A sea surface roughness inversion method based on image edge point density

Abstract

The application discloses a sea surface roughness inversion method based on image edge point density, comprising the following steps: collecting sea surface images under specified shooting conditions and recording real-time wind speed information; when collecting images, keeping the average gray scale of the collected images within a hypothetical interval [m, n]; determining image edge points according to the threshold value th of the gradient modulus value, dividing the sum of the gradient modulus values of all edge pixel points by the total number of image pixel points, and finally taking the result as the image edge point density; solving the sea surface roughness z0 under the corresponding wind speed of all shooting moments according to the wind speed at the height of z meters; using data fitting to process the image edge point density and the sea surface roughness z0 data, and constructing the mapping relationship between the image edge point density and the sea surface roughness z0 under the shooting conditions; calculating the image edge point density of the image to be measured; and obtaining the sea surface roughness inversion value z0' according to the constructed mapping relationship between the image edge point density and the sea surface roughness.

Description

Technical Field

[0001] This invention relates to the field of digital image processing, and specifically to a method for inverting sea surface roughness based on image edge point density. Background Technology

[0002] In aerodynamics, sea surface roughness is defined as the height of the point where the wind profile is zero relative to sea level. It is a physical quantity that characterizes the weakening effect of sea surface roughness on airflow. It is of great significance in marine engineering applications, such as port design and berthing stability conditions. Accurate estimation of sea surface roughness is crucial for retrieving wave wavelengths and periods.

[0003] Currently, visual observation technology has demonstrated significant advantages in sea surface roughness estimation, and a series of studies have been conducted both domestically and internationally in the field of visual observation technology for sea surface roughness, mainly focusing on inversion methods based on image features. Image features are easily identifiable parts of an image that differ from other areas, and they contain rich information. Among them, regions in an image where color, brightness, or texture changes significantly are called edges. These image features usually correspond to the outline of an object, and the image composed of edges is also called an edge image, with the pixels that make up these edges called edge pixels. Sea surface roughness inversion methods based on image features utilize image features to reflect parameters such as wave wavelength and wave height, and obtain the corresponding sea surface roughness by constructing a mapping model. However, two problems still exist: first, image feature calculation is complex; second, traditional algorithms often ignore the intensity of edge image changes when calculating image features, focusing more on the frequency of edge images, making it difficult to accurately describe the state of ocean waves. Summary of the Invention

[0004] To address the problems encountered when using image features for sea surface roughness inversion, this invention provides a sea surface roughness inversion method based on image edge point density. This method innovatively combines the number of edge points in a wave image with the gradient magnitude of the edge image, achieving a quantitative description of wave features. This makes the mapping relationship of the sea surface roughness estimation model clearer, further improving the model's interpretability and computational efficiency.

[0005] The technical solution adopted by this invention to solve its technical problem is:

[0006] A method for inverting sea surface roughness based on image edge point density includes the following steps:

[0007] Step 1): At a height of b meters above sea level, acquire images using a camera with focal length f and resolution i×j, where the angle between the camera's optical axis and the sea level is a°. Simultaneously, record the real-time wind speed at a height of z meters.

[0008] Step 2) Calculate the average gray level i of the input sea surface grayscale image, keeping the average gray level i within the assumed interval [m,n].

[0009] Step 3) Calculate the gradient magnitude of each pixel in the image. Pixels with gradient magnitudes greater than a threshold th are defined as edge pixels. Calculate the sum of the gradient magnitudes of all edge pixels and divide it by the total number of pixels in the image. The final result is used as the image edge density. Used to reflect the state of the ocean waves corresponding to the image.

[0010] Step 4), based on the wind speed at a height of z meters, calculate the sea surface roughness z0 at the corresponding wind speed for all shooting times. This ensures that the roughness of each captured image is... All of these correspond to the sea surface roughness at the time of the shooting.

[0011] Step 5), use data fitting to process the image edge point density. Using the sea surface roughness z0 data, construct the image edge point density under this shooting condition. The mapping relationship between the surface roughness z0 and the sea surface roughness z0.

[0012] Step 6): Input a grayscale image of ocean waves that meets the acquisition requirements. Repeat steps 2 to 3 to calculate the image edge point density of the input image. Based on the constructed mapping relationship between image edge point density and sea surface roughness, obtain the sea surface roughness inversion value z0'. Finally, the sea surface roughness information is retrieved using sea surface images of a specified area.

[0013] Advantages or beneficial effects of the present invention:

[0014] (1) This invention solves the problem that current traditional visual observation techniques for sea surface roughness cannot fully reflect the state of ocean waves because they ignore the intensity of gradients at edge pixels, by introducing the concept of image edge point density. This method enhances the correlation between the input and output of the mapping model and improves the accuracy of the algorithm.

[0015] (2) The image feature calculation method designed in this invention is simple and has low algorithm complexity, which is conducive to loading into commonly used embedded platforms, so that it has the characteristics of low cost, high performance and high robustness in subsequent engineering applications. Attached Figure Description

[0016] Figure 1 This is a flowchart of the sea surface roughness inversion method for image edge point density in the embodiment.

[0017] Figure 2 This is a schematic diagram of image acquisition.

[0018] Figure 3 These are actual images of the sea surface.

[0019] Figure 4 Image edge point density and corresponding sea surface roughness (×10) -3 A diagram showing the relationship between meters (meters). Detailed Implementation

[0020] The present invention will now be described in detail with reference to the accompanying drawings and embodiments, examples of which are shown in the drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0021] Example:

[0022] The sea surface roughness inversion method based on image edge point density proposed in this invention will be described below with reference to the accompanying drawings.

[0023] like Figure 1 As shown, a method for inverting sea surface roughness based on image edge point density includes the following steps:

[0024] Step 1), such as Figure 2 As shown, the image acquisition device was set up at a height of 2.45 meters above sea level, using a camera with a focal length of 35 mm and a resolution of 2448×2048 to acquire images. The angle between the camera's optical axis and the sea level was 80°. Simultaneously, a anemometer recorded the real-time wind speed at a height of approximately 2.45 meters above sea level.

[0025] Step 2), input the collected grayscale image of the sea surface, as follows: Figure 3 As shown, the average gray level i of the image is calculated, keeping the average gray level i within the assumed interval [90, 110].

[0026] Step 3) Calculate the lateral gradient g of each pixel. x Longitudinal gradient g y and gradient magnitude Pixels with a gradient magnitude greater than 10 are defined as edge pixels. The sum of the gradient magnitudes of all edge pixels is calculated and divided by the total number of pixels in the image. The final result is the image edge pixel density reflecting the corresponding wave state in the wave image.

[0027] Step 4) Calculate the average wind speed corresponding to the 5-second time period of the acquired image based on the real-time wind speed. Based on the conversion relationship of wind speed at different sea altitudes and wind speed data By considering the range of values, the conversion relationship between the average wind speed at a height of 2.45 meters and the average wind speed at a height of 10 meters is obtained. The conversion formula between the average sea wind speed at 2.45 meters and 10 meters is as follows:

[0028]

[0029] in, Let be the average wind speed at a height of 10 meters. Combining this with the empirical formula for the average wind speed at 10 meters and sea surface roughness, we can derive the mapping formula for the average wind speed at a height of 2.45 meters and sea surface roughness. Therefore, based on wind speed data... Obtain the sea surface roughness z0 of the corresponding image in the image set, and then compare the sea surface roughness result with the image edge point density. One-to-one correspondence. The formula for mapping wind speed at a height of 2.45 meters to sea surface roughness is as follows:

[0030]

[0031] Step 5): Using a polynomial fitting method, based on the sea surface roughness z0 and the image edge point density... A scatter plot is used to construct a mapping model between the two, as shown in the following diagram. Figure 4 The mapping relationship corresponding to the image capture conditions in this embodiment is as follows:

[0032] z0'=0.003614g 3 +0.000901g 2 -0.08585g +0.1718

[0033] in

[0034] Step 6): Input a grayscale image of ocean waves that meets the acquisition requirements from an on-site test. Repeat steps 2 to 3 to obtain the corresponding image edge point density. Based on the constructed mapping relationship model between image edge point density and sea surface roughness, calculate the sea surface roughness inversion value z0'. This ultimately achieves the inversion of sea surface roughness information using sea surface images of a specified area.

[0035] Compared with common methods for inverting sea surface roughness images, the method of this invention incorporates gradient changes in the wave image as image features, such as... Figure 4 As shown, incorporating gradient intensity information from wave images strengthens the relationship between image features and sea surface roughness, reducing errors in the mapping model between image features and sea surface roughness. This method also avoids the problem of excessively long image feature computation times, improving the speed and robustness of sea surface roughness image inversion methods.

[0036] The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Therefore, any changes made in accordance with the shape and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for inverting sea surface roughness based on image edge point density, characterized in that, Includes the following steps: Step 1) At a distance of b meters from the sea level, use a camera with focal length f and resolution i×j to acquire images, where the angle between the camera's optical axis and the sea level is a°. At the same time, use a anemometer to record the real-time wind speed at a height of z meters. Step 2), calculate the average gray level i of the input image, keeping the average gray level i within the assumed interval [m,n]; Step 3): Calculate the gradient magnitude of each pixel in the image. Calculate the sum of the gradient magnitudes of all edge pixels whose gradient magnitude is greater than the threshold th, and divide this sum by the total number of pixels in the image. The final result is used as the image edge point density. Used to reflect the state of the waves corresponding to the wave image; Step 4): Based on the wind speed at a height of z meters, calculate the sea surface roughness z0 at the average wind speed corresponding to all shooting times, so that the roughness of each captured image is... All of these correspond to the sea surface roughness at the time of the photograph; Step 5), use data fitting to process the image edge point density. Using the sea surface roughness z0 data, construct the image edge point density under this shooting condition. Mapping relationship with sea surface roughness z0; Step 6) Input a grayscale image of ocean waves that meets the acquisition requirements. Perform steps 2 to 3 on the input image to calculate the image edge point density. Based on the constructed mapping relationship between the image edge point density and the sea surface roughness, obtain the sea surface roughness inversion value z0'. Finally, realize the inversion of sea surface roughness information using the sea surface image of the specified area.

2. The sea surface roughness inversion method based on image edge point density according to claim 1, characterized in that... In step 3), the definition of image edge point density is: the gradient magnitude of edge pixels is regarded as the edge quality of that point, and the edge quality of non-edge pixels is 0. The image edge point density is the sum of the edge quality of the entire image divided by the total number of pixels.

3. The sea surface roughness inversion method based on image edge point density according to claim 1, characterized in that... In step 3), the gradient magnitude of a pixel in the image is the horizontal gradient g of that pixel. x and longitudinal gradient g y The arithmetic square root of the sum of squares; where the transverse gradient g at that point is... x and longitudinal gradient g y The calculation formula is as follows: g x =[I(x+1,y)-I(x,y)+I(x,y)-I(x-1,y)] / 2 (4) g y =[I(x,y+1)-I(x,y)+I(x,y)-I(x,y-1)] / 2 (5) Where x is the column number of the point, y is the row number of the point, and I(x,y) represents the gray value of the pixel.

4. The sea surface roughness inversion method based on image edge point density according to claim 1, characterized in that... In step 5), the sea surface roughness z0 is compared with the image edge point density. By linking the data together and constructing a mapping relationship, the sea surface roughness information can ultimately be retrieved using sea surface images of a specified area.

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