KR101672291B1 - Water column correction using airborne hyperspectral image - Google Patents
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Abstract
Description
[0001] The present invention relates to a method for detecting a ridge root sound using aviation hyperspectral image, and more particularly, to a method for detecting a root-mean-square This paper deals with a method for effectively detecting clams, extending the coverage area of coastal seabed cladding, improving the range and accuracy of analysis, and detecting sparsely populated areas using aviation super-spectroscopic images.
In coastal ecosystems, algae are not only central to the material circulation as primary producers, but also play a role in enhancing secondary productivity as a habitat for various organisms including fishes and invertebrates. The coast is a place where the earth, ocean, and atmosphere interact with each other on the surface of the earth, and it is greatly affected by environmental change and climate change caused by development. Therefore, the need for monitoring coastal ecosystems is increasing, and baseline monitoring data is required for subsea coverage data.
The method for surveying the coastal seabed can be done by using various observation equipments installed on the ship and by field survey by diving survey. This is the most accurate method for diving surveys, but it is inefficient in scope and time of survey. The survey method by ship is more efficient than submergence survey in terms of scope and time. However, coastal shallow water depths, which are the main axis of coastal ecosystems, have limited accessibility to ships. In general, submergence survey and ship survey are based on sampling method.
On the other hand, the use of coastal images is limited due to the low spatial resolution of about 1 km for the cloud satellite images used for marine monitoring. Fortunately, remote sensing data with spatial resolutions suitable for coastal surveys, such as high resolution satellite images and aerial ultrasonic spectroscopic images, are being supplied. High-resolution images are effective for investigating coastal seabed environments, such as the distribution of seaweeds or coral that underlie marine ecosystems. However, due to the absorption of light by water, the same submarine coating and conditions exhibit different reflectivity in the image depending on the depth of water. Therefore, by normalizing the effect of water through the correction of depth of water, it is possible to analyze more submarine coating and extend the spatial range.
On the other hand, it is the phenomenon that the calcium carbonate (lime powder) which melts in the seawater precipitates and sticks to the bottom of the sea floor, the bottom of the sea floor and the rock in white. When calcium carbonate starts sticking to the floor or rock, the hydrogen ion concentration on the bottom surface is changed to strongly alkaline pH of about 9.5, so that algae living under neutral condition of
It is an object of the present invention to provide a water depth correction method for ultra-spectroscopic images in order to extend the coverage area of coastal seabed and to improve the accuracy.
It is another object of the present invention to provide a technique for accurately detecting a root sound using an aerial ultrasonic image.
In order to solve the above-mentioned problems, the embodiment of the present invention acquires an aerial ultrasonic spectroscopic image photographed in 96 bands having a width of 7.2 nm and a wavelength of 366.6 nm to 1048.5 nm at a photographing height of 2000 m, a spatial resolution of 1.5 m, and a wavelength of 366.6 nm to 1048.5 nm ; A preprocessing step of pre-processing the obtained aerial ultrasonic spectroscopic image; A water depth correction step of correcting the depth of the 40 bands of 402.6 nm to 682.8 nm of the preprocessed aerosol spectroscopic image; Detecting a dark spot using the aerosol-corrected aerosol-spectroscopic image, wherein the preprocessing step applies a radiation correction coefficient of the sensor to the raw image of the acquired aerosol spectroscopic image to reach the sensor for each pixel, A radiation correction step of acquiring a radiance image; An atmospheric correction step of obtaining a spectral reflectance image in an index by removing the influence of the atmosphere using the MODTRAN radiation transfer model to the radiant luminance image obtained in the radiation correction step; And a geometric correction step of registering the geographical coordinates using the Global Positioning System (GPS) data, the IMU (Inertial Measurement System) data, and the ground reference point survey data to the spectral reflectance image obtained in the atmospheric correction step, correction step, the equation r W = r T (λ i ) -Y (λ i) r T (λ IR) -R r (λ i) calculating a spectral reflectance r W of the water column (here, from the r T Y is the Angstrom exponent,? I is the visible light band,? IR is the near infrared band, and Rr is the Rayleigh reflectance); Obtaining a spectral reflectance R W (z) of a water body having a water depth of 1 m to 15 m from the aerial ultrasonic spectroscopic image to a point where the sea floor is sandy; Calculating an absorption coefficient K for each wavelength by using an exponential distribution regression model for the bands constituting the aerial ultrasonic image; ) And (R W (0) = R W from the step of (z) / exp (-Kz) calculating the spectral reflectance R W (0) of the sea floor in water (wherein, R W (z) is a spectral waterbody Wherein K is an absorption coefficient, and z is depth. The step of detecting the concave sound comprises the steps of: providing a spectral database storing spectral information about four coating classes of bright sand, dark sand, seaweed, And a step of classifying the aerial ultrasonic spectroscopic image with reference to the spectroscopic information stored in the spectroscopic database by using the maximum likelihood method to detect the dark spots.
According to an embodiment of the present invention, the effect of noise is reduced by minimizing the influence of the water depth by correcting the depth of field for the aerial ultrasonic image. Accordingly, it is possible to classify the coastal seabed effectively, and it is possible to improve the range and accuracy of the analysis.
In addition, according to an embodiment of the present invention, it is possible to accurately detect the consonant sound using the aerosol super-spectroscopic image corrected by the depth-of-field.
FIG. 1 is a diagram illustrating a combination of natural light colors of an aerial ultrasonic spectroscopic image used in the method of detecting a ridge number tone according to an embodiment of the present invention.
Fig. 2 is a graph showing a regression model of bands between water depth and water body reflectance for three bands.
FIG. 3 is a graph showing the R2 value for the regression model of the band between the water depth and the water body reflectance.
FIG. 4 is a result of applying the water depth correction method according to an embodiment of the present invention to FIG.
FIG. 5 is a graph showing the spectral reflectance according to the water depth after the water depth correction for the three bands exemplified in FIG.
6 is a graph showing a coefficient of variance of the sand reflectance for 40 bands of 402.6 nm-682.8 nm wavelength region.
Fig. 7 (a) shows the result of submarine coating classification for the image before the water depth correction, and Fig. 7 (b) shows the results of the submarine coating classification for the image after the water depth correction.
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the drawings, the same reference numerals are used to designate the same or similar components throughout the drawings. In the following description of the present invention, detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present disclosure rather unclear.
The method of detecting a root-mean-square sound using the aerial ultrasonic spectroscopic image according to an embodiment of the present invention includes the steps of obtaining an aerial ultrasonic spectroscopic image, a pre-processing step of pre-processing the obtained aerial ultrasonic spectroscopic image, A step of correcting the depth of the water, and a step of detecting the deep sound using the aerosol super-spectral image corrected by the depth. Each step will be described in detail below.
Acquiring the aerial ultrasonic image:
The ultrasound image can be defined as an image composed of several tens to several hundred continuous bands having a narrow band width. The ultrasound image has the advantage that the spectral reflection characteristics of the surface can be observed every pixel as compared with the multispectral image having fewer than 10 bands. These advantages are advantageous for classifying more diverse indicators or quantitatively analyzing specific factors.
FIG. 1 is a diagram showing a natural light color combination (RGB = 650
(used for water column correction)
(402.6 nm-682.8 nm)
(used for water column correction)
(40)
In the corresponding aerial ultrasonic image, only 40 bands (bands 6 to 45) having a wavelength of 402.6 nm to 682.8 nm corresponding to the visible light region can be used after the preprocessing step after the preprocessing step among the 96 bands. The reason for this is that the bands (
* Pretreatment step:
The preprocessing step for the superspectral image may include a radiation correction step, an atmospheric correction step, and a geometric correction step.
The radiation correction step is a process of acquiring an at-sensor radiance image that reaches the sensor for each pixel by applying the sensor radiation correction coefficient and system information to the original image of the acquired aerosol image.
In the atmospheric correction step, the spectral reflectance (radiation brightness / incident illuminance) in the surface is obtained by removing the influence of the atmosphere by applying geometric information of the solar-surface-sensor to the radiant luminance image obtained in the radiation correction step, . In the case of a coastal observation image, the energy (incident light intensity) incident on the water surface through the atmosphere is partially reflected at the surface of the water, and energy transmitted through the water surface is reflected by the water surface and reaches the sensor. Therefore, it is necessary to remove both the influence of the atmosphere and the energy reflected from the water (radiation brightness) to obtain the reflectance of the waterbody and the sea floor. In the preprocessing process, the effect of the atmosphere was corrected using a general radiative transfer model (MODTRAN).
The geometric correction step is a process of registering the geographical coordinates on the spectral reflectance image using GPS (Global Positioning System) / IMU (Inertial Measurement System) data and ground reference point survey data.
* Depth correction step:
Depth data used for depth correction can be obtained by a variety of methods, which in this example were obtained from CZMIL (Optech Inc., Canada). The photographed altitude was 400m and the laser density was 1.5 point / m 2 . CZMIL airborne water depth lidar calculates the depth of water by using the time difference that laser pulses of two wavelengths of near infrared and green light are scattered back to the sensor from the sea surface and the sea floor.
First, to correct the water depth, a step of calculating the spectral reflectance R W of the water body is performed. The reflectance (R W ) of the water body (sea water) can be defined as shown in Equation (1) and includes a signal reflected from the seabed when the water depth is shallow.
Where L W is the water-leaving radiance and E d is the downwelling irradiance. However, the total radiance (L T ) observed by an actual remote sensing sensor includes all the energy reflected from the water body, the water surface, and the atmosphere. In the preprocessing step, the sensor arrival reflectance (R T ) can be obtained through the atmospheric correction step, which includes the reflectance of the water body and the water surface. The reflectance of the water surface varies depending on the geometry of the solar-water surface-sensor, and has a higher reflectance than that of the waterbody. Therefore, the reflectivity of the water contained in the energy reaching the sensor is a cause of distortion or error in analyzing the characteristics of seawater or seabed. Therefore, correction of sea surface reflectance is necessary to analyze seawater and seabed.
Equation (2) shows a method of correcting the reflectance of the water surface to obtain the reflectance of the water body at the sensor arrival reflectance. This method assumes that reflectance of the water body in the near-infrared is close to zero, so that reflectance of the near-infrared reflects from the water surface. Therefore, it is possible to obtain the reflectance of the water body by estimating the water surface reflectance of each wavelength using the reflectance of near infrared rays and further removing the atmospheric effect (Rayleigh reflectance) thereof.
&Quot; (2) "
R W = R T (λ i ) -Y (λ i ) R T (λ IR ) -R r (λ i )
Here, R T denotes a sensor-arriving spectral reflectance, Y denotes an Angstrom exponent, λ i denotes a visible light band, λ IR denotes a near-infrared band, and R r denotes a Rayleigh reflectance.
The electromagnetic energy is absorbed exponentially according to the depth of water as shown in Equation (3).
&Quot; (3) "
L (z) = L (0) exp (-Kz)
Where K is the attenuation coefficient and z is depth.
The relationship between the water depth and the sea floor reflectance (albedo) and the reflectance (R W ) of the water body is expressed by the following equation (4).
&Quot; (4) "
R W = (A d -R ∞ ) exp (-gz) + R ∞
Where R ∞ is the reflectance of the water column at a very deep water depth where the reflectance of the seabed does not influence, A d is the reflectance (albedo) of the sea floor, z is the depth of water, g is the scattering absorption coefficient attenuation coefficient. Therefore, the reflectance of the ocean floor can be inversely calculated using Equation (4). However, this method has some limitations in applying to the actual coastal waters. First, it is necessary to assume that the captured image should include a point having a very deep depth where the reflectance of the seabed does not influence. However, this point may not be included in the image of the coast. Second, the scattering absorption coefficient g of light is obtained from experiments in very clean ocean waters such as the South Pacific. Therefore, it is possible to acquire the reflectance of the seabed when the image is acquired with shallow coastal depth.
Therefore, in this embodiment, the water depth correction is defined as the correction by the reflectance at the water surface having the water depth of 0 m, on the assumption that the bottom surface of the same covered sea floor has the same reflectance regardless of the water depth. Therefore, it is possible to estimate the absorption coefficient (K) for each wavelength through the regression model of the depth and reflectance after obtaining the reflectance for the bottom surface of the same cover and condition at various depths, The reflectance of the seabed surface in the water surface can be calculated by transforming together.
&Quot; (5) "
R W (0) = R W (z) / exp (-K z)
In this embodiment, in order to calculate the absorption coefficient K for each wavelength, the reflectance R W (z) of a water body having a water depth of 1 m to 15 m for 24 points in sand with less variation in the coating was obtained from the CASI-1500 image . The point where the sea floor was sand was judged based on the roughness of the bottom surface and the brightness of the image. Thereafter, the absorption coefficient K calculated through the exponential distribution regression model for each band is substituted into Equation (5) as shown in the example of the three bands in Fig. 2, so that the reflectance on the sea floor is corrected to reflectivity on the water surface .
FIG. 3 shows R 2 values for the regression model of the band between the water depth and the water body reflectance. Here, the R 2 value is relatively low as the wavelength is shorter in a 400 nm to 500 nm wavelength region corresponding to a blue light region. This can be attributed to the increased underwater scattering as the wavelength is shorter.
FIG. 4 is a result of applying the water depth correction method according to an embodiment of the present invention to FIG. 4 (a) shows a spectral reflectance image of a water body corrected for the effects of atmosphere and water surface, and FIG. 4 (b) shows an image subjected to a water depth correction step. As shown in FIG. 4 (a), the sea floor can be seen only in a very shallow range (6-7 m) before the water depth correction, but the sea floor appears to be sharply sharper as the water depth becomes deeper. As shown in FIG. 4 (b), the image corrected to the depth of the water has a relatively deep depth (up to 15 m) compared with the images of FIGS. 1 and 4 (a) Can be seen. FIG. 5 shows the spectral reflectance according to the water depth after the water depth correction for the three bands exemplified in FIG. As shown in FIG. 5, similar reflectance is shown even after changing the depth of the water after the water depth correction.
FIG. 6 shows the coefficient of variance of the sand reflectance for 40 bands of 402.6 nm-682.8 nm wavelength region. The coefficient of variation (standard deviation / average) is a coefficient for describing the distribution of samples when it is difficult to explain the distribution of samples only by means of the mean and the standard deviation. The closer to 0, the smaller the variation of the value. As shown in FIG. 6, it can be seen that the coefficient of variation after the water depth correction is greatly reduced, which can be interpreted that the variation of the reflectance along the water depth is greatly reduced. Particularly, it can be seen that the decreasing width of the variation coefficient becomes larger toward the long wavelength (red light) region after the water depth correction. This is because, as the wavelength becomes longer, the intensity of the signal observed by the sensor becomes weaker and the influence of the noise becomes larger as the absorption of light by the water increases. However, the effect of noise is reduced by minimizing the effect of depth by correction of depth of water.
When the water depth correction for the aerial ultrasonic image is completed, it is possible to detect the darkness using this. As the ultrasound image is presented, a new definition is needed compared with the image classification that was previously covered in the multispectral image. It is possible to detect (target) or identify (material) only specific objects using aerial ultrasonic images. The classification is to define the classification level for each pixel, and object detection is the process of finding an object already known by the analyst and defines the object for each pixel of the image.
The spectroscopic database needs to be prepared proactively in order to detect the dark spots using the aerosol corrected spectroscopic images. As the grade of covering the sea floor, four grades of bright sand, dark sand, seaweed (bedrock), and pebble (white bedrock) can be defined. The spectral database stores spectral information for these four classes of coatings.
The spectroscopic database is provided, and when the depth correction of the aerosol superimposed spectroscopic image is completed, the spectroscopic information stored in the spectroscopic database can be referred to to classify the aerosol spectroscopic image to detect the dark spots. At this time, maximum likelihood can be used as a classification algorithm.
Figure 7 shows the results of submarine cover classification for the images before and after the water depth correction. When the classification result is confirmed by visual observation, it can be seen that the sand is classified as rock or the rock is not clearly classified in the deep water area (right side of the drawing) in the case of the image before the water depth correction. On the contrary, after the correction of the water depth, the phenomenon that the sand is classified as the rock by the water depth is reduced, and the boundary between the rocks where the seaweeds are distributed and the rocks where the whitening phenomenon occurs are more clearly seen. The classification accuracy was improved from 84% before the water depth correction to 97% after the water depth correction, and 13% p improvement of the classification accuracy was obtained when the water depth correction was performed. According to classification level, the classification accuracy of 22% p and 9% p improvement in dark sand and sea bed rock distribution was improved respectively. Therefore, it is possible to classify the coastal subsoil cover by the correction of the depth of water, and the range and accuracy of the analysis can be improved.
It will be apparent to those skilled in the art that various modifications, additions and substitutions are possible, without departing from the scope and spirit of the invention as disclosed in the accompanying claims. . Therefore, the present invention is not intended to limit the scope of the present invention but to limit the scope of the technical idea of the present invention. The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents should be construed as falling within the scope of the present invention.
Claims (1)
A preprocessing step of pre-processing the obtained aerial ultrasonic image;
A depth correction step of correcting the depth of the preprocessed aerosol spectroscopic image;
And detecting the dark sound using the aerosol-corrected spectroscopic image,
The pre-
A radiative correction step of acquiring a radiance image arriving at the sensor for each pixel by applying a radiative correction coefficient of the sensor to the raw image of the acquired aerial ultrasonic spectral image;
An atmospheric correction step of obtaining a spectral reflectance image in an index by removing the influence of the atmosphere using the MODTRAN radiation transfer model to the radiant luminance image obtained in the radiation correction step; And
And a geometric correction step of registering the geographical coordinates using the Global Positioning System (GPS) data, the IMU (Inertial Measurement System) data, and the ground reference point survey data to the spectral reflectance image obtained in the atmospheric correction step,
In the water depth correction step,
Equation R W = R T (λ i ) -Y (λ i) R T (λ IR) -R r calculating a spectral reflectance R W of the water column from the (λ i) (where, R T is the spectral sensor is reached Y is the Angstrom exponent,? I is the visible light band,? IR is the near infrared band, and Rr is the Rayleigh reflectance);
Obtaining a spectral reflectance R W (z) of a water body having a water depth of 1 m to 15 m from the aerial ultrasonic spectroscopic image to a point where the sea floor is sandy;
Calculating an absorption coefficient K for each wavelength by using an exponential distribution regression model for the bands constituting the aerial ultrasonic image; And
Equation R W (0) = R W from the step of (z) / exp (-Kz) calculating the spectral reflectance R W (0) of the sea floor in water (wherein, R W (z) is the spectral reflectance of a water body , K is the absorption coefficient, and z is the depth)
The method of claim 1,
Providing a spectroscopic database in which spectroscopic information about four coating classes of bright sand, dark sand, seaweed, and dark green is stored;
And a step of classifying the aerial ultrasonic spectroscopic image by referring to the spectroscopic information stored in the spectroscopic database by using the maximum likelihood method to detect the dark spots.
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