An aerial survey-based oil spill volume estimation method

Abstract

The application discloses a kind of overflow oil quantity estimation method based on aerial survey, it is related to oil spill monitoring technical field, the method is first to the sampling shooting of oil spill area, according to the color setting corresponding pixel value range of different categories oil film in color map, then overall shooting is carried out, based on pixel value range, the thickness of oil film is divided into region, get primary color oil film, emulsion oil film and thin oil film, according to the thickness of thin oil film determined by pixel value size, the thickness of emulsion oil film and primary color oil film is measured using radar, the overlapping part of primary color oil film and emulsion oil film is extracted, further adjustment is made by setting matching weight mode, finally the total overflow oil quantity is obtained by superimposing the overflow oil quantity corresponding to all pixel points in image;The technical scheme of the application detects the thickness of different categories oil film using multiple measurement methods, improves the accuracy of overall overflow oil quantity estimation, reduces the complexity of later calculation, and locally adjusts the junction area of different types of oil film, improves the calculation accuracy.

Description

Technical Field

[0001] This application relates to the technical field of oil spill monitoring, and more specifically, to a method for estimating oil spill volume based on aerial surveys. Background Technology

[0002] In the fields of environmental monitoring and marine pollution control, oil slick detection technology is of great significance for emergency response and pollution assessment of oil spills. Existing oil slick detection methods mainly rely on optical and spectroscopic techniques, such as multispectral, hyperspectral imaging, and fluorescence imaging. However, while these methods can accurately obtain the spectral characteristics and thickness information of oil slicks under laboratory conditions, they have revealed multiple shortcomings in real-world marine applications, especially in the case of large-scale heavy oil collection, where numerous technical problems exist.

[0003] Traditional methods for heavy oil detection over large sea areas often face challenges due to complex sea surface conditions (such as waves, reflections, and wind / wave interference) and the dynamic and non-uniform distribution of oil films. A single sampling method is often insufficient to detect different types of oil films. For example, for the original color of heavy oil films, the thickness variation has a relatively small impact on the intensity of reflected light, making the relationship between the spectral reflectance signal and the oil film thickness more complex. This limits the accuracy of spectrometers when processing such oil films. For emulsified oil films, due to the scattering effect, there is no direct linear relationship between the intensity of reflected light and the actual thickness of the oil film. Especially when there are numerous small oil droplets and water on the surface of emulsified oil films, the spectral reflectance signal may become blurred due to light scattering, making it difficult to accurately reflect the oil film thickness. Furthermore, the high-precision spectroscopic equipment used in traditional methods is expensive, generates a large amount of data, and the subsequent analysis process is complex, affecting the efficiency of real-time monitoring.

[0004] In oil spills, in addition to heavy oil, emulsified oil can form due to oil-water mixing. This type of oil film differs significantly from heavy oil in visual and spectral characteristics. However, due to its thinness and the significant influence of ambient light and seawater background on its color changes, traditional detection methods often struggle to effectively capture its characteristics, and it is sometimes even ignored entirely. This selective detection not only leads to incomplete estimations of the total oil volume in the spill area but may also underestimate the actual environmental impact of the accident. Furthermore, there are clear boundaries or transition zones between different types of oil films (such as original color oil films, emulsified oil films, and thin oil films) within the spill area. These zones exhibit continuous changes in physical properties due to oil film mixing and gradual thickness variations. Traditional methods typically do not introduce appropriate correction coefficients or directly ignore these transition zones when dealing with these boundary areas, resulting in significant errors in thickness and area calculations. Accumulated errors in large-scale oil film estimations can significantly affect the accuracy of the final oil spill volume. Summary of the Invention

[0005] The purpose of this application is to provide an oil spill estimation method based on aerial survey to address the shortcomings of existing technologies, thereby solving the problems of high equipment investment costs, complex post-processing of data, difficulty in adapting a single sampling method to different types of oil films, insufficient detection of emulsified oil and thin oil, and inadequate treatment of oil film boundary areas in current large-scale heavy oil collection and oil film detection.

[0006] The technical solution of this application is: to provide a method for estimating oil spill volume based on aerial surveying, the method comprising:

[0007] Step 1: Use an airborne camera to sample and photograph the target oil spill area to obtain a pre-collected image. Manually calibrate the pre-collected image to classify the oil film in the pre-collected image into original color oil film, emulsified oil film and thin oil film.

[0008] Step 2: Based on the color characteristics of different types of oil films in the pre-collected image, set the pixel value range corresponding to each type of oil film. If the pixel value ranges of each oil film overlap, then process as follows: If the pixel value range of the thin oil film overlaps with the pixel value range of the original color or emulsified oil film, then take the middle value of the overlapping part as the boundary pixel value and redivide the pixel value range of the thin oil film and other oil films. If the pixel value range of the original color oil film overlaps with the pixel value range of the emulsified oil film, then take the overlapping part as the mixed pixel value range.

[0009] Step 3: Use an airborne camera to capture a color image of the target oil spill area from the vertical direction. Correct the brightness of the color image based on the average brightness of the pre-acquired image. Divide the oil film in the corrected color image into regions based on the pixel value range and mixed pixel value range of each oil film. At the same time, use an airborne ultrasonic radar to measure the same region and obtain the oil film thickness in the same region.

[0010] Step 4: For thin oil film, sample and simulate the oil spill in the spill area, determine the relationship between different oil film thicknesses and image pixel values ​​under the same external light intensity, and determine the thickness of the thin oil film in the corresponding area based on the size of the pixel value of the thin oil film area in the color image.

[0011] Step 5: For the mixed pixel region, set corresponding matching weights for each individual pixel according to its color similarity with the original color oil film and the emulsified oil film, retrieve the initial oil film thickness of the mixed pixel region measured by the airborne ultrasonic radar, and update the oil film thickness corresponding to each pixel based on the matching weights.

[0012] Step 6: For different types of oil films, calculate the oil spill volume of each pixel based on the actual area and thickness of the oil film corresponding to a single pixel in the color image, and then superimpose the oil spill components of each pixel in all color images to obtain the total oil spill volume.

[0013] Furthermore, in step 2, the corresponding pixel value range is set according to the color characteristics of different types of oil films in the pre-collected image, specifically including:

[0014] Based on the oil film classification results, the original color oil film area, emulsified oil film area, and thin oil film area are marked in the pre-acquired image. All pixel values ​​corresponding to each oil film area are extracted. For any given area, the initial three-channel pixel value range is first set based on the extreme values ​​of the three-channel pixel values ​​in the area. Then, the three-channel pixel value ranges for each type of oil film are adjusted according to the overlap of the pixel value ranges. The initial three-channel pixel value range for the original color oil film is [P]. Min ,P Max The initial three-channel pixel value range of the emulsified oil film is [E]. Min E Max The initial three-channel pixel value range of the thin oil film is [T]. Min ,T Max ], where P represents the original color oil film, E represents the emulsified oil film, and T represents the thin oil film.

[0015] Furthermore, the pixel value range of the primary color oil film [P] Min ,P Max ] is represented as:

[0016]

[0017] In the formula, This represents the minimum intensity values ​​of the red, green, and blue channels in the primary color oil film region, obtained by averaging the minimum values ​​of the primary color oil film pixels in different pre-captured images. This represents the maximum intensity values ​​of the red, green, and blue channels in the primary color oil film region obtained by averaging the maximum values ​​of the primary color oil film pixels in different pre-captured images. N is the number of pre-captured images, and n is the index of the pre-captured image.

[0018] Furthermore, in step 2, the three-channel pixel value ranges of each type of oil film are adjusted according to the overlap of pixel value ranges, specifically including:

[0019] For any two oil films, when the pixel value ranges of the three channels all intersect, it is determined that the pixel value ranges of the three channels of the two oil films overlap.

[0020] The three-channel pixel value range of the thin oil film is compared with the three-channel pixel values ​​of the other two oil films. When the three-channel pixel value ranges of the two oil films being compared overlap, the channel with the smallest overlap range is selected for adjustment, while the pixel value ranges of the other two channels remain unchanged. If the smallest overlap range is not unique, a channel is randomly selected. For the selected channel, the middle value of the overlapping part is used as the boundary pixel value to divide the two pixel value ranges, thus distinguishing between the two categories.

[0021] The three-channel pixel value range of the primary color oil film is compared with the three-channel pixel value of the emulsion oil film. When the three-channel pixel value ranges of the two oil films overlap, the overlapping part is taken as the mixed pixel value range. The channel with the smallest overlapping range and no inclusion relationship between the pixel value ranges is selected as the determination channel to distinguish between the primary color oil film, the emulsion oil film and the mixed pixels. If the smallest overlapping range is not unique, a channel is randomly selected as the determination channel. For the selected channel, the pixels of the overlapping part are the mixed pixels.

[0022] Furthermore, step 4 specifically includes:

[0023] By using a multivariable nonlinear function to represent the relationship between pixel values ​​and thickness, the expression for the pixel values ​​and thickness of a thin oil film is obtained:

[0024] h(R,G,B)=a·exp(b R R+b G G+b B B)

[0025] In the formula, h(R,G,B) is the thickness of the thin oil film, a is a constant coefficient, and b is a constant coefficient. R b is the weighting coefficient for the red channel. G b is the weighting coefficient for the green channel. B This represents the weighting coefficient for the blue channel;

[0026] The experiment obtained data on the relationship between pixel values ​​and thickness of the thin oil film. This data was then substituted into the error function expression, and the optimal parameters {a,b} were obtained by minimizing this error function. R ,b G ,b B The error function is expressed as:

[0027]

[0028] In the formula, M is the number of samples of the change relationship data, m is the index of the sample, and h is the index of the sample. m R represents the actual oil film thickness corresponding to the m-th sample. m G represents the intensity value of the red channel corresponding to the m-th sample. m Let B be the intensity value corresponding to the green channel of the m-th sample. m This represents the intensity value of the blue channel corresponding to the m-th sample.

[0029] Furthermore, setting the corresponding matching weights in step 5 specifically includes:

[0030] For any region within the primary color oil film region and the emulsified oil film region, the average value of each channel is taken as the center RGB value of the oil film, and the center RGB value of the primary color oil film is... The center RGB value of the emulsified oil film is Calculate the first Euclidean distance between a single pixel in the mixed pixel region and different oil films, calculate the second Euclidean distance between the primary color oil film and the emulsified oil film, determine the color similarity between the pixel and different oil films based on the ratio of the first Euclidean distance to the second Euclidean distance, and normalize the similarity to obtain the matching weight between the pixel and different oil films.

[0031] The color similarity between pixels in the mixed pixel region and different oil films is:

[0032]

[0033] In the formula, L P L represents the color similarity between a pixel and the original color oil film. E The color similarity between a pixel and the emulsion oil film, where ∈ is a constant, and D P D is the first Euclidean distance between the pixel and the primary color oil film. E D is the first Euclidean distance between the pixel and the emulsion oil film. PE The second Euclidean distance; normalizing the similarity yields:

[0034]

[0035] In the formula, ω P ω represents the matching weight between pixels in the mixed pixel region and the primary color oil film. E The matching weights between pixels in the mixed pixel region and the emulsion oil film.

[0036] Furthermore, step 5 also includes: for mixed pixel regions, calculating the oil spillage amount of each pixel based on the oil film thickness and the matching weights corresponding to different oil films, specifically including:

[0037] By retrieving measurement data from the airborne ultrasonic radar, the initial oil film thickness of each pixel in the mixed pixel region is obtained. Based on the matching weights between the pixel and different oil films, the oil spill volume of the pixel is calculated.

[0038]

[0039] In the formula, O mix The oil spill component represents the mixed pixel region, where s is the actual area corresponding to the pixel, and h is the oil spill component. mix,i Let c be the initial oil film thickness of the i-th pixel in the mixed pixel region. E I represents the oil concentration of the emulsified oil film, and I represents the total number of pixels in the mixed pixel region.

[0040] Further, in step 6, for the primary color oil film, emulsified oil film, and thin oil film, the amount of oil spillage at each pixel is calculated based on the actual area and oil film thickness corresponding to a single pixel in the color image, specifically including:

[0041] By retrieving measurement data from the airborne ultrasonic radar, the oil film thickness of each pixel in the primary color oil film area and the emulsified oil film is obtained. Based on the actual area corresponding to each pixel and the oil overflow component of each pixel's oil film thickness, the oil overflow component of the primary color oil film area is:

[0042]

[0043] In the formula, K is the number of pixels in the primary color oil film region, and h P,k Let be the oil film thickness at the k-th pixel in the primary color oil film region, and let be the oil overflow component of the emulsified oil film:

[0044]

[0045] In the formula, V represents the number of pixels in the emulsified oil film region, and h E,d Let be the oil film thickness at the v-th pixel in the emulsified oil film region;

[0046] Retrieve the oil film thickness data for each pixel in the thin oil film area. Based on the actual area of ​​each pixel and the oil film thickness of each pixel, calculate the oil overflow component of the thin oil film:

[0047]

[0048] In the formula, Y represents the number of pixels in the thin oil film region, and h T,y Let y be the thickness of the oil film at the y-th pixel in the thin oil film region.

[0049] Further, step 3 includes brightness correction of the color image based on the average brightness of the pre-acquired image, specifically including: calculating a brightness normalization factor in advance based on the average brightness of the pre-acquired image. L s L represents the average brightness of the pre-acquired image. f The average brightness of a color image is corrected by multiplying the RGB value of each pixel in the color image by the scale. The corrected RGB values ​​are represented as R′=Rscale, G′=G·scale, B′=B·scale.

[0050] The beneficial effects of this application are:

[0051] First, the technical solution in this application classifies oil slicks on the sea surface based on characteristics such as color and thickness, and uses a combination of optical imaging and radar measurement to detect different types of oil slicks. This solves the problem that a single sampling method is difficult to adapt to the detection of different types of oil slicks, making the overall oil spill estimation more accurate. The technical solution in this application previously obtained a thickness calculation model for thin oil slicks through experiments. After acquiring color images, the thickness can be directly calculated from the RGB values ​​of the thin oil slick area in the image. Since the area covered by thin oil slicks on the sea surface is larger than that covered by other oil slicks, compared to the method of calculating the thickness of each pixel individually when using a spectrometer, the method in this application that directly calculates the thickness using this model simplifies the subsequent data processing flow, reduces the complexity of later analysis, and improves real-time performance. Compared with existing methods, the technical solution in this application is more suitable for large-scale heavy oil collection at sea.

[0052] Secondly, the technical solution in this application divides different oil films by setting a three-channel pixel value range. To address the problem of inaccurate oil film boundary distinction caused by inaccurate manual labeling during the division process, this application extracts the overlapping parts between various oil films for further division to avoid division errors. In addition, this application also sets a mixed pixel value range to extract pixels falling on the overlapping parts between the original color oil film and the emulsion oil film for further adjustment. The technical solution in this application fully considers the discontinuity and mixing effect of oil films, and compensates for the problems of blurred boundaries and indistinct transitions between the original color oil film and the emulsion, reducing the statistical error of the mixed state oil film and improving the accuracy of the total oil spill calculation results. Attached Figure Description

[0053] The advantages of the above and / or additional aspects of this application will become apparent and readily understood in the description of the embodiments in conjunction with the following drawings, wherein:

[0054] Figure 1 This is a schematic flowchart of an oil spill estimation method based on aerial survey according to an embodiment of this application. Detailed Implementation

[0055] To better understand the above-mentioned objectives, features, and advantages of this application, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of this application can be combined with each other.

[0056] In the following description, many specific details are set forth in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of this application is not limited to the specific embodiments disclosed below.

[0057] like Figure 1 As shown, this embodiment provides a method for estimating oil spill volume based on aerial surveying, including:

[0058] Step 1: Use an airborne camera to sample and photograph the target oil spill area to obtain a pre-collected image. Manually calibrate the pre-collected image to classify the oil film in the pre-collected image into original color oil film, emulsified oil film, and thin oil film.

[0059] It should be noted that leaked oil typically floats on the water surface, forming an oil film. Due to the action of water currents, this oil film can develop into films of varying thicknesses. Especially in marine environments, the surging of seawater accelerates emulsification, making the oil film morphology even more complex. Different types and thicknesses of oil films exhibit significant differences in physical properties, environmental impact, and monitoring methods; the same oil film detection method cannot be applied to all oil films. Since tanker accidents often involve heavy oil, this embodiment primarily focuses on heavy oils such as crude oil and HFO (Heavy Fuel Oil).

[0060] Specifically, oil films of different types or thicknesses exhibit different color characteristics. Thicker oil films tend to cluster together in blocky patterns, typically displaying darker colors such as dark brown, black, or the original color of crude oil. Thinner oil films are formed by the diffusion of thicker films, surrounding them and covering a larger area. Thinner oil films slightly alter the background water color, typically displaying lighter colors such as metallic or light brown. Emulsified oil films are emulsions of oil and water, typically displaying colors such as pale yellow, orange-red, or brown, and may also exhibit irregular ripples or a turbid visual effect. Emulsified oil films are usually distributed in areas where the oil film is thinner and at the edges, with the edges exhibiting a narrow band distribution. Therefore, oil film classification is necessary during monitoring. Detection based on the classification results can more accurately assess the environmental impact of oil films and provide a basis for subsequent oil spill response.

[0061] A camera and positioning system are installed on a carrier drone. Based on the location data fed back by the positioning system, the drone is controlled to reach multiple sampling and shooting points above the target oil spill area in sequence. At each sampling and shooting point, the camera is used to take pictures of the oil spill area to obtain a pre-collected image. The pre-collected image is then uploaded to the host computer.

[0062] Step 2: Based on the color characteristics of different types of oil films in the pre-collected image, set the pixel value range corresponding to each type of oil film. If the pixel value ranges of each oil film overlap, then process as follows: If the pixel value range of the thin oil film overlaps with the pixel value range of the original color or emulsified oil film, then take the middle value of the overlapping part as the boundary pixel value and redivide the pixel value range of the thin oil film and other oil films. If the pixel value range of the original color oil film overlaps with the pixel value range of the emulsified oil film, then take the overlapping part as the mixed pixel value range.

[0063] In the host computer, the oil film is manually divided into original color oil film, emulsified oil film, and thin oil film. Based on the classification results of the oil film, the original color oil film area, emulsified oil film area, and thin oil film area are marked in the pre-acquisition image respectively. All pixel values ​​of the original color oil film area, emulsified oil film area, and thin oil film area are extracted respectively. For any area, firstly, the initial three-channel pixel value range is set according to the extreme values ​​of the three-channel pixel values ​​in the area. Then, the three-channel pixel value range of each type of oil film is adjusted according to the overlap of the pixel value range.

[0064] Specifically, the initial three-channel pixel value range corresponding to the primary color oil film is [P Min ,P Max The initial three-channel pixel value range corresponding to the emulsified oil film is [E]. Min E Max The initial three-channel pixel value range corresponding to the thin oil film is [T]. Min ,T Max ], where P represents the original color oil film, E represents the emulsified oil film, and T represents the thin oil film.

[0065] The initial three-channel pixel value range corresponding to the primary color oil film [P] Min ,P Max ] is represented as:

[0066]

[0067] In the formula, R represents the red channel, G represents the green channel, and B represents the blue channel. This represents the minimum intensity values ​​of the red, green, and blue channels in the primary color oil film region, obtained by averaging the minimum values ​​of the primary color oil film pixels in different pre-captured images. This represents the maximum intensity values ​​of the red, green, and blue channels in the primary color oil film region, obtained by averaging the maximum values ​​of the primary color oil film pixels in different pre-captured images. N is the number of pre-captured images, and n is the index of the pre-captured image (i.e., n is the nth pre-captured image). The minimum intensity value of the red channel representing the primary color oil film region in the nth pre-captured image. The maximum intensity value of the red channel representing the primary color oil film region in the nth pre-captured image. The minimum intensity value of the green channel representing the primary color oil film region in the nth pre-captured image. The maximum intensity value of the green channel represents the primary color oil film region in the nth pre-captured image. The minimum intensity value of the blue channel representing the primary color oil film region in the nth pre-captured image. The maximum intensity value of the green channel represents the primary color oil film region in the nth pre-collected image.

[0068] The initial three-channel pixel value range corresponding to the emulsified oil film [E] Min E Max ] is represented as:

[0069]

[0070] In the formula, This represents the minimum intensity values ​​of the red, green, and blue channels in the emulsified oil film region, obtained by averaging the minimum values ​​of the pixels in the emulsified oil film in different pre-captured images. This represents the maximum intensity values ​​of the red, green, and blue channels in the emulsified oil film region obtained by averaging the maximum values ​​of the pixels in the emulsified oil film in different pre-acquired images. In this embodiment, the calculation process for the initial three-channel pixel value range of the emulsified oil film is the same as that of the original color oil film, and will not be repeated here.

[0071] The initial three-channel pixel value range corresponding to the thin oil film [T] Min ,T Max ] is represented as:

[0072]

[0073] In the formula, This represents the minimum intensity values ​​of the red, green, and blue channels in the thin oil film region, obtained by averaging the minimum values ​​of the pixels in the thin oil film in different pre-captured images. This represents the maximum intensity values ​​of the red, green, and blue channels in the thin oil film region obtained by averaging the maximum values ​​of the thin oil film pixels in different pre-collected images. In this embodiment, the calculation process of the initial three-channel pixel value range of the thin oil film is the same as that of the original color oil film, and will not be repeated here.

[0074] For any two oil films, when the pixel value ranges of all three channels intersect, the three-channel pixel value ranges of the two oil films are determined to overlap. The three-channel pixel value ranges of each type of oil film are adjusted based on the initial overlap of the three-channel pixel value ranges of the three oil films, specifically including:

[0075] The three-channel pixel value range of the thin oil film is compared with the three-channel pixel values ​​of the other two types of oil films (including the primary color oil film and the emulsified oil film). When the three-channel pixel value ranges of the two oil films being compared overlap, the channel with the smallest overlap range is selected for adjustment, while the pixel value ranges of the other two channels remain unchanged. If the smallest overlap range is not unique, a channel is randomly selected for adjustment. For the selected channel, the median value of the overlapping part is used as the boundary pixel value to divide the two pixel value ranges and distinguish between the two categories. If the two pixel value ranges are the same, other channels are selected for adjustment.

[0076] For example, and Overlap, the median value R of the overlapping part mid As the boundary pixel value, if Then As the pixel value range for a thin oil film, The range of pixel values ​​for the primary color oil film.

[0077] The three-channel pixel value range of the primary color oil film is compared with that of the emulsion oil film. When the three-channel pixel value ranges of the two oil films overlap, the overlapping part is taken as the mixed pixel value range. At the same time, the channel with the smallest overlapping range and no inclusion relationship between the pixel value ranges is selected as the determination channel to distinguish between the primary color oil film, the emulsion oil film, and the mixed pixels. If the smallest overlapping range is not unique, a channel is randomly selected as the determination channel. For the selected channel, the pixels in the overlapping part are the mixed pixels, the pixels in the primary color oil film pixel value range other than the overlapping part are the primary color oil film pixels, and the pixels in the emulsion oil film pixel value range other than the overlapping part are the emulsion oil film pixels.

[0078] In this embodiment, after obtaining the initial three-channel pixel value range corresponding to each oil film, the three-channel pixel value ranges of any two oil film regions are compared to avoid situations where the three-channel pixel value ranges completely overlap or are all inclusive (i.e., the pixel value range of each channel is inclusive, meaning that a smaller range is included in another larger range). If the three-channel pixel value ranges corresponding to the two oil films completely overlap or are all inclusive, the initial three-channel pixel value ranges need to be recalculated until there are three-channel pixel value ranges that do not completely overlap. This is to prevent the inclusion of all pixel value ranges from being removed when removing the overlapping part, which would cause the oil film to lose the pixel value range of one channel and cause a calculation error (this is mainly to avoid the situation where the pixel ranges of the two oil films cannot be distinguished).

[0079] In this embodiment, the type of oil in the oil spill is uncertain, and different types of oil have different color characteristics. Therefore, before formally acquiring color images of the oil spill area, it is necessary to pre-acquire multiple images for labeling different types of oil films, and these images are used as pre-acquired images. After the oil spill occurs, it is necessary to first obtain the color feature information of the oil film through the pre-acquired images. Based on the color feature information of the oil film, the colors corresponding to different types of oil films are obtained, and the pixel value range of the current situation is determined based on the colors of different types of oil films. This allows for the rapid division of the oil film regions in the image according to the preset pixel value range when processing the formally acquired color images, thus obtaining the location distribution characteristics of different types of oil films.

[0080] It should be noted that when labeling various oil films in pre-collected images based on human experience, some details may be overlooked, leading to inaccurate labeling. For example, the boundaries of different types of oil films may not be accurately distinguished, resulting in overlapping ranges of the three-channel pixel values ​​corresponding to different types of oil films. This is especially true for primary color oil films and emulsified oil films. In the target oil spill area, the edges of primary color oil films or areas with thinner oil layers have a larger contact area with seawater, making them more prone to slight emulsification. These areas usually exhibit a mixed state of primary color oil film and emulsified oil film, with a small area and inconspicuous color change. During manual labeling, they are often directly classified as primary color oil films. In actual image processing, if the initial three-channel pixel values ​​corresponding to each oil film are directly used for classification, not only will classification errors occur, but the impact of the mixed state of primary color oil film and emulsified oil film on the total oil spill volume will also be ignored, leading to inaccurate calculation results. Furthermore, because primary color oil films at sea are usually irregularly distributed with long edge lines, failure to adjust for this when calculating the oil spill volume will increase the calculation error. Therefore, this application extracts the overlapping portions between the thin oil films and further divides them to avoid division errors. This application also sets a mixed pixel value range to extract pixels that fall in the overlapping portion between the original color oil film and the emulsified oil film for further processing, so as to weaken the influence of the mixed state of the original color oil film and the emulsified oil film on the total oil spill volume and improve the accuracy of the calculation results.

[0081] Step 3: Use an airborne camera to capture a complete color image of the target oil spill area from a vertical direction. Correct the brightness of this color image based on the average brightness of the pre-captured image. The interval between pre-capture and final capture should not be too long to avoid significant changes in ambient light. Alternatively, they can be performed simultaneously, but the pre-captured image should be sent back to the host computer for processing first. Based on the pixel value range and mixed pixel value range corresponding to each oil film, the oil film in the corrected color image is divided into regions: original color oil film region, emulsified oil film region, thin oil film region, and mixed pixel region. Simultaneously, use an airborne ultrasonic radar to measure the same area on the sea surface (i.e., the target oil spill area captured by the airborne camera) to obtain oil film thickness data for the same area. The camera's height above the sea surface should be approximately the same for each capture.

[0082] The target oil spill area was divided into multiple equally sized shooting areas. A positioning system and camera were mounted on a carrier drone. Based on the position data fed back by the positioning system, the drone was controlled to sequentially reach the shooting positions above each shooting area according to a pre-set route. At each shooting position, the camera took pictures from a vertical direction to obtain color images from a vertical perspective. The color images collected by the camera in each shooting area constituted the overall color image of the target oil spill area, that is, the shooting range covered the entire oil spill area. Each shooting position was at the same height above the sea surface. The collected color images were uploaded to a host computer for brightness correction and oil film area division (that is, dividing the color image into primary color oil film, emulsified oil film, thin oil film and mixed pixel area according to the pixel value range and mixed pixel value range corresponding to each oil film).

[0083] While capturing color images, an airborne ultrasonic radar is used to scan and measure the same area to obtain oil film thickness data for the same area. The measured image data and ultrasonic radar data are then matched for positional registration, specifically as follows: the pixels in the color image are mapped one-to-one with the coordinate points in the positioning system, and the measurement points of the airborne ultrasonic radar are mapped one-to-one with the coordinate points in the positioning system, so that the pixels in the color image and the radar measurement points can be spatially aligned in a unified positioning coordinate system. Then, the color image is divided into grids, where each grid is a square with an equal area, such that each grid is smaller than a pre-set area and contains more than two radar measurement points. The average value of the measurement results of each radar measurement point is taken as the oil film thickness of the pixel corresponding to the primary color oil film, emulsified oil film, and mixed pixel in that grid.

[0084] In this embodiment, the airborne ultrasonic radar needs to perform large-area measurements, and an array-type ultrasonic radar, such as MIMO imaging radar, phased array radar, or SAR synthetic aperture radar, can be used. In this embodiment, both the camera and radar are mounted on the UAV and are rigidly connected with no relative motion. The positioning system provides the UAV's position and attitude in the ground coordinate system. Each pixel acquired by the camera can have its position in the ground coordinate system calculated using the camera's intrinsic and extrinsic parameters. Each measurement point acquired by the radar is itself a three-dimensional point with direction and distance information, which can be directly transformed to the ground coordinate system using the radar's extrinsic parameters.

[0085] To quickly complete brightness correction and oil film area segmentation, before capturing a complete color image of the target oil spill area, a brightness normalization factor is calculated based on the average brightness of the pre-acquired image. The brightness normalization factor is expressed as:

[0086]

[0087] In the formula, L s L represents the average brightness of the pre-acquired image.f The average brightness is the color image acquired in step 3. The formula for calculating the average brightness is as follows:

[0088]

[0089] In the formula, L includes L s and L f X represents the number of pixels in a single image; the RGB value of each pixel in the color image is multiplied by the scale for brightness correction (when the corrected value exceeds the range [0, 255], the excess part is cropped, for example, when the corrected value is 257, the excess part is cropped to obtain a value of 155). The corrected RGB value is represented as:

[0090] R′=R·scale, G′=G·scale, B′=B·scale

[0091] In this embodiment, pixels in the color image that are not within the pixel value range corresponding to the primary color oil film, emulsified oil film, and thin oil film are used as background pixels, which correspond to the sea surface without oil film.

[0092] In this embodiment, since the target oil spill area is divided into multiple shooting areas of the same size, after a single shooting area is captured, the camera will upload the color image to the host computer. At the same time, the drone carrying the ultrasonic radar is controlled to go to the same area to measure the oil film thickness. The host computer uses existing image processing software platforms (such as MATLAB, OpenCV and other software tools) to divide the oil film in the color image into regions in real time based on the pixel value range corresponding to different types of oil films, and obtain the regions corresponding to different types of oil films.

[0093] Step 4: For thin oil film, sample and simulate the oil spill in the spill area, determine the relationship between different oil film thicknesses and image pixel values ​​under the same external light intensity, and determine the thickness of the thin oil film in the corresponding area based on the size of the pixel value of the thin oil film area in the color image.

[0094] It should be noted that before determining the thickness of the thin oil film based on the pixel values ​​of the thin oil film region in the color image, it is necessary to obtain the data on the relationship between the pixel values ​​and the thickness of the thin oil film through experimental measurement. Then, based on these corresponding data, a mathematical model of pixel values ​​and thickness is fitted by nonlinear fitting. In subsequent calculations, the corresponding thickness is directly calculated using this model, i.e., the pixel values ​​of the thin oil film region in the color image.

[0095] Using a multivariable nonlinear function to represent the relationship between pixel values ​​and thickness, the expression for the relationship between pixel values ​​and thickness of a thin oil film is obtained as follows:

[0096] h(R,G,B)=a·exp(b R R+bG G+b B B)

[0097] In the formula, h(R,G,B) is the thickness of the thin oil film, a is a constant coefficient representing the scaling factor of the model, and b R b is the weighting coefficient for the red channel, representing the influence of the red channel on oil film thickness. G b is the weighting coefficient for the green channel, representing the influence of the green channel on oil film thickness. B The weighting coefficient for the blue channel represents the influence of the blue channel on the oil film thickness.

[0098] Oil samples were collected from the spill area. Under the same external light intensity, oil samples were gradually added to the experimental tank in predetermined amounts. After each addition, an image of the oil film was taken vertically at a fixed height using a camera, and the thickness of the oil film was measured using a thickness measuring device. The pixel values ​​of the oil film at each thickness were recorded to obtain the relationship between the pixel values ​​of the thin oil film and its thickness. This relationship data was then substituted into the error function expression (representing the difference between the predicted and actual values), and the optimal parameters {a,b} were obtained by minimizing this error function. R ,b G ,b B The error function is expressed as:

[0099]

[0100] In the formula, M is the number of samples of the change relationship data, m is the index of the sample, and h is the index of the sample. m R represents the actual oil film thickness corresponding to the m-th sample (i.e., the thickness value measured in the experiment). m G represents the intensity value of the red channel corresponding to the m-th sample. m Let B be the intensity value corresponding to the green channel of the m-th sample. m This represents the intensity value of the blue channel corresponding to the m-th sample.

[0101] In this embodiment, the thin oil film is typically thin and uniform, exhibiting distinct color characteristics in optical detection. The color (i.e., the change in RGB values) of the thin oil film changes significantly with thickness, and the thickness can be predicted by pre-establishing a mathematical model of pixel values ​​and thickness. In contrast, the primary color oil film is thicker and has lower transparency, while the emulsified oil film has stronger light scattering properties. The color of these two types of oil films does not change significantly with thickness, therefore, the thickness cannot be determined using a model of pixel values ​​and thickness.

[0102] Step 5: For the mixed pixel region, set corresponding matching weights (i.e., oil volume contribution weights corresponding to different oil films) for each individual pixel according to its color similarity with the original color oil film and the emulsified oil film. Retrieve the initial oil film thickness of the mixed pixel region measured by the airborne ultrasonic radar, and update the oil film thickness corresponding to each pixel based on the matching weights.

[0103] For any region within a single color image, either the primary color oil film region or the emulsified oil film region, the average value of each channel is taken as the center RGB value of the oil film, and the center RGB value of the primary color oil film is... The center RGB value of the emulsified oil film is Among them, the center RGB value of the primary color oil film Represented as:

[0104]

[0105] In the formula, M represents the total number of pixel values ​​in the primary color oil film region, and i represents the index of each pixel in the primary color oil film region. Let be the red channel intensity value of the i-th pixel in the primary color oil film region. Let be the green channel intensity value of the i-th pixel in the primary color oil film region. This represents the blue channel intensity value of the i-th pixel in the primary color oil film region; the calculation method for the center RGB value of the emulsified oil film is the same as that for the center RGB value of the primary color oil film, and will not be repeated here.

[0106] For a single pixel in a mixed pixel region, the first Euclidean distance between the pixel and different oil films is calculated based on its three-channel pixel values. The second Euclidean distance between the primary color oil film and the emulsified oil film is calculated. The color similarity between the pixel and different oil films is determined based on the ratio of the first Euclidean distance to the second Euclidean distance. The similarity is normalized to obtain the matching weights between the pixel and the primary color oil film and between the pixel and the emulsified oil film. Specifically, the first Euclidean distance between the pixel and different oil films is expressed as:

[0107]

[0108] In the formula, D P D is the first Euclidean distance between the pixel and the primary color oil film. E Let be the first Euclidean distance between the pixel and the emulsion oil film, and j be the index of the pixel in the mixed pixel region; the second Euclidean distance between the primary color oil film and the emulsion oil film is expressed as:

[0109]

[0110] The color similarity between pixels in a mixed pixel region and different oil films is represented as follows:

[0111]

[0112] In the formula, L P L represents the color similarity between a pixel and the original color oil film. E The color similarity between a pixel and the emulsion film, ∈, is a small constant to prevent division by zero errors; the similarity is normalized to obtain the matching weight:

[0113]

[0114] In the formula, ω P ω represents the matching weight between pixels in the mixed pixel region and the primary color oil film. E This represents the matching weight between pixels in the mixed pixel region and the emulsion oil film.

[0115] The oil concentration at various points of the marine emulsified oil film is obtained using a sampling detection method. The average oil concentration at each point is taken as the oil concentration of the emulsified oil film. Measurement data from an airborne ultrasonic radar is retrieved to obtain the initial oil film thickness of each pixel in the mixed pixel region. Based on this initial oil film thickness and matching weight, the oil film thickness corresponding to each pixel is updated. Finally, the oil spill volume of each pixel is calculated based on the oil film thickness and the actual area corresponding to the pixel.

[0116]

[0117] In the formula, O mix The oil spill component represents the mixed pixel region, where s is the actual area corresponding to the pixel, and h is the oil spill component. mix,i Let c be the initial oil film thickness of the i-th pixel in the mixed pixel region. E Where I is the oil concentration of the emulsified oil film, I is the total number of pixels in the mixed pixel region, and h is the total number of pixels in the mixed pixel region. mix,i (ω P +ω E ·c E ) represents the updated oil film thickness of the i-th pixel in the mixed pixel region.

[0118] Step 6: For different types of oil films, calculate the oil spill volume of each pixel based on the actual area and thickness of the oil film corresponding to a single pixel in the color image, and then sum the oil spill volumes of each pixel in all color images to obtain the total oil spill volume.

[0119] For the primary color oil film in the color image, the measurement data from the airborne ultrasonic radar is retrieved to obtain the oil film thickness at each pixel in the primary color oil film region. Based on the actual area and oil film thickness corresponding to the pixel, the oil spill component of the primary color oil film is calculated:

[0120]

[0121] In the formula, OP Here, K represents the amount of oil spillage in the primary color oil film region, k represents the number of pixels in the primary color oil film region, and h represents the index of each pixel in the primary color oil film region. P,k The thickness of the oil film at the k-th pixel in the primary color oil film region.

[0122] For the emulsified oil film in the color image, the measurement data from the airborne ultrasonic radar is retrieved to obtain the oil film thickness at each pixel in the emulsified oil film region. Based on the actual area and oil film thickness corresponding to each pixel, the oil spill component of the emulsified oil film is calculated.

[0123]

[0124] In the formula, O E Here, V represents the oil spillage component in the emulsified oil film region, V represents the number of pixels in the emulsified oil film region, v represents the index of each pixel in the emulsified oil film region, and h represents the oil spillage component in the emulsified oil film region. E,v The thickness of the oil film at the v-th pixel in the emulsified oil film region.

[0125] Retrieve the oil film thickness data corresponding to each pixel in the thin oil film region of the color image. Based on the actual area and oil film thickness corresponding to each pixel, calculate the oil spill component of the thin oil film:

[0126]

[0127] In the formula, O T Let Y be the oil spill component in the thin oil film region, Y be the number of pixels in the thin oil film region, y be the index of each pixel in the thin oil film region, and h be the index of each pixel in the thin oil film region. T,y Let y be the thickness of the oil film at the y-th pixel in the thin oil film region.

[0128] The amount of oil spilled corresponding to a single color image is:

[0129] O Sec =O P +O E +O T +O mix

[0130] The total oil spill volume is obtained by superimposing the oil spill volumes corresponding to all the color images.

[0131] In this embodiment, the oil spill area captured in a single color image is only a part of the seawater oil spill area. When calculating the total oil spill volume, it is necessary to superimpose the oil spill volumes corresponding to all the calculated color images.

[0132] The technical solution of this application uses radar to detect oil films with a large thickness and determines the thickness of thin oil films based on color. It is suitable for heavy oils with a darker color, such as crude oil and HFO. Moreover, the technical solution of this application can divide the target oil spill area in a grid-like manner, calculate the oil spill volume based on the color image of a single area, and finally perform superposition calculation. It is suitable for detecting target oil spill areas with a large area.

[0133] The steps in this application can be rearranged, combined, or deleted according to actual needs.

[0134] The units in the device of this application can be merged, divided, or deleted according to actual needs.

[0135] Although this application has been disclosed in detail with reference to the accompanying drawings, it should be understood that these descriptions are merely exemplary and not intended to limit the application of this application. The scope of protection of this application is defined by the appended claims and may include various variations, modifications, and equivalents of the invention without departing from the scope and spirit of this application.

Claims

1. A method for estimating oil spill volume based on aerial surveying, characterized in that, The method includes: Step 1: Use an airborne camera to sample and photograph the target oil spill area to obtain a pre-collected image. Manually calibrate the pre-collected image to classify the oil film in the pre-collected image into original color oil film, emulsified oil film and thin oil film. Step 2: Based on the color characteristics of different types of oil films in the pre-collected image, set the pixel value range corresponding to each type of oil film. If the pixel value ranges of each oil film overlap, then process as follows: If the pixel value range of the thin oil film overlaps with the pixel value range of the original color or emulsified oil film, then take the middle value of the overlapping part as the boundary pixel value and redivide the pixel value range of the thin oil film and other oil films. If the pixel value range of the original color oil film overlaps with the pixel value range of the emulsified oil film, then take the overlapping part as the mixed pixel value range. Step 3: Use an airborne camera to capture a color image of the target oil spill area from the vertical direction. Correct the brightness of the color image based on the average brightness of the pre-acquired image. Divide the oil film in the corrected color image into regions based on the pixel value range and mixed pixel value range of each oil film. At the same time, use an airborne ultrasonic radar to measure the same region and obtain the oil film thickness in the same region. Step 4: For thin oil film, sample and simulate the oil spill in the spill area, determine the relationship between different oil film thicknesses and image pixel values ​​under the same external light intensity, and determine the thickness of the thin oil film in the corresponding area based on the size of the pixel value of the thin oil film area in the color image. Step 5: For the mixed pixel region, set corresponding matching weights for each individual pixel according to its color similarity with the original color oil film and the emulsified oil film, retrieve the initial oil film thickness of the mixed pixel region measured by the airborne ultrasonic radar, and update the oil film thickness corresponding to each pixel based on the matching weights. Step 6: For different types of oil films, calculate the oil spill volume of each pixel based on the actual area and thickness of the oil film corresponding to a single pixel in the color image, and then superimpose the oil spill components of each pixel in all color images to obtain the total oil spill volume.

2. The oil spill estimation method based on aerial survey as described in claim 1, characterized in that, In step 2, the corresponding pixel value range is set according to the color characteristics of different types of oil films in the pre-collected image, specifically including: Based on the oil film classification results, the original color oil film area, emulsified oil film area, and thin oil film area are marked in the pre-acquired image. All pixel values ​​corresponding to each oil film area are extracted. For any given area, the initial three-channel pixel value range is first set based on the extreme values ​​of the three-channel pixel values ​​in the area. Then, the three-channel pixel value ranges for each type of oil film are adjusted according to the overlap of the pixel value ranges. The initial three-channel pixel value range for the original color oil film is [P]. Min ,P Max The initial three-channel pixel value range of the emulsified oil film is [E]. Min E Max The initial three-channel pixel value range of the thin oil film is [T]. Min ,T Max ], where P represents the original color oil film, E represents the emulsified oil film, and T represents the thin oil film.

3. The oil spill estimation method based on aerial survey as described in claim 2, characterized in that, The pixel value range of the primary color oil film [P] Min ,P Max ] is represented as: In the formula, This represents the minimum intensity values ​​of the red, green, and blue channels in the primary color oil film region, obtained by averaging the minimum values ​​of the primary color oil film pixels in different pre-captured images. This represents the maximum intensity values ​​of the red, green, and blue channels in the primary color oil film region obtained by averaging the maximum values ​​of the primary color oil film pixels in different pre-captured images. N is the number of pre-captured images, and n is the index of the pre-captured image.

4. The oil spill estimation method based on aerial survey as described in claim 3, characterized in that, In step 2, the three-channel pixel value ranges of each type of oil film are adjusted according to the overlap of pixel value ranges, specifically including: For any two oil films, when the pixel value ranges of the three channels all intersect, it is determined that the pixel value ranges of the three channels of the two oil films overlap. The three-channel pixel value range of the thin oil film is compared with the three-channel pixel values ​​of the other two oil films. When the three-channel pixel value ranges of the two oil films being compared overlap, the channel with the smallest overlap range is selected for adjustment, while the pixel value ranges of the other two channels remain unchanged. If the smallest overlap range is not unique, a channel is randomly selected. For the selected channel, the middle value of the overlapping part is used as the boundary pixel value to divide the two pixel value ranges, thus distinguishing between the two categories. The three-channel pixel value range of the primary color oil film is compared with the three-channel pixel value of the emulsion oil film. When the three-channel pixel value ranges of the two oil films overlap, the overlapping part is taken as the mixed pixel value range. The channel with the smallest overlapping range and no inclusion relationship between the pixel value ranges is selected as the determination channel to distinguish between the primary color oil film, the emulsion oil film and the mixed pixels. If the smallest overlapping range is not unique, a channel is randomly selected as the determination channel. For the selected channel, the pixels of the overlapping part are the mixed pixels.

5. The oil spill estimation method based on aerial survey as described in claim 2, characterized in that, Step 4 specifically includes: By using a multivariable nonlinear function to represent the relationship between pixel values ​​and thickness, the expression for the pixel values ​​and thickness of a thin oil film is obtained: h(R,G,B)=a·exp(b R R+b G G+b B B) In the formula, h(R,G,B) is the thickness of the thin oil film, a is a constant coefficient, and b is a constant coefficient. R b is the weighting coefficient for the red channel. G b is the weighting coefficient for the green channel. B This represents the weighting coefficient for the blue channel; The experiment obtained data on the relationship between pixel values ​​and thickness of the thin oil film. This data was then substituted into the error function expression, and the optimal parameters {a,b} were obtained by minimizing this error function. R ,b G ,b B The error function is expressed as: In the formula, M is the number of samples of the change relationship data, m is the index of the sample, and h is the index of the sample. m R represents the actual oil film thickness corresponding to the m-th sample. m G represents the intensity value of the red channel corresponding to the m-th sample. m Let B be the intensity value corresponding to the green channel of the m-th sample. m This represents the intensity value of the blue channel for the m-th sample.

6. The oil spill estimation method based on aerial survey as described in claim 3, characterized in that, Setting the corresponding matching weights in step 5 specifically includes: For any region within the primary color oil film region and the emulsified oil film region, the average value of each channel is taken as the center RGB value of the oil film, and the center RGB value of the primary color oil film is... The center RGB value of the emulsified oil film is Calculate the first Euclidean distance between a single pixel in the mixed pixel region and different oil films, calculate the second Euclidean distance between the primary color oil film and the emulsified oil film, determine the color similarity between the pixel and different oil films based on the ratio of the first Euclidean distance to the second Euclidean distance, and normalize the similarity to obtain the matching weight between the pixel and different oil films. The color similarity between pixels in the mixed pixel region and different oil films is: In the formula, L P L represents the color similarity between a pixel and the original color oil film. E The color similarity between a pixel and the emulsion oil film, where ∈ is a constant, and D P D is the first Euclidean distance between the pixel and the primary color oil film. E D is the first Euclidean distance between the pixel and the emulsion oil film. PE The second Euclidean distance; normalizing the similarity yields: In the formula, ω P ω represents the matching weight between pixels in the mixed pixel region and the primary color oil film. E The matching weights between pixels in the mixed pixel region and the emulsion oil film.

7. The oil spill estimation method based on aerial survey as described in claim 6, characterized in that, Step 5 further includes: for mixed pixel regions, calculating the oil overflow amount of the pixel based on the oil film thickness of the pixel and the matching weight corresponding to different oil films, specifically including: By retrieving measurement data from the airborne ultrasonic radar, the initial oil film thickness of each pixel in the mixed pixel region is obtained. Based on the matching weights between the pixel and different oil films, the oil spill volume of the pixel is calculated. In the formula, O mix The oil spill component represents the mixed pixel region, where s is the actual area corresponding to the pixel, and h is the oil spill component. mix,i Let c be the initial oil film thickness of the i-th pixel in the mixed pixel region. E I represents the oil concentration of the emulsified oil film, and I represents the total number of pixels in the mixed pixel region.

8. The oil spill estimation method based on aerial survey as described in claim 5, characterized in that, In step 6, for the primary color oil film, emulsified oil film, and thin oil film, the amount of oil spillage at each pixel is calculated based on the actual area and oil film thickness corresponding to a single pixel in the color image. Specifically, this includes: By retrieving measurement data from the airborne ultrasonic radar, the oil film thickness of each pixel in the primary color oil film area and the emulsified oil film is obtained. Based on the actual area corresponding to each pixel and the oil overflow component of each pixel's oil film thickness, the oil overflow component of the primary color oil film area is: In the formula, K is the number of pixels in the primary color oil film region, and h P,k Let be the oil film thickness at the k-th pixel in the primary color oil film region, and let be the oil overflow component of the emulsified oil film: In the formula, V represents the number of pixels in the emulsified oil film region, and h E,d Let be the oil film thickness at the v-th pixel in the emulsified oil film region; Retrieve the oil film thickness data for each pixel in the thin oil film area. Based on the actual area of ​​each pixel and the oil film thickness of each pixel, calculate the oil overflow component of the thin oil film: In the formula, Y represents the number of pixels in the thin oil film region, and h T,y Let y be the thickness of the oil film at the y-th pixel in the thin oil film region.

9. The oil spill estimation method based on aerial survey as described in claim 1, characterized in that, Step 3 includes brightness correction of the color image based on the average brightness of the pre-acquired image, specifically including: calculating a brightness normalization factor in advance based on the average brightness of the pre-acquired image. L s L represents the average brightness of the pre-acquired image. f To calculate the average brightness of a color image, the RGB value of each pixel in the color image is multiplied by the scale to perform brightness correction. The corrected RGB values ​​are represented as R′=R·scale, G′=G·scale, B′=B·scale.

Images