Engineering, 2005, 32, 1174- 1198. doi: 10.1016/j.oceaneng.2004.12.005
12	LI J, ZHANG H, HOU P, et al. Mapping the bathymetry of shallow coastal water using single-frame fine-resolution optical remote sensing imagery. Acta Oceanologica Sinica, 2016, 35 (1): 60- 66. doi: 10.1007/s13131-016-0797-x
13	沈斯敏, 朱首贤, 康彦彦, 等. 基于快速傅里叶变换方法遥感反演海浪波长和水深的仿真分析. 华东师范大学学报(自然科学版), 2019, (2): 184- 194. doi: 10.3969/j.issn.1000-5641.2019.02.020
14	POUPARDIN A, IDIER D, MICHELE M D, et al. Water depth inversion from a single SPOT-5 dataset. IEEE Transactions on Geoence and Remote Sensing, 2016, 54 (4): 2329- 2342. doi: 10.1109/TGRS.2015.2499379
15	刘春媛. 复Morlet小波光学三维轮廓提取关键参数快速设定方法. 黑龙江科技学院学报, 2013, 23 (2): 191- 195.
16	叶安乐, 李凤岐. 物理海洋学 [M]. 山东青岛: 青岛海洋大学出版社, 1992.
17	KIRBY J T, WEI G, CHEN Q, et al. FUNWAVE 1.0: Fully nonlinear Boussinesq wave model documentation and user’s manual [R]. Newark, DE: University of Delaware, 1998.

子图长度	不同分辨率资料反演的波长/m
子图长度	0.25 m 分辨率	0.5 m 分辨率	1 m 分辨率	2 m 分辨率	4 m 分辨率	8 m 分辨率	16 m 分辨率	32 m 分辨率
128 m	77.8	77.8	76.9	76.9	76.9	77.9	81.5	99.7
256 m	81.4	81.3	81.8	81.8	82.4	82.7	81.6	79.5
512 m	82.1	81.9	82.3	83.8	82.7	82.2	81.3	83.6
1024 m	82.0	82.5	82.5	82.5	82.6	82.6	81.2	80.0

子图长度	不同分辨率资料反演的波长误差/m
子图长度	1 m 分辨率	2 m 分辨率	4 m 分辨率	8 m 分辨率	16 m 分辨率	32 m 分辨率
128 m	3.51	4.01	4.37	4.36	4.55	9.60
256 m	1.69	1.47	1.68	1.67	1.78	6.05
512 m	1.88	1.83	2.01	1.96	2.15	3.28
1024 m	1.91	1.88	1.76	1.59	1.78	3.57
10000 m	2.33	2.30	2.33	2.55	2.16	3.28

子图长度	不同分辨率资料反演的波长误差/m
子图长度	1 m 分辨率	2 m 分辨率	4 m 分辨率	8 m 分辨率	16 m 分辨率	32 m 分辨率
128 m	12.68	12.56	12.65	12.97	12.97	33.83
256 m	3.24	3.30	3.51	4.29	4.28	21.60
512 m	1.56	1.58	1.71	1.88	1.89	9.36
1024 m	2.09	2.07	1.58	1.65	1.65	2.77
2000 m	2.19	2.16	1.88	2.34	1.52	3.33

子图长度	不同比值(A₁/A₂)组合波反演的波长/m
子图长度	1.05 比值	1.10 比值	1.15 比值	1.20 比值	1.25 比值	1.30 比值
128 m	83.0	83.0	83.0	81.4	81.4	79.9
256 m	88.5	86.5	85.0	85.0	85.0	84.5
512 m	106.7	106.7	82.1	81.1	81.1	80.1
1024 m	90	89	87	86	86	86

子图长度	不同分辨率资料反演斜坡地形的平均绝对误差/m
子图长度	1 m 分辨率	2 m 分辨率	4 m 分辨率	8 m 分辨率	16 m 分辨率	32 m 分辨率
128 m	1.36	1.38	1.32	1.38	1.58	7.08
256 m	0.84	0.84	0.75	0.74	1.07	4.57
512 m	0.84	0.85	0.71	0.73	0.81	3.37
1024 m	0.91	0.91	0.74	0.76	0.78	3.17
4000 m	0.74	0.78	0.84	0.80	0.79	2.19