CN111781146A - Wave parameter inversion method using high-resolution satellite optical image - Google Patents
Wave parameter inversion method using high-resolution satellite optical image Download PDFInfo
- Publication number
- CN111781146A CN111781146A CN202010612160.7A CN202010612160A CN111781146A CN 111781146 A CN111781146 A CN 111781146A CN 202010612160 A CN202010612160 A CN 202010612160A CN 111781146 A CN111781146 A CN 111781146A
- Authority
- CN
- China
- Prior art keywords
- wave
- image
- sea
- obtaining
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1793—Remote sensing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Discrete Mathematics (AREA)
- Immunology (AREA)
- General Health & Medical Sciences (AREA)
- Biochemistry (AREA)
- Analytical Chemistry (AREA)
- Pathology (AREA)
- Algebra (AREA)
- Life Sciences & Earth Sciences (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Image Processing (AREA)
Abstract
The invention relates to a wave parameter inversion method by using a high-resolution satellite optical image, belonging to the technical field of ocean wave parameter extraction. The invention comprises the following steps: the method comprises the following steps: preprocessing an original optical image: step two: acquiring a gray value over the ocean; step three: segmenting an image window; step four: strengthening and identifying image characteristics; step five: acquiring an energy spectrum; step six: acquiring the propagation direction of sea waves; step seven: acquiring a radial wave number spectrum; step eight: acquiring a dominant wavelength lambda; step nine: calculating a main wave frequency f and a period T; step ten: obtaining wave information: and sliding the window to obtain the wave propagation direction, wavelength and period of other sub-windows, thereby obtaining wave information in any region in the satellite observation range. The invention can avoid artificial interference calculation by using a two-dimensional Fourier window segmentation sliding technology, obtain accurate wave direction and further obtain information such as wavelength, period and the like.
Description
Technical Field
The invention relates to a wave parameter inversion method by using a high-resolution satellite optical image, belonging to the technical field of ocean wave parameter extraction.
Background
Ocean wave information is important content in ocean service observation, ocean observation in China is mainly developed through a wave buoy of an anchor system at present, the wave buoy can provide information such as wave period, wave direction and wave height once per hour, in addition, two-dimensional near-shore wave information can be observed through a shore-based X-band wave measuring radar, but the observation cost is high, the potential of large-scale popularization is not provided, a microwave scatterometer carried by a satellite can also observe sea surface wave height, but the resolution ratio is low, and coastal areas are often easily interfered by land signals. The applicant knows that wave information is currently inverted by utilizing satellite remote sensing images such as resource III and the like, but the calculation data needs to be started through human intervention when the wave direction is calculated, namely the calculation of the wave direction is determined by the visual direction, so that the calculation of the wave direction is not accurate. But the main defects of the prior art are as follows: 1) buoy fixed-point observation belongs to time domain measurement, and wave information extraction in a space domain cannot be realized; 2) the inversion accuracy of the sea wave under small wind waves is low; 3) the shore-based wave-measuring radar has high cost; 4) the satellite microwave sensor has low resolution.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a wave parameter inversion method by using a high-resolution satellite optical image, and the two-dimensional Fourier window segmentation sliding technology is used to avoid artificial interference calculation, obtain an accurate wave direction and further obtain information such as wavelength, period and the like.
The invention relates to a wave parameter inversion method by using a high-resolution satellite optical image, which comprises the following steps:
the method comprises the following steps: preprocessing an original optical image: the method comprises the following steps:
the first step is as follows: calculating DN value difference: because different earth surfaces have gray DN value differences in four channels of red, green, blue and near infrared, the method is used for sea-land separation, finds out the most obvious wave band combination capable of effectively separating land and sea through spectral analysis, and respectively calculates the DN value differences of blue light, near infrared and green light;
the second step is that: calculating a mask matrix M: synthesizing two difference values to obtain a mask matrix M of a sea-land mask, wherein 1 represents sea and 0 represents land;
step two: obtaining the gray value over the sea: performing convolution operation on the mask matrix M and the image after four-channel fusion to obtain a gray value above the ocean, indicating that ocean information in a blue light channel of the GF-1 optical satellite image is stored most completely, and finally selecting the blue light channel to perform sea wave inversion;
step three: segmentation of image window: carrying out image window segmentation, and setting the size of a sub-window to be 1 km;
step four: image feature enhancement and identification: gradient vector information of the ocean surface reflectivity is obtained through sober operator calculation, and image feature strengthening and identification of wave crests and wave troughs of the sea waves are achieved;
G＝(Sx+iSy)*I (3)
in the formula: sxRepresenting the image subjected to the detection of the transverse edges, SyAn image representing longitudinal edge detection, G representing a gradient vector of sea surface reflectivity; i denotes a coefficient of an image for longitudinal edge detection, i 1.. n, n being the number of image elements; i represents an original image matrix;
step five: acquisition of energy spectrum: performing two-dimensional Fourier transform on the sub-window gradient vector information to obtain an energy spectrum;
step six: obtaining the wave propagation direction: obtaining the propagation direction of sea waves by carrying out statistical analysis on the energy spectrum;
step seven: obtaining a radial wave number spectrum: obtaining a radial wave number spectrum by performing radial one-dimensional Fourier change on the sub-window;
step eight: acquisition of dominant wavelength λ: obtaining a dominant wave wavelength lambda by counting wave numbers corresponding to energy peaks of the radial wave number spectrum;
step nine: calculation of dominant wave frequency f and period T: calculating a dominant wave frequency f and a period T by using a dispersion relation of wave fluctuation;
in the formula: f represents the wave frequency, T represents the period, lambda represents the wavelength, and g represents the acceleration of gravity;
step ten: obtaining wave information: and sliding the window to obtain the wave propagation direction, wavelength and period of other sub-windows, thereby obtaining wave information in any region in the satellite observation range.
Preferably, in the second step, the GF-1 optical satellite image adopts an optimal band combination technique for inverting the ocean waves.
Preferably, in the second step, the GF-1 optical satellite image is preprocessed to provide information of wave crests and wave troughs of the ocean surface.
Preferably, in the fifth step, the wave information after the GF-1 optical satellite image preprocessing is subjected to two-dimensional fourier transform, and 180-degree blur is eliminated by combining with the actual wind direction, so that the wave direction is obtained and is consistent with the wave buoy result.
Preferably, in the seventh step, the sea power density spectrum calculated in the x, y and radial directions by the one-dimensional fourier transform is used for judging the radial wavelength from the peak value, and the wave period is obtained through the dispersion relation and is consistent with the wave buoy and the GNSS buoy.
Preferably, in the step ten, the acquisition of the wave information needs to use a satellite remote sensing image and adopt a two-dimensional fourier and a one-dimensional radial fourier to perform a combined inversion of the wave direction and wavelength technology.
The invention has the beneficial effects that: 1) sea wave information extraction considering spatial characteristics; 2) extracting wave information under a low sea condition; 3) the wave observation cost is reduced, the conventional GF-1 optical image data has low acquisition cost and can be freely used for public use such as scientific research, and the conventional sensors such as a buoy have high price and are difficult to acquire data; 4) the space coverage range is improved, the high-resolution satellite can monitor most of the sea areas of the world, including open sea areas, the traditional methods such as buoy measurement and the like nearly provide measurement of limited points, and the space distribution is very limited; 5) the observation resolution can be improved, the GF-1 optical image resolution is 2m, and the coverage range of each scene can reach 50 kilometers.
Drawings
FIG. 1 is a schematic flow diagram of the invention as a whole.
FIG. 2 is a schematic flow chart of steps one to four of the present invention.
FIG. 3 is a schematic flow chart of steps five to six of the present invention.
Fig. 4 is a schematic flow chart of steps seven to ten of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
at present, the number of on-orbit operation of high-resolution satellites in China is increased day by day, the observation coverage range of oceans in China is enlarged day by day, ocean observation modes can be enriched by utilizing the high-resolution optical satellite images to invert ocean waves, ocean observation and scientific research are facilitated, and the popularization of the application of the high-resolution optical satellites in China is facilitated. Compared with the prior art, the invention has the following characteristics:
according to the invention, sea wave element information can be obtained by high-resolution satellite optical image inversion. The observation capability of the traditional wave buoy on the calm sea condition is limited by the sensitivity of an instrument system, and an effective signal is usually submerged by noise. By the method and the device, the wave wavelength, period and direction information of the sea waves can be obtained. The coverage area of the satellite optical image is wider, and the space distribution state of sea waves can be effectively reflected. Compared with the near-shore business wave buoy, the satellite optical image can be implemented in the global sea area without being limited by regions.
A wave test of national high resolution No. 1 (GF-1) optical image inversion is developed in Shandong coastal areas, accuracy verification is carried out by using a wave buoy of an ocean station and a GNSS height measurement buoy, and comparison shows that the wave information obtained by the method is high in accuracy and has good applicability under the condition of small wind waves.
As shown in fig. 1, the wave parameter inversion method using high resolution satellite optical images according to the present invention includes the following steps:
the method comprises the following steps: preprocessing of the original optical image, as shown in fig. 2: the method comprises the following steps:
the first step is as follows: calculating DN value difference: because different earth surfaces have gray DN value differences in four channels of red, green, blue and near infrared, the method is used for sea-land separation, finds out the most obvious wave band combination capable of effectively separating land and sea through spectral analysis, and respectively calculates the DN value differences of blue light, near infrared and green light;
the second step is that: calculating a mask matrix M: synthesizing two difference values to obtain a mask matrix M of a sea-land mask, wherein 1 represents sea and 0 represents land;
step two: obtaining the gray value over the sea: performing convolution operation on the mask matrix M and the image after four-channel fusion to obtain a gray value above the ocean, indicating that ocean information in a blue light channel of the GF-1 optical satellite image is stored most completely, and finally selecting the blue light channel to perform sea wave inversion;
step three: segmentation of image window: carrying out image window segmentation, and setting the size of a sub-window to be 1 km;
step four: image feature enhancement and identification: gradient vector information of the ocean surface reflectivity is obtained through sober operator calculation, and image feature strengthening and identification of wave crests and wave troughs of the sea waves are achieved;
G＝(Sx+iSy)*I (3)
in the formula: sxRepresenting the image subjected to the detection of the transverse edges, SyAn image representing longitudinal edge detection, G representing a gradient vector of sea surface reflectivity; i denotes a coefficient of an image for longitudinal edge detection, i 1.. n, n being the number of image elements; i represents an original image matrix;
step five: acquisition of energy spectrum: performing two-dimensional Fourier transform on the sub-window gradient vector information to obtain an energy spectrum;
step six: obtaining the wave propagation direction: obtaining the propagation direction of sea waves by carrying out statistical analysis on the energy spectrum;
step seven: obtaining a radial wave number spectrum: obtaining a radial wave number spectrum by performing radial one-dimensional Fourier change on the sub-window;
step eight: acquisition of dominant wavelength λ: obtaining a dominant wave wavelength lambda by counting wave numbers corresponding to energy peaks of the radial wave number spectrum;
step nine: calculation of dominant wave frequency f and period T: calculating a dominant wave frequency f and a period T by using a dispersion relation of wave fluctuation;
in the formula: f represents the wave frequency, T represents the period, lambda represents the wavelength, and g represents the acceleration of gravity;
step ten: obtaining wave information: and sliding the window to obtain the wave propagation direction, wavelength and period of other sub-windows, thereby obtaining wave information in any region in the satellite observation range.
In the second step, the GF-1 optical satellite image adopts an optimal wave band combination technology for inverting the sea wave.
And in the second step, preprocessing the GF-1 optical satellite image to provide wave crest and wave trough information of the ocean surface.
As shown in fig. 3, in the fifth step, two-dimensional fourier transform is performed on the wave information after GF-1 optical satellite image preprocessing, and 180-degree blur is eliminated by combining with the actual wind direction, so that a wave direction is obtained, which is consistent with the wave buoy result.
As shown in fig. 4, in the seventh step, the sea power density spectrum calculated in the x, y and radial directions by the one-dimensional fourier transform is used to determine the radial wavelength from the peak value, and the wave period is obtained through the dispersion relation and is consistent with the wave buoy and the GNSS buoy.
In the step ten, the sea wave information is acquired by using a satellite remote sensing image and adopting a two-dimensional Fourier and a one-dimensional radial Fourier to carry out combined inversion on the sea wave direction and wavelength technology.
The invention considers the sea wave information extraction of the space characteristics; extracting wave information under a low sea condition; the wave observation cost is reduced, the conventional GF-1 optical image data has low acquisition cost and can be freely used for public use such as scientific research, and the conventional sensors such as a buoy have high price and are difficult to acquire data; the space coverage range is improved, the high-resolution satellite can monitor most of the sea areas of the world, including open sea areas, the traditional methods such as buoy measurement and the like nearly provide measurement of limited points, and the space distribution is very limited; the observation resolution can be improved, the GF-1 optical image resolution is 2m, and the coverage range of each scene can reach 50 kilometers.
The invention can be widely applied to the occasion of extracting the ocean wave parameters.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A wave parameter inversion method by using a high-resolution satellite optical image is characterized by comprising the following steps:
the method comprises the following steps: preprocessing an original optical image: the method comprises the following steps:
the first step is as follows: calculating DN value difference: because different earth surfaces have gray DN value differences in four channels of red, green, blue and near infrared, the method is used for sea-land separation, finds out the most obvious wave band combination capable of effectively separating land and sea through spectral analysis, and respectively calculates the DN value differences of blue light, near infrared and green light;
the second step is that: calculating a mask matrix M: synthesizing two difference values to obtain a mask matrix M of a sea-land mask, wherein 1 represents sea and 0 represents land;
step two: obtaining the gray value over the sea: performing convolution operation on the mask matrix M and the image after four-channel fusion to obtain a gray value above the ocean, indicating that ocean information in a blue light channel of the GF-1 optical satellite image is stored most completely, and finally selecting the blue light channel to perform sea wave inversion;
step three: segmentation of image window: carrying out image window segmentation, and setting the size of a sub-window to be 1 km;
step four: image feature enhancement and identification: gradient vector information of the ocean surface reflectivity is obtained through sober operator calculation, and image feature strengthening and identification of wave crests and wave troughs of the sea waves are achieved;
G＝(Sx+iSy)*I (3)
in the formula: sxRepresenting the image subjected to the detection of the transverse edges, SyAn image representing longitudinal edge detection, G representing a gradient vector of sea surface reflectivity; i denotes a coefficient of an image for longitudinal edge detection, i 1.. n, n being the number of image elements; i represents an original image matrix;
step five: acquisition of energy spectrum: performing two-dimensional Fourier transform on the sub-window gradient vector information to obtain an energy spectrum;
step six: obtaining the wave propagation direction: obtaining the propagation direction of sea waves by carrying out statistical analysis on the energy spectrum;
step seven: obtaining a radial wave number spectrum: obtaining a radial wave number spectrum by performing radial one-dimensional Fourier change on the sub-window;
step eight: acquisition of dominant wavelength λ: obtaining a dominant wave wavelength lambda by counting wave numbers corresponding to energy peaks of the radial wave number spectrum;
step nine: calculation of dominant wave frequency f and period T: calculating a dominant wave frequency f and a period T by using a dispersion relation of wave fluctuation;
in the formula: f represents the wave frequency, T represents the period, lambda represents the wavelength, and g represents the acceleration of gravity;
step ten: obtaining wave information: and sliding the window to obtain the wave propagation direction, wavelength and period of other sub-windows, thereby obtaining wave information in any region in the satellite observation range.
2. The wave parameter inversion method using high resolution satellite optical images according to claim 1, wherein in the second step, the GF-1 optical satellite images adopt an optimal band combination technique for inverting sea waves.
3. The wave parameter inversion method using high resolution satellite optical images according to claim 1, wherein in the second step, GF-1 optical satellite images are preprocessed to provide wave crest and wave trough information of the ocean surface.
4. The wave parameter inversion method using the high-resolution satellite optical image according to claim 1, wherein in the fifth step, the two-dimensional fourier transform is performed on the wave information after the GF-1 optical satellite image preprocessing, and 180 ° ambiguity is eliminated by combining with an actual wind direction to obtain a wave direction, which is consistent with a wave buoy result.
5. The wave parameter inversion method using high resolution satellite optical images according to claim 1, wherein in the seventh step, the sea power density spectrum calculated in x, y and radial directions by one-dimensional fourier transform determines the radial wavelength from the peak value, and the wave period is obtained by dispersion relation and is consistent with the wave buoy and the GNSS buoy.
6. The wave parameter inversion method using the high-resolution satellite optical image as claimed in claim 1, wherein in the step ten, the sea wave information is acquired by using a satellite remote sensing image and adopting a two-dimensional fourier and a one-dimensional radial fourier to perform a combined inversion sea wave direction and wavelength technology.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010612160.7A CN111781146B (en) | 2020-06-30 | 2020-06-30 | Wave parameter inversion method using high-resolution satellite optical image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010612160.7A CN111781146B (en) | 2020-06-30 | 2020-06-30 | Wave parameter inversion method using high-resolution satellite optical image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111781146A true CN111781146A (en) | 2020-10-16 |
| CN111781146B CN111781146B (en) | 2023-02-24 |
Family
ID=72760391
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010612160.7A Active CN111781146B (en) | 2020-06-30 | 2020-06-30 | Wave parameter inversion method using high-resolution satellite optical image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111781146B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112883562A (en) * | 2021-02-01 | 2021-06-01 | 上海交通大学三亚崖州湾深海科技研究院 | Method for repairing ocean platform actual measurement stress spectrum based on neural network algorithm |
| CN113514833A (en) * | 2021-04-25 | 2021-10-19 | 南京信息工程大学 | Sea surface arbitrary point wave direction inversion method based on sea wave image |
| CN114594503A (en) * | 2022-03-02 | 2022-06-07 | 中南大学 | Shallow sea terrain inversion method, computer equipment and storage medium |
| CN114740550A (en) * | 2022-06-14 | 2022-07-12 | 广东海洋大学 | Intelligent recognition early warning method and system for continuous storm events |
| CN115659131A (en) * | 2022-09-27 | 2023-01-31 | 中国海洋大学 | Maritime work structure near-field wave propagation direction identification method |
| CN116258787A (en) * | 2023-05-15 | 2023-06-13 | 自然资源部第一海洋研究所 | Wave direction spectrum algorithm suitable for wave image |
| CN117058532A (en) * | 2023-10-08 | 2023-11-14 | 自然资源部第一海洋研究所 | Method and system for inverting water depth and wave height based on sea wave and solar flare signals |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013066447A1 (en) * | 2011-08-01 | 2013-05-10 | The Trustees Of Columbia University In The City Of New York | Lens-free planar imager and wireless transmitter |
| CN103383455A (en) * | 2013-07-03 | 2013-11-06 | 南京信息工程大学 | Ocean wave parameter extraction method based on shape-from-shading (SFS) technology |
| CN103605887A (en) * | 2013-11-13 | 2014-02-26 | 北京航空航天大学 | Simulation model for space-time distribution of on-orbit satellite imagery wave water surface solar flares |
| US20170131418A1 (en) * | 2015-11-05 | 2017-05-11 | Cgg Services Sa | Device and method for full waveform inversion |
| CN109116320A (en) * | 2018-07-13 | 2019-01-01 | 中国人民解放军国防科技大学 | Sea wave characteristic parameter extraction method based on radar echo signals |
| CN111257886A (en) * | 2020-03-23 | 2020-06-09 | 南京信息工程大学 | Method for inverting sea wave parameters by using single shipborne X-band radar image |
-
2020
- 2020-06-30 CN CN202010612160.7A patent/CN111781146B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013066447A1 (en) * | 2011-08-01 | 2013-05-10 | The Trustees Of Columbia University In The City Of New York | Lens-free planar imager and wireless transmitter |
| CN103383455A (en) * | 2013-07-03 | 2013-11-06 | 南京信息工程大学 | Ocean wave parameter extraction method based on shape-from-shading (SFS) technology |
| CN103605887A (en) * | 2013-11-13 | 2014-02-26 | 北京航空航天大学 | Simulation model for space-time distribution of on-orbit satellite imagery wave water surface solar flares |
| US20170131418A1 (en) * | 2015-11-05 | 2017-05-11 | Cgg Services Sa | Device and method for full waveform inversion |
| CN109116320A (en) * | 2018-07-13 | 2019-01-01 | 中国人民解放军国防科技大学 | Sea wave characteristic parameter extraction method based on radar echo signals |
| CN111257886A (en) * | 2020-03-23 | 2020-06-09 | 南京信息工程大学 | Method for inverting sea wave parameters by using single shipborne X-band radar image |
Non-Patent Citations (11)
| Title |
|---|
| LI X: "Observation of hurricane-generated ocean swell refraction at he Gulf Stream north wall with the RADARSAT-1 synthetic aperture radar", 《IEEE TRANSACTIONS ON GEOSCIENCE & REMOTE SENSING 》 * |
| XIE L L: "Cruise observation of Rossby waves with finite wavelength from the Pacific to the South China Sea", 《JOURNAL OF PHYSICAL OCEANOGRAPHY》 * |
| 余晓磊等: "利用环境一号卫星热红外影像反演渤海海表温度", 《海洋技术》 * |
| 吴忠强等: "基于多源影像融合去云的水深遥感反演研究――以哨兵-2A和资源三号为例", 《测绘与空间地理信息》 * |
| 张毅等: "基于外部风向初始信息的SAR影像海面风场反演研究", 《遥感技术与应用》 * |
| 李俊杰等: ""高分七号"卫星遥感影像自动云检测", 《航天返回与遥感》 * |
| 杨劲松等: "高分三号卫星对海浪的首次定量遥感", 《海洋与湖沼》 * |
| 梁冠辉: "GNSS海洋浮标海面高程动态不确定度研究", 《海洋科学进展》 * |
| 滕惠忠等: "遥感水深反演海图修测应用研究", 《海洋测绘》 * |
| 王婧: "高分三号（GF-3）卫星波模式SAR海浪信息反演研究", 《优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 谭可易等: "基于EMD方法的SAR海浪参数反演", 《广东海洋大学学报》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112883562A (en) * | 2021-02-01 | 2021-06-01 | 上海交通大学三亚崖州湾深海科技研究院 | Method for repairing ocean platform actual measurement stress spectrum based on neural network algorithm |
| CN112883562B (en) * | 2021-02-01 | 2023-02-24 | 上海交通大学三亚崖州湾深海科技研究院 | Ocean platform actual measurement stress spectrum repairing method based on neural network algorithm |
| CN113514833A (en) * | 2021-04-25 | 2021-10-19 | 南京信息工程大学 | Sea surface arbitrary point wave direction inversion method based on sea wave image |
| CN113514833B (en) * | 2021-04-25 | 2023-10-27 | 南京信息工程大学 | Sea surface arbitrary point wave direction inversion method based on sea wave image |
| CN114594503A (en) * | 2022-03-02 | 2022-06-07 | 中南大学 | Shallow sea terrain inversion method, computer equipment and storage medium |
| CN114740550A (en) * | 2022-06-14 | 2022-07-12 | 广东海洋大学 | Intelligent recognition early warning method and system for continuous storm events |
| CN115659131A (en) * | 2022-09-27 | 2023-01-31 | 中国海洋大学 | Maritime work structure near-field wave propagation direction identification method |
| CN116258787A (en) * | 2023-05-15 | 2023-06-13 | 自然资源部第一海洋研究所 | Wave direction spectrum algorithm suitable for wave image |
| CN116258787B (en) * | 2023-05-15 | 2023-10-17 | 自然资源部第一海洋研究所 | Wave direction spectrum algorithm suitable for wave image |
| CN117058532A (en) * | 2023-10-08 | 2023-11-14 | 自然资源部第一海洋研究所 | Method and system for inverting water depth and wave height based on sea wave and solar flare signals |
| CN117058532B (en) * | 2023-10-08 | 2023-12-19 | 自然资源部第一海洋研究所 | Method and system for inverting water depth and wave height based on sea wave and solar flare signals |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111781146B (en) | 2023-02-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111781146B (en) | Wave parameter inversion method using high-resolution satellite optical image | |
| Wang et al. | Dynamic detection of water surface area of Ebinur Lake using multi-source satellite data (Landsat and Sentinel-1A) and its responses to changing environment | |
| Wang et al. | Automatic extraction of Sargassum features from Sentinel-2 MSI images | |
| Zhang et al. | Observation of sea surface roughness at a pixel scale using multi-angle sun glitter images acquired by the ASTER sensor | |
| CN114201732A (en) | Sentinel-2A image-based shallow sea water depth inversion method | |
| Chen et al. | Rain-contaminated region segmentation of X-band marine radar images with an ensemble of SegNets | |
| Wang et al. | Sea ice classification with convolutional neural networks using Sentinel-L scansar images | |
| CN108983235B (en) | Cross polarization SAR wind field inversion method based on swath | |
| CN114758218A (en) | High-turbidity underwater topography inversion method suitable for hyperspectral satellite images | |
| Liu et al. | Computing ocean surface currents from GOCI ocean color satellite imagery | |
| Yu et al. | Automatic extraction of green tide using dual polarization Chinese GF-3 SAR images | |
| Hawkins et al. | GEOSAT altimeter sea-ice mapping | |
| Kuo et al. | Directional spectrum analysis and statistics obtained from ERS-1 SAR wave images | |
| CN114167419A (en) | Method for extracting river width by combining satellite remote sensing image and river flow meter data | |
| CN114325697A (en) | Wide-width cross-polarization SAR image thermal noise extraction method considering incidence angle | |
| Chen et al. | An adaptive GaoFen-3 SAR wind field retrieval algorithm based on information entropy | |
| Wang et al. | Comparison of Doppler-Derived Sea Ice Radial Surface Velocity Measurement Methods From Sentinel-1A IW Data | |
| Wang et al. | Seafloor classification based on deep-sea multibeam data—Application to the southwest Indian Ridge at 50.47° E | |
| Zhang et al. | Reconstruction of large complex sand-wave bathymetry with adaptive partitioning combining satellite imagery and sparse multi-beam data | |
| CN112766132A (en) | Dry salt beach extraction method based on sentinel No. 1 data | |
| CN115983141B (en) | Deep learning-based method, medium and system for inverting wave height of ocean waves | |
| Zheng et al. | Water body change detection based on Sentinel-1 and HJ-1A/B satellites data | |
| Tamura et al. | Extraction of ocean wave parameters from video images | |
| Cui et al. | Wave Height Estimation and Validation Based on the UFS Mode Data of Gaofen-3 in South China Sea | |
| CN106970373A (en) | The high extracting method of rips wave based on buoyance continuous imaging system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |