Water-leaving reflectivity determination method, water transparency determination method and device

Abstract

The invention provides a water-leaving reflectivity determination method, a water transparency determination method and a water-leaving reflectivity determination device. According to the specific implementation scheme, a water surface image is obtained, the image content of the water surface image comprises a water body, a color card and a reflector, and the color card comprises a plurality of color blocks with different reflectivity; cutting the water surface image to obtain a first image block related to a water body, a second image block related to mirror surface information of a reflector and a plurality of third image blocks related to a color card; determining a first reflectivity related to the first image block and a second reflectivity related to the second image block based on the pixel information of the first image block, the pixel information of the second image block, the pixel information of the plurality of third image blocks and the reflectivity information of the plurality of color blocks; the off-water reflectance is determined based on the first reflectance and the second reflectance.

Description

TECHNICAL FIELD

[0001] The present disclosure belongs to the technical field of environmental remote sensing and water environment monitoring, and more particularly, relates to a water-leaving reflectance determination method, a water body transparency determination method and device. BACKGROUND

[0002] At present, the acquisition of water quality parameters mainly relies on traditional manual sampling and remote sensing methods. Among them, remote sensing monitoring has the advantages of large range and high efficiency, but it mainly depends on professional sensor equipment, which is high in cost and inconvenient to deploy. With the popularity of digital cameras and smart phones, it is possible to obtain water optical information based on ordinary photographic equipment. Determining water-leaving reflectance is the premise of water quality parameter inversion, so it has very important application value. The JPEG format photo obtained based on ordinary photographic equipment has the advantages of convenient acquisition and rapid processing, but due to its nonlinear optical response and image compression characteristics, direct use in water-leaving reflectance determination has certain challenges, especially in field applications, which are limited by observation geometry, exposure parameters and environmental interference, so a complete and reliable water-leaving reflectance determination method is urgently needed. SUMMARY

[0003] In view of this, the present disclosure provides a water-leaving reflectance determination method, a water body transparency determination method and device.

[0004] In a first aspect of the embodiments of the present disclosure, a water-leaving reflectance determination method is provided, comprising: obtaining a water surface image, the image content of the water surface image containing a water body, a color card and a mirror, the color card containing a plurality of color blocks with different reflectivities; performing cropping processing on the water surface image to obtain a first image block related to the water body, a second image block related to the mirror surface information of the mirror, and a plurality of third image blocks related to the color card; determining a first reflectivity related to the first image block and a second reflectivity related to the second image block based on the pixel information of the first image block, the pixel information of the second image block, the pixel information of the plurality of third image blocks, and the reflectivity information of the plurality of color blocks; and determining the water-leaving reflectance based on the first reflectivity and the second reflectivity.

[0005] In an embodiment of the present disclosure, for any one target image block in the first image block, the second image block and the plurality of third image blocks, the method comprises: determining a target gray scale value corresponding to each of a plurality of color channels of the target image block based on the gray scale values of a plurality of pixel points of the target image block in the plurality of color channels; and obtaining the pixel information of the target image block based on the target gray scale values corresponding to the plurality of color channels.

[0006] In another embodiment of the present disclosure, the determining the first reflectance related to the first image block and the second reflectance related to the second image block based on the pixel information of the first image block, the pixel information of the second image block, the pixel information of the plurality of third image blocks and the reflectance information of the plurality of color blocks comprises: determining a fitting relationship between the pixel information of an image block and the reflectance information related to the image block based on the pixel information of the plurality of third image blocks and the reflectance information of the plurality of color blocks; determining the first reflectance related to the first image block based on the fitting relationship and the pixel information of the first image block; determining the second reflectance related to the second image block based on the fitting relationship and the pixel information of the second image block.

[0007] In yet another embodiment of the present disclosure, the determining the fitting relationship between the pixel information of an image block and the reflectance information related to the image block based on the pixel information of the plurality of third image blocks and the reflectance information of the plurality of color blocks comprises: performing linear fitting on the natural logarithm of the pixel information of the plurality of third image blocks as the independent variable and the natural logarithm of the reflectance information of the plurality of color blocks as the dependent variable to determine a fitting coefficient; and determining the fitting relationship between the pixel information of an image block and the reflectance information related to the image block based on the fitting coefficient.

[0008] In still another embodiment of the present disclosure, the cropping the water surface image to obtain the first image block related to the water body, the second image block related to the mirror surface information of the mirror and the plurality of third image blocks related to the color card comprises: determining a first image area related to the water body, a second image area related to the mirror surface information of the mirror and a third image area related to the color card from the water surface image; cropping the first image block from the first image area; cropping the second image block from the second image area; and cropping the plurality of third image blocks from the third image area based on the color information of each of the plurality of color blocks.

[0009] In still another embodiment of the present disclosure, the mirror surface information comprises sky image information reflected by the mirror.

[0010] In still another embodiment of the present disclosure, the reflectance information of the plurality of color blocks comprises a plurality of reflectance values of the color blocks related to the plurality of color channels.

[0011] In a second aspect of the embodiments of the present disclosure, a method for determining the transparency of a water body is provided, comprising: determining the transparency of a target water body based on the ex-water reflectance of the target water body; wherein the ex-water reflectance of the target water body is determined according to the ex-water reflectance determination method as described above.

[0012] In a third aspect of this disclosure, a device for determining water reflectivity is provided, comprising:

[0013] The image acquisition module is used to acquire water surface images. The image content of the water surface images includes water, color charts, and reflectors. The color charts contain multiple color blocks with different reflectivities.

[0014] The image cropping module is used to crop the water surface image to obtain a first image block related to the water body, a second image block related to the mirror information of the reflector, and multiple third image blocks related to the color card.

[0015] The image processing module is configured to determine a first reflectance related to the first image block and a second reflectance related to the second image block based on the pixel information of the first image block, the pixel information of the second image block, the pixel information of a plurality of third image blocks, and the reflectance information of a plurality of color blocks.

[0016] The reflectivity determination module is used to determine the water-free reflectivity based on the first reflectivity and the second reflectivity mentioned above.

[0017] In a fourth aspect of the present disclosure, a water transparency determination apparatus is provided, comprising: a transparency determination module, configured to determine the transparency of a target water body based on the water-free reflectance of the target water body; wherein the water-free reflectance of the target water body is determined according to the water-free reflectance determination method described above.

[0018] According to embodiments of this disclosure, a water surface image containing image information of the water body, image information of a color chart, and mirror image information of a reflector can be acquired. Based on this image information, the water reflectance can be determined to analyze water quality parameters. Compared to existing technologies, this disclosure, by introducing a reflector, can acquire sky image information reflected by the reflector and determine the water reflectance based on this sky image information and the water body image. This reduces the influence of skylight interference signals on the water reflectance measurement and improves the accuracy of the water reflectance measurement. Attached Figure Description

[0019] The above and other objects, features and advantages of this disclosure will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0020] Figure 1 A schematic diagram of a water surface image for determining water reflectivity according to an embodiment of the present disclosure is shown.

[0021] Figure 2 A flowchart illustrating a method for determining water reflectivity according to an embodiment of the present disclosure is shown schematically.

[0022] Figure 3A flowchart illustrating a method for determining water reflectivity according to another embodiment of the present disclosure is shown schematically.

[0023] Figure 4 A flowchart illustrating a method for determining water transparency according to an embodiment of the present disclosure is shown schematically.

[0024] Figure 5 A block diagram of a water reflectance determination apparatus according to an embodiment of the present disclosure is shown schematically.

[0025] Figure 6 A block diagram of a water transparency determination apparatus according to an embodiment of the present disclosure is shown schematically. Detailed Implementation

[0026] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0027] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0028] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0029] Water quality parameters reflect the health and cleanliness of water bodies. Currently, the main methods for measuring water quality parameters include manual sampling and remote sensing image analysis. However, both methods rely heavily on specialized sensor equipment, which is costly and inconvenient to deploy.

[0030] The prerequisite for retrieving various water quality parameters is obtaining relatively accurate water reflectance. With the widespread use of digital cameras and smartphones, it has become possible to obtain water reflectance using ordinary photographic equipment. Current research has shown that smartphones can be used to acquire water surface images and determine water reflectance based on these images.

[0031] Water reflectivity refers to the proportion of light energy that, after hitting a body of water, is actually reflected back from the water surface and reaches the sensor. However, the water reflectivity information obtained by existing methods is not accurate and requires further correction. This is because the signal received by the sensor contains two main parts:

[0032] Water-leaving radiation: Light enters the water body, interacts with chlorophyll, suspended matter, and other substances in the water, and is then reflected back out of the water surface, which is the desired useful signal.

[0033] Water surface reflection radiation: The water surface directly reflects the bright light from the sky above it (skylight) into the sensor, which is an unwanted interference noise signal.

[0034] Therefore, water reflectivity correction is needed to eliminate interference from skylight.

[0035] To correct for water reflectance and eliminate interference from skylight, it is necessary to acquire images of both the water body and the sky simultaneously. Acquiring water images requires adherence to standard observation geometry specifications. These specifications are a unified set of geometrical conditions, including observation angles, positions, and coordinate systems, established to ensure effective comparison and correct interpretation of measurement results across different times, instruments, and operators. Based on the observation angles defined by these specifications, current technology cannot simultaneously capture images of both the water body and the sky; instead, it requires separate captures in two steps to obtain separate images of the water body and the sky. However, because the sky is relatively bright and the water body relatively dark, the exposure parameters of the two images are inconsistent, and it is impossible to normalize and correct the image information of the water body and sky images based on these exposure parameters.

[0036] Therefore, a method is needed to simultaneously acquire sky and water images from a specific angle. Based on this, a reflector is considered. The sky image can be reflected through the reflector, allowing for simultaneous capture of both water and sky images from a predefined observation angle. Since the water and sky images use the same exposure parameters, exposure parameter correction is unnecessary, and the images can be directly used to determine the reflectivity away from the water.

[0037] Figure 1 A schematic diagram of a water surface image for determining water reflectivity according to an embodiment of the present disclosure is shown.

[0038] like Figure 1 As shown, the schematic diagram of the water surface image according to this embodiment may include a water body 101, a color chart 102, and a reflector 103.

[0039] Water body 101 contains image information related to the content of the water body in the water surface image, and the reflectance of the water body can be obtained based on this image information.

[0040] The color chart 102 contains image information related to the content of the color chart in the water surface image. Based on this image information and the reflectance of the color chart under different light conditions, image information and reflectance information related to the image information can be obtained.

[0041] The reflector 103 contains image information related to the content of the reflector in the water surface image, and reflectivity information related to the image information reflected by the reflector can be obtained based on this image information.

[0042] Figure 2 A flowchart illustrating a method for determining water reflectivity according to an embodiment of the present disclosure is shown schematically.

[0043] like Figure 2 As shown, the method for determining water reflectance includes operations S210~S240.

[0044] In operation S210, a water surface image is acquired. The water surface image contains the water body, a color chart, and a reflector. The color chart contains multiple color blocks with different reflectivities.

[0045] In operation S220, the water surface image is cropped to obtain a first image block related to the water body, a second image block related to the mirror surface information of the reflector, and multiple third image blocks related to the color card.

[0046] In operation S230, based on the pixel information of the first image block, the pixel information of the second image block, the pixel information of multiple third image blocks, and the reflectance information of multiple color blocks, a first reflectance associated with the first image block and a second reflectance associated with the second image block are determined.

[0047] In operation S240, the water-free reflectivity is determined based on the first reflectivity and the second reflectivity.

[0048] Images of the water surface can be captured by photographic devices such as smartphones or digital cameras. These devices, based on different image sensors and image sensing principles, can obtain image data in different formats. Currently, most smartphones or digital cameras save photos in JPEG (Joint Photographic Experts Group) format by default, which is also the most common image format on the internet. The most common characteristic of JPEG format images is that they undergo lossy compression, significantly compressing file size by discarding image information that is not sensitive to the human eye, making them easy to store and share. Some professional-grade cameras can capture photos in RAW (Raw Image Format), which are the raw, unprocessed light signal data captured by the image sensor.

[0049] The format of the acquired water surface image can be JPEG, RAW, etc.

[0050] Taking images of the water surface is best done on a clear, cloudless day with stable lighting. Scientific measurements of the water surface require a controllable and known light source, namely the sun. Therefore, clear, cloudless weather is essential to ensure that the sun is the sole dominant light source, with stable light intensity that is not affected by drifting clouds. Of the signals received from the image sensor, less than 10% is the useful signal from the water body (water reflectance); over 90% is interference caused by atmospheric scattered light. Clouds are moving diffusers that cause drastic changes in light intensity within a short period. On clear, cloudless days, algorithms can most accurately estimate and eliminate interference, thus extracting the pure water signal. If the lighting is unstable when measuring water reflectance, the differences in the measured signal may be primarily due to changes in the light source, rather than the properties of the water itself.

[0051] When photographing water surfaces, choose calm, waveless waters. Waves alter the geometry of the water surface; each wave's slope and each bubble reflects and scatters light at different angles, making the water's reflectivity extremely complex, random, and unpredictable. This makes it impossible to accurately estimate and correct for skylight reflected from the water, introducing significant errors when calculating water reflectivity.

[0052] Imaging water surfaces can be based on the "above-the-surface method." This method is a sophisticated, standardized optical measurement protocol that, through strict angle control, extracts the true optical signals of water bodies from complex background noise. To use the above-the-surface method, the imaging equipment needs to be pointed at the water surface with specific observation zenith angles (typically 40°) and azimuth angles (typically 90°-135°). The observation zenith angle is the angle between the imaging equipment's observation direction and the local zenith direction, where the zenith direction is the direction the observer points perpendicular to the ground towards the sky, and the observation direction is the direction the observer points towards the observed object. The azimuth angle is the horizontal angle measured clockwise from the geographic North Pole to the observation direction.

[0053] Water bodies can be lakes, rivers, reservoirs, ponds, etc.

[0054] The reflector can be a mirrored aluminum plate, a silver-plated glass mirror, etc.

[0055] The number of color blocks can be 2, 3, 4, 5, or more. For example, when there are 3 color blocks, the reflectance of each color block in the color chart can be 25%, 50%, 75% or 20%, 50%, 80%. When there are 5 color blocks, the reflectance of each color block in the color chart can be 10%, 30%, 50%, 70%, 90% or 10%, 20%, 50%, 80%, 90%.

[0056] A suitable image segmentation algorithm can be used to segment a water surface image into different parts based on the specific content it contains, and then crop out a first image block, a second image block, and multiple third image blocks from these different parts.

[0057] The first image patch related to the water body is a portion of the image obtained by cropping an image region that contains only water content. This image region containing only water content is obtained from the water surface image using a suitable image segmentation algorithm.

[0058] The second image patch associated with the reflector is a portion of the image obtained by cropping an image region that contains only the reflector content. This image region, containing only the reflector content, is obtained from the water surface image using a suitable image segmentation algorithm.

[0059] Multiple third image patches associated with the color chart are obtained by cropping image regions containing only the color chart content. These color chart-only image regions are obtained from the water surface image using a suitable image segmentation algorithm. The color chart-only image regions include image regions containing multiple color patches, and each partial image is cropped from the image region of each color patch.

[0060] The number of third image blocks can be 2, 3, 4, 5 or more, but the number must be consistent with the number of color blocks, and each third image block contains only the color information of one color block.

[0061] Suitable image segmentation algorithms, as described above, may include threshold segmentation, edge detection segmentation, region growing segmentation, semantic segmentation, instance segmentation, panoptic segmentation, etc., and are not limited here.

[0062] The pixel information of an image block can include pixel position information, pixel color information, pixel transparency information, etc.

[0063] The reflectance information of a color block can be the reflectance value of the color block under visible light, ultraviolet light, and X-rays.

[0064] The water reflectance WaterRrs can be determined using the following formula:

[0065] (1)

[0066] Where FirstRef is the first reflectivity and SecondRef is the second reflectivity.

[0067] Through the embodiments of the present invention, sky image information reflected by the reflector can be obtained, and the water reflectivity can be determined based on the sky image information and the image of the water body, thereby reducing the influence of interference signals from sky light on the water reflectivity measurement and improving the accuracy of water reflectivity measurement.

[0068] In some embodiments, obtaining the pixel information of any one of the first image block, the second image block, and a plurality of third image blocks for a target image block may include the following operations:

[0069] Based on the grayscale values ​​of multiple pixels in multiple color channels of the target image block, the target grayscale values ​​corresponding to the target image block and each of the multiple color channels are determined; and based on the target grayscale values ​​corresponding to each of the multiple color channels, the pixel information of the target image block is obtained.

[0070] The grayscale value of a pixel in multiple color channels can be the grayscale value of a pixel in the red, green, and blue color channels, or it can be the grayscale value of a pixel in the red, green, blue, and cyan color channels. A pixel is the smallest unit in a digital image. It can carry color information. A color channel is the result of decomposing this color into different primary color components. Each channel is responsible for recording the intensity of only one primary color component, and the grayscale value is the basic unit for describing the intensity of a single color channel. The color of a pixel is determined by the grayscale values ​​of multiple channels.

[0071] The target grayscale value of a target image patch in each of the red, green, and blue color channels can be the median, mode, or mean of the grayscale values ​​of all pixels in the image patch in each of the three color channels. For example, if a target image patch contains multiple pixels, and a pixel has red, green, and blue color channels, then the target image patch has grayscale values ​​in all three color channels. If a pixel has a specific grayscale value in the red color channel, then the grayscale value of the target image patch in the red color channel can be either the average of all grayscale values ​​of all pixels in the image patch in the red color channel, or the mode of all grayscale values ​​of all pixels in the image patch in the red color channel.

[0072] The target grayscale value of a target image patch in each of the four color channels (red, green, blue, and cyan) can be the median, mode, or mean of the grayscale values ​​of all pixels in the image patch in each of the four color channels. The number of grayscale values ​​of the target image patch is the same as the number of color channels of the pixels in the target image patch. If the pixels of the target image patch have four color channels (red, green, blue, and cyan), then the target image patch has four grayscale values ​​in the four color channels (red, green, blue, and cyan).

[0073] Through the embodiments of the present invention, multiple grayscale value information of any one of the first image block, the second image block, and multiple third image blocks can be conveniently obtained. The aforementioned multiple grayscale value information can correspond to the reflectance information of different colors of light, and the reflectance information of different colors of light can reflect various water quality parameters of the water body. Therefore, through the embodiments of the present invention, various water quality parameters of the water body can be conveniently obtained.

[0074] According to embodiments of this disclosure, determining a first reflectance associated with the first image block and a second reflectance associated with the second image block based on pixel information of a first image block, pixel information of a second image block, pixel information of a plurality of third image blocks, and reflectance information of a plurality of color blocks may include the following operations:

[0075] Based on the pixel information of multiple third image blocks and the reflectance information of multiple color blocks, a fitting relationship is determined between the pixel information of the image blocks and the reflectance information associated with the image blocks; based on the fitting relationship and the pixel information of the first image block, a first reflectance associated with the first image block is determined; based on the fitting relationship and the pixel information of the second image block, a second reflectance associated with the second image block is determined.

[0076] The pixel information of multiple third image patches and the reflectance information of multiple color patches are known. The fitting relationship between the pixel information of the image patches and the reflectance information of the color patches can be determined by methods such as least squares method, polynomial fitting, and nonlinear fitting.

[0077] There is a correspondence between the pixel information of multiple third image blocks and the reflectance information of multiple color blocks. For example, if a third image block is obtained by cropping from a color chart area, then there is a correspondence between the third image and the color chart. The third image block has grayscale values ​​in red, green, and blue color channels, while the color chart has reflectance values ​​under red, green, and blue light. Therefore, the grayscale value of the third image block in the red color channel and the reflectance value of the color chart under red light are the first set of data points. The grayscale value of the third image block in the green color channel and the reflectance value of the color chart under green light are the second set of data points. The grayscale value of the third image block in the blue color channel and the reflectance value of the color chart under blue light are the third set of data points. Based on these three sets of data points, the fitting relationship between the pixel information of the image block and the reflectance information of the color block can be determined.

[0078] As an alternative fitting method, the fitting relationship between the pixel information of an image patch and the reflectance information associated with the image patch can be determined by the following method.

[0079] For example, the color chart has 5 color blocks, each with a corresponding reflectance value under red, blue, and green light; there are 5 third image blocks, each with grayscale values ​​in the red, blue, and green color channels; since the third image blocks are cropped from different color block images, there is a one-to-one correspondence between the 5 color blocks and the 5 third image blocks, with the first third image block corresponding to the first color block, the second third image block corresponding to the second color block, and so on.

[0080] Calculate the linear regression coefficients:

[0081] (2)

[0082] (3)

[0083] Where G_a and G_b both represent linear regression coefficients, Let represent the natural logarithm of the grayscale value of the i-th image patch in a certain color channel. It represents the natural logarithm of the reflectance value of the i-th color patch under the light corresponding to that color channel.

[0084] Because there are three color channels: red, blue, and green, the linear regression coefficients G_a and G_b each have three different values, with each value corresponding to a color.

[0085] Based on the linear regression coefficients, the fitted relationship is expressed as follows:

[0086] (4)

[0087] Where Y is the reflectance information associated with the image patch, and X is the pixel information of the image patch.

[0088] Through the embodiments of the present invention, multiple sets of related data points composed of pixel information of multiple third image blocks and reflectance information of multiple color blocks can be conveniently obtained, and a more accurate fitting relationship can be determined based on the multiple sets of data points, thereby determining the relevant reflectance through the fitting relationship and the pixel information of the related image blocks.

[0089] In some embodiments, cropping the water surface image to obtain a first image block related to the water body, a second image block related to the mirror surface information of the reflector, and multiple third image blocks related to the color chart may include the following operations:

[0090] From the water surface image, determine a first image region related to the water body, a second image region related to the mirror surface information of the reflector, and a third image region related to the color card; crop a first image block from the first image region; crop a second image block from the second image region; based on the color information of each of the multiple color blocks, crop multiple third image blocks from the third image region.

[0091] When defining the first, second, and third image regions, an edge-detection-based image segmentation algorithm can be selected. Leveraging the characteristic that cards and mirrors typically have clear, continuous, and regularly shaped edges, all strong edges are first identified. Then, contours conforming to the geometric features of the cards and mirrors are selected from these edges. Finally, these contours are used to segment the water surface image into a first image region related to the water body, a second image region related to the mirror's surface information, and a third image region related to the color card. The image is then segmented according to different image content to obtain different image regions containing only specific content. Subsequently, cropping operations are performed on the segmented image regions to obtain different image blocks corresponding to each region.

[0092] The first image patch is the portion of the first image region that does not contain specific interfering features. Specific interfering features can be solar flares, shadows, and floating objects on the water surface. Solar flares are extremely intense, highly directional specular reflections formed by the specular reflection of sunlight on the water surface. Their intensity and polarization characteristics are drastically different from skylight, being hundreds or thousands of times greater than the weak radiance of water. When the image region contains flares, the flare signal can overwhelm the water signal, and the sensor's dynamic range may saturate, leading to complete data invalidation. Shadows fundamentally alter lighting conditions. In unshaded areas, the light source is direct sunlight plus diffuse skylight, a stable, measurable, and modelable lighting environment. However, in shaded areas, the light source is reduced to diffuse skylight or more complex environmental reflected light, with direct sunlight completely blocked. This causes a dramatic change in the incident light spectrum of the water under shadow, resulting in a measured water reflection signal that reflects unnatural, abnormal lighting conditions, making it incomparable to standard models and data from unshaded areas.

[0093] The second image patch can be the geometric center of the second image region. This is because the edge regions of the image are more likely to contain unwanted interference, and edge stretching and distortion are most severe under lens distortion, while the central region typically has the best optical quality, the most concentrated information, and the least interference. First, determine the geometric center point of the second image region, then determine the size of the central portion. Based on the geometric center point and the size of the central portion, the required second image patch can be determined. The size of the central portion can be one-fifth or one-quarter of the size of the second image region.

[0094] Through the embodiments of the present invention, some specific interference information in the image can be eliminated to obtain target image patches containing more accurate image information. Based on these more accurate target image patches, more accurate water reflectance can be obtained.

[0095] In some embodiments, the mirror information of the reflector may be the sky image information reflected by the reflector.

[0096] Through the embodiments of the present invention, water body image information and sky image information can be acquired simultaneously at a standardized observation angle. The water body image and sky image use the same exposure parameters, which can be directly used for water reflectance correction to obtain more accurate water reflectance.

[0097] In some embodiments, the reflectance information of a color block can be the reflectance values ​​of the color block under red, green, and blue light, or the reflectance values ​​of the color block under red, green, blue, and cyan light. The reflectance value of a color block under any color of light needs to correspond to the color channel component of the pixel of the third image block corresponding to that color block. For example, the reflectance value of a color block under red light needs to correspond to the red channel component of the pixel of the third image block corresponding to that color block, and the reflectance value of a color block under blue light needs to correspond to the blue channel component of the pixel of the third image block corresponding to that color block.

[0098] Through the embodiments of the present invention, various reflectance information of color blocks can be easily obtained, and a fitting relationship can be obtained based on the reflectance information and the image information of the image blocks, providing a guarantee for the subsequent determination of water-free reflectance.

[0099] Figure 3 A flowchart illustrating a method for determining water reflectivity according to another embodiment of the present disclosure is shown schematically.

[0100] like Figure 3 As shown, the method for determining water reflectance includes operations S310~S380.

[0101] During operation S310, a water surface image is acquired. The image content of the water surface image includes the water body, a color chart, and a reflector. The color chart contains multiple color blocks with different reflectivities.

[0102] In operation S320, a first image region related to the water body, a second image region related to the mirror information of the reflector, and a third image region related to the color chart are determined from the water surface image.

[0103] In operation S330, a first image block is cropped from the first image region, a second image block is cropped from the second image region, and multiple third image blocks are cropped from the third image region based on the color information of each of the multiple color blocks.

[0104] In operation S340, based on the grayscale values ​​of each pixel in the target image block in the multiple color channels, the target grayscale values ​​corresponding to the target image block and the multiple color channels are determined.

[0105] In operation S350, pixel information of the target image block is obtained based on the target grayscale values ​​corresponding to each of the multiple color channels.

[0106] In operation S360, based on the pixel information of multiple third image blocks and the reflectance information of multiple color blocks, the fitting relationship between the pixel information of the image blocks and the reflectance information associated with the image blocks is determined.

[0107] In operation S370, a first reflectance is determined based on the fitting relationship and the pixel information of the first image block, and a second reflectance is determined based on the fitting relationship and the pixel information of the second image block.

[0108] In operation S380, the water reflectance is determined based on the first reflectance and the second reflectance.

[0109] Figure 4 A schematic diagram of a method for determining water transparency according to an embodiment of the present disclosure is shown.

[0110] like Figure 4 As shown, the method includes operations S410~S420.

[0111] In operation S410, the water reflectance of the target water body is obtained.

[0112] In operation S420, the transparency of the target water body is determined based on the water reflectance of the target water body.

[0113] In the embodiments of this disclosure, the water reflectance of the target water body can be obtained using the water reflectance determination method described above, which will not be elaborated here.

[0114] Through the embodiments of the present invention, sky image information reflected by the reflector can be obtained, and the water reflectance can be determined based on the sky image information and the water body image, thereby reducing the influence of sky light interference signals on the water reflectance measurement, improving the accuracy of water reflectance measurement, and thus improving the accuracy of water transparency measurement.

[0115] Figure 5 A block diagram of a water reflectance determination apparatus according to an embodiment of the present disclosure is shown schematically.

[0116] like Figure 5 As shown, the water reflectance determination device includes an image acquisition module 510, an image cropping module 520, an image processing module 530, and a reflectance determination module 540.

[0117] The image acquisition module 510 is used to acquire a water surface image. The image content of the water surface image includes the water body, a color chart, and a reflector. The color chart contains multiple color blocks with different reflectivities.

[0118] The image cropping module 520 is used to crop the water surface image to obtain a first image block related to the water body, a second image block related to the mirror surface information of the reflector, and multiple third image blocks related to the color card.

[0119] The image processing module 530 is used to determine a first reflectance related to the first image block and a second reflectance related to the second image block based on the pixel information of the first image block, the pixel information of the second image block, the pixel information of multiple third image blocks, and the reflectance information of multiple color blocks.

[0120] The reflectivity determination module 540 is used to determine the water-free reflectivity based on the first reflectivity and the second reflectivity.

[0121] According to embodiments of the present disclosure, the image processing module 530 includes a first processing submodule, a second processing submodule, a third processing submodule, a fourth processing submodule, and a fifth processing submodule.

[0122] The first processing submodule is used to determine the target grayscale value of the target image block and the corresponding target grayscale value of each of the multiple color channels based on the grayscale value of each of the multiple pixels of the target image block in the multiple color channels.

[0123] The second processing submodule is used to obtain the pixel information of the target image block based on the target grayscale values ​​corresponding to each of the multiple color channels.

[0124] The third processing submodule is used to determine the fitting relationship between the pixel information of an image block and the reflectance information associated with the image block, based on the pixel information of multiple third image blocks and the reflectance information of multiple color blocks.

[0125] The fourth processing submodule is used to determine the first reflectance associated with the first image block based on the fitted relationship and the pixel information of the first image block.

[0126] The fifth processing submodule is used to determine the second reflectance associated with the second image patch based on the fitting relationship and the pixel information of the second image patch.

[0127] According to embodiments of this disclosure, the third processing submodule includes a first processing unit and a second processing unit.

[0128] The first processing unit is used to perform linear fitting by taking the natural logarithm of the pixel information of multiple third image patches as the independent variable and the natural logarithm of the reflectance information of multiple color patches as the dependent variable, and determining the fitting coefficients.

[0129] The second processing unit is used to determine the fitting relationship between the pixel information of the image patch and the reflectance information associated with the image patch based on the fitting coefficients.

[0130] According to embodiments of this disclosure, the image cropping module 520 includes a first cropping submodule, a second cropping submodule, a third cropping submodule, and a fourth cropping submodule.

[0131] The first cropping submodule is used to determine from the water surface image a first image region related to the water body, a second image region related to the mirror surface information of the reflector, and a third image region related to the color chart.

[0132] The second cropping submodule is used to crop the first image block from the first image region.

[0133] The third cropping submodule is used to crop the second image block from the second image region.

[0134] The fourth cropping submodule is used to crop multiple third image blocks from the third image region based on the color information of each of the multiple color blocks.

[0135] Figure 6 A block diagram of a water transparency determination apparatus according to an embodiment of the present disclosure is shown schematically.

[0136] like Figure 6 As shown, the water transparency determination device 600 may include a reflectance acquisition module 610 and a transparency determination module 620.

[0137] The reflectance acquisition module 610 is used to acquire the water reflectance of the target water body.

[0138] Transparency determination module 620 is used to obtain the transparency of the target water body based on the water reflectance of the target water body.

[0139] In the embodiments of this disclosure, the water reflectance of the target water body can be obtained using the water reflectance determination method described above, which will not be elaborated here.

[0140] According to embodiments of this disclosure, the turbidity, nutrient status, color, and cyanobacterial bloom status of a target water body can be determined based on its water reflectance.

[0141] In the embodiments of this disclosure, the water reflectance of the target water body can be obtained using the water reflectance determination method described above, which will not be elaborated here.

[0142] According to embodiments of this disclosure, water surface images can be obtained through the public's smartphones, enabling convenient water quality monitoring.

[0143] Those skilled in the art will understand that the features described in the various embodiments of this disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments of this disclosure can be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0144] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A method for determining water reflectivity, characterized in that, The method includes: Acquire a water surface image, the image content of which includes water, a color chart, and a reflector, the color chart containing multiple color blocks with different reflectivities; The water surface image is cropped to obtain a first image block related to the water body, a second image block related to the mirror surface information of the reflector, and multiple third image blocks related to the color chart; Based on the pixel information of the first image block, the pixel information of the second image block, the pixel information of the plurality of third image blocks, and the reflectance information of the plurality of color blocks, a first reflectance associated with the first image block and a second reflectance associated with the second image block are determined; The water-free reflectance is determined based on the first reflectance and the second reflectance.

2. The method according to claim 1, characterized in that, For any one of the first image block, the second image block, and the plurality of third image blocks, the method further includes: Based on the grayscale values ​​of multiple pixels of the target image block in multiple color channels, determine the target grayscale values ​​corresponding to the target image block and each of the multiple color channels; and The pixel information of the target image block is obtained based on the target grayscale values ​​corresponding to each of the multiple color channels.

3. The method according to claim 1, characterized in that, Based on the pixel information of the first image block, the pixel information of the second image block, the pixel information of multiple third image blocks, and the reflectance information of multiple color blocks, a first reflectance associated with the first image block and a second reflectance associated with the second image block are determined, including: Based on the pixel information of multiple third image blocks and the reflectance information of multiple color blocks, a fitting relationship is determined between the pixel information of the image blocks and the reflectance information associated with the image blocks; Based on the fitting relationship and the pixel information of the first image block, a first reflectance associated with the first image block is determined; Based on the fitting relationship and the pixel information of the second image block, a second reflectance associated with the second image block is determined.

4. The method according to claim 3, characterized in that, Based on the pixel information of multiple third image patches and the reflectance information of multiple color patches, a fitting relationship is determined between the pixel information of the image patches and the reflectance information associated with the image patches, including: The natural logarithms of the pixel information of multiple third image blocks are used as independent variables, and the natural logarithms of the reflectance information of multiple color blocks are used as dependent variables to perform linear fitting and determine the fitting coefficients. Based on the fitting coefficients, the fitting relationship between the pixel information of the image block and the reflectance information associated with the image block is determined.

5. The method according to claim 1, characterized in that, The water surface image is cropped to obtain a first image block related to the water body, a second image block related to the mirror surface information of the reflector, and multiple third image blocks related to the color chart, including: From the water surface image, determine a first image region related to the water body, a second image region related to the mirror information of the reflector, and a third image region related to the color chart; The first image block is obtained by cropping from the first image region; The second image block is obtained by cropping from the second image region; Based on the color information of each of the multiple color blocks, multiple third image blocks are cropped from the third image region.

6. The method according to claim 1, characterized in that, The mirror information includes the sky image information reflected by the reflector.

7. The method according to claim 1, characterized in that, The reflectance information of the multiple color patches includes multiple reflectance values ​​of the color patches associated with the multiple color channels.

8. A method for determining water transparency, characterized in that, The method includes: The transparency of the target water body is determined based on its water-free reflectance. The water reflectance of the target water body is determined according to the method described in any one of claims 1 to 7.

9. A device for determining water reflectivity, characterized in that, The device includes: The image acquisition module is used to acquire a water surface image. The image content of the water surface image includes water, a color chart, and a reflector. The color chart contains multiple color blocks with different reflectivities. The image cropping module is used to crop the water surface image to obtain a first image block related to the water body, a second image block related to the mirror information of the reflector, and multiple third image blocks related to the color card. An image processing module is configured to determine a first reflectance associated with the first image block and a second reflectance associated with the second image block based on pixel information of the first image block, pixel information of the second image block, pixel information of a plurality of third image blocks, and reflectance information of a plurality of color blocks; A reflectivity determination module is used to determine the water-free reflectivity based on the first reflectivity and the second reflectivity.

10. A device for determining water transparency, characterized in that, The device includes: The transparency determination module is used to determine the transparency of the target water body based on its water reflectance. The water reflectance of the target water body is determined according to the method described in any one of claims 1 to 7.

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