CN114859352A - SAR satellite ocean observation image self-adaptive stretching method and device - Google Patents
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Abstract
The embodiment of the invention discloses a method and a device for self-adaptive stretching of an SAR satellite marine observation image. The method comprises the following steps: acquiring an SAR image; preprocessing the SAR image to obtain a preprocessing result; performing standard deviation stretching on the pretreatment result to obtain a stretching result; fitting gamma distribution of the image gray value of the stretching result to obtain a fitting result; calculating the standard deviation multiple corresponding to the minimum error to obtain the optimal standard deviation multiple; and outputting the optimal standard deviation multiple and the stretching result. By implementing the method provided by the embodiment of the invention, the interference of the target bright point in the sea surface SAR image can be eliminated without manually modifying the stretching parameter, the optimal effect of sea surface texture is achieved, and the stripes of wind, waves and the like on the sea surface can be clearly displayed.
Description
Technical Field
The invention belongs to the technical field of geophysical measurement, and particularly relates to a method and a device for self-adaptive stretching of an SAR satellite marine observation image.
Background
Ocean element observation is the basis of ocean research, and has very important significance for understanding ocean environment, improving accuracy of ocean forecast, developing ocean resources and the like, SAR (Synthetic Aperture Radar) is an active earth observation system, can observe earth all day long and all weather without limitation of sunshine and weather conditions, and has certain penetration capacity, and satellite-borne SAR can realize multiband, multi-polarization, multi-view direction and multi-depression angle observation, and the characteristics enable the satellite-borne SAR to have unique advantages in ocean observation. The method can effectively acquire ocean basic elements such as a sea surface wind field, sea waves and a flow field, and can monitor sea surface targets such as ships and oil spilling.
The SAR image stretching is a key step in improving the display quality of the SAR image, can improve the contrast of the image, enriches the detail information of the image, inhibits the noise of the image, not only improves the overall visual effect of the image, but also is beneficial to subsequent image application, including sample establishment and artificial intelligence interpretation. Currently, contrast enhancement algorithms are mainly used for SAR image enhancement. The contrast enhancement algorithm can be divided into a spatial domain method and a frequency domain method, the prior art adaptively selects segmentation points of piecewise linear transformation based on histogram transformation and an EM algorithm, performs gray scale range compression on a background and an uninteresting region, and performs gray scale range stretching processing on an interested target region; the linear transformation is to process the pixels one by one in a segmentation manner within the image gray scale range, and is to transform the dynamic range of the brightness value of the original image into a specified range or the whole dynamic range according to a linear relation. In the practical operation, 2 brightness intervals are given, and a certain brightness interval [ a, b ] of an input image is mapped into a brightness interval [ c, d ] of an output image, namely, the gray level of each pixel of the image is linearly transformed according to a linear proportion, so that the visual effect of the image is improved; one type of linear transformation is standard deviation stretching, which is performed by taking a certain multiple of standard deviation as an extreme value range, mapping values exceeding the standard deviation range directly to the extreme value of the target pixel range, and linearly stretching the rest values to 0-255. However, in the stretching and enhancing method in the prior art, for the SAR images under the same satellite and the same polarization mode, the stretching parameters of the SAR images need to be manually selected, for the SAR images under different satellites and different polarization modes, the stretching parameters of the SAR images need to be manually modified, the interference of a target bright spot in the SAR images is not considered, the optimal effect of sea surface textures cannot be achieved, and the stripes of wind, waves and the like on the sea surface can be clearly displayed.
Therefore, it is necessary to design a new method, which eliminates the interference of the target bright point in the sea surface SAR image without manually modifying the stretching parameters, achieves the best effect of the sea surface texture, and can clearly display the stripes of the sea surface, such as wind, wave, etc.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method and a device for adaptively stretching an SAR satellite ocean observation image.
In order to achieve the purpose, the invention adopts the following technical scheme: the SAR satellite ocean observation image self-adaptive stretching method comprises the following steps:
acquiring an SAR image;
preprocessing the SAR image to obtain a preprocessing result;
performing standard deviation stretching on the pretreatment result to obtain a stretching result;
fitting gamma distribution of the image gray value of the stretching result to obtain a fitting result;
calculating the standard deviation multiple corresponding to the minimum error to obtain the optimal standard deviation multiple;
and outputting the optimal standard deviation multiple and the stretching result.
The further technical scheme is as follows: the SAR image is an SAR image of a high-resolution third-number sentinel and a sentinel first-number under different imaging modes and polarization modes.
The further technical scheme is as follows: the preprocessing the SAR image to obtain a preprocessing result comprises the following steps:
carrying out relative radiation correction and geometric correction on the SAR image to obtain a correction result;
and carrying out image cutting on the correction result to obtain a preprocessing result.
The further technical scheme is as follows: the standard deviation stretching the pretreatment result to obtain a stretching result comprises:
stretching the pre-treatment result by 0 to 15 times standard deviation to obtain a stretching result.
The further technical scheme is as follows: the stretching the pre-treatment result by 0 to 15 times standard deviation to obtain a stretched result comprises:
determining a standard image, calculating the mean value and the standard deviation of the preprocessing result, and setting a standard deviation range;
and comparing whether the pixel value of each pixel in the preprocessing result is in a standard deviation range, setting the pixel value smaller than the minimum value of the standard deviation range as 0, setting the pixel value larger than the maximum value of the standard deviation range as 255, and stretching the rest values between 0 and 255 to obtain a stretching result.
The further technical scheme is as follows: the fitting of the gamma distribution of the image gray values of the stretching result to obtain a fitting result comprises:
fitting the gray values of the standard image and the stretching result by gamma distribution respectively to obtain a fitting result;
and calculating a threshold of a 99.9% integral domain of a standard image fitting result, and removing the interference of the target bright point.
The further technical scheme is as follows: the calculating the standard deviation multiple corresponding to the minimum error to obtain the optimal standard deviation multiple includes:
and calculating the square error of the fitting result of the standard image and the stretched image between 0 and 99.9 percent of the integral domain threshold value of the fitting result of the standard image, and taking the standard deviation stretching multiple with the minimum square error as the optimal standard deviation multiple.
The invention also provides a self-adaptive stretching device for the SAR satellite marine observation image, which comprises the following components:
an image acquisition unit for acquiring an SAR image;
the preprocessing unit is used for preprocessing the SAR image to obtain a preprocessing result;
the stretching unit is used for stretching the pretreatment result in a standard deviation manner to obtain a stretching result;
the fitting unit is used for fitting gamma distribution of the image gray value of the stretching result to obtain a fitting result;
the calculating unit is used for calculating the standard deviation multiple corresponding to the minimum error so as to obtain the optimal standard deviation multiple;
and the output unit is used for outputting the optimal standard deviation multiple and the stretching result.
The invention also provides computer equipment which comprises a memory and a processor, wherein the memory is stored with a computer program, and the processor realizes the method when executing the computer program.
The invention also provides a storage medium storing a computer program which, when executed by a processor, implements the method described above.
Compared with the prior art, the invention has the beneficial effects that: according to the method, after the SAR image is obtained and preprocessed, the preprocessed result is stretched by adopting a standard deviation stretching algorithm, gamma distribution is used for fitting the standard image and the gray value of the stretched result, the threshold value of 99.9% integral domain of the fitting result of the standard image is calculated, the interference of a target bright spot is removed, the standard deviation multiple corresponding to the minimum error is calculated as the optimal standard deviation multiple, the stretching parameter is not required to be manually modified, the interference of the target bright spot in the SAR image on the sea surface is removed, the optimal effect of sea surface texture is achieved, and the stripes such as wind, waves and the like on the sea surface can be clearly displayed.
The invention is further described below with reference to the accompanying drawings and specific embodiments.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic view of an application scenario of an adaptive stretching method for an SAR satellite marine observation image according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for adaptively stretching an SAR satellite marine observation image according to an embodiment of the present invention;
fig. 3 is a schematic sub-flow diagram of an adaptive stretching method for an SAR satellite marine observation image according to an embodiment of the present invention;
fig. 4 is a schematic sub-flow diagram of an adaptive stretching method for an SAR satellite marine observation image according to an embodiment of the present invention;
fig. 5 is a schematic sub-flow diagram of an adaptive stretching method for an SAR satellite marine observation image according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of gamma distribution fitting to gray scale values of a standard image according to an embodiment of the present invention;
FIG. 7 is a diagram of a standard image provided by an embodiment of the present invention;
fig. 8 is a schematic diagram of a GF3 HH polarization mode standard deviation stretched SAR image provided by an embodiment of the present invention;
FIG. 9 is a schematic diagram of a 4.5 standard deviation stretch of a GF3 HH polarization mode provided by an embodiment of the present invention;
fig. 10 is a schematic diagram of a 4-fold standard deviation stretch of a GF3 HH polarization mode according to an embodiment of the present invention;
FIG. 11 is a schematic diagram of a 5 standard deviation stretch of a GF3 HH polarization mode provided by an embodiment of the present invention;
fig. 12 is a schematic diagram of a 3-fold standard deviation stretch of GF3 HH polarization mode provided by an embodiment of the present invention;
fig. 13 is a schematic diagram of a GF3 HH polarization mode 6 times standard deviation stretch according to an embodiment of the present invention;
FIG. 14 is a graph of gray scale values of gamma distribution fitting images of GF3 HH polarization mode according to an embodiment of the present invention;
FIG. 15 is a graph showing the error of the standard deviation stretch of GF3 HH polarization mode with respect to the gamma distribution of a standard image for different multiples according to an embodiment of the present invention;
fig. 16 is a schematic diagram of fitting image gray values to GF3 HV polarization mode gamma distribution provided by an embodiment of the present invention;
FIG. 17 is a graph showing the error in standard deviation stretch and standard image gamma distributions for different multiples of GF3 HV polarization mode, according to an embodiment of the present invention;
fig. 18 is a schematic diagram of gamma distribution fitting image gray scale values of GF3 VV polarization mode according to an embodiment of the present invention;
FIG. 19 is a graph showing the error between the standard deviation stretch of GF3 VV polarization mode at different multiples and the gamma distribution of a standard image according to an embodiment of the present invention;
FIG. 20 is a schematic diagram of gray scale values of a sentinel model I HV polarization mode gamma distribution fit image provided by an embodiment of the present invention;
FIG. 21 is a schematic diagram of the error in the standard deviation stretch and standard image gamma distribution for different multiples of sentinel HV polarization mode according to an embodiment of the present invention;
FIG. 22 is a schematic diagram of fitting image gray values to a distribution of gamma of a sentinel number one VV polarization mode provided by an embodiment of the invention;
FIG. 23 is a schematic diagram of the error in the standard deviation stretch and the standard image gamma distribution for different multiples of the sentinel number one VV polarization mode provided by an embodiment of the present invention;
fig. 24 is a schematic block diagram of an adaptive stretching device for a SAR satellite marine observation image according to an embodiment of the present invention;
FIG. 25 is a schematic block diagram of a computer apparatus provided by an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1 and fig. 2, fig. 1 is a schematic view of an application scenario of an adaptive stretching method for an SAR satellite marine observation image according to an embodiment of the present invention. Fig. 2 is a schematic flow chart of a SAR satellite marine observation image adaptive stretching method provided by an embodiment of the present invention. The SAR satellite marine observation image self-adaptive stretching method is applied to a server. The server performs data interaction with a radar camera and a terminal, by preprocessing an SAR image input by the radar camera, combining standard deviation stretching and gamma distribution, fitting a standard image gray normalization histogram by using the gamma distribution, calculating a threshold of 99.9% integral domain, comparing gamma distribution errors of the standard image and images to be stretched of different stretching standard deviation multiples within the threshold, selecting the stretching multiple with the minimum error as an optimal stretching multiple parameter, realizing that the stretching parameters do not need to be manually modified, deleting the interference of a target bright point in a sea surface SAR image, achieving the best effect of sea surface texture, and clearly displaying sea surface wind, wave and other stripes.
Fig. 2 is a schematic flow chart of a method for adaptively stretching a SAR satellite marine observation image according to an embodiment of the present invention. As shown in fig. 2, the method includes the following steps S110 to S160.
And S110, acquiring the SAR image.
In this embodiment, the SAR image refers to a synthetic aperture radar image obtained by shooting the sea surface, and a radar camera may be used to shoot the SAR image.
Specifically, the SAR images are SAR images under different imaging modes and polarization modes of a high-resolution three-number mode and a sentinel one-number mode.
And S120, preprocessing the SAR image to obtain a preprocessing result.
In this embodiment, the preprocessing result refers to an image formed by preprocessing the SAR image such as rectification and cutting.
In an embodiment, referring to fig. 3, the step S120 may include steps S121 to S122.
S121, performing relative radiation correction and geometric correction on the SAR image to obtain a correction result.
In this embodiment, the correction result refers to an image formed after the SAR image is subjected to the relative radiation correction and the geometric correction.
And S122, carrying out image cutting on the correction result to obtain a preprocessing result.
In the present embodiment, image cutting is useful for determining an image that needs to be stretched.
S130, performing standard deviation stretching on the pretreatment result to obtain a stretching result.
In the present embodiment, the stretching result refers to a result formed after the standard deviation stretching is performed on the image.
In this example, the pretreatment result was subjected to 0 to 15 times standard deviation stretching to obtain a stretching result.
In an embodiment, referring to fig. 4, the step S130 may include steps S131 to S132.
S131, determining a standard image, calculating the mean value and the standard deviation of the preprocessing result, and setting a standard deviation range;
s132, comparing whether the pixel value of each pixel in the preprocessing result is in the standard deviation range, setting the pixel value smaller than the minimum value of the standard deviation range as 0, setting the pixel value larger than the maximum value of the standard deviation range as 255, and stretching the rest values between 0 and 255 to obtain a stretching result.
Selecting a sea surface SAR image with the best visual effect as a standard image, combining standard deviation stretching and gamma distribution, fitting a standard image gray normalization histogram by using the gamma distribution, calculating the threshold value of 99.9% integral domain of the standard image, comparing the gamma distribution error of the standard image and the image to be stretched with different stretching standard deviation multiples within the threshold value, and selecting the stretching multiple with the minimum error as an optimal stretching multiple parameter.
In this embodiment, the standard deviation stretching belongs to linear transformation, and the linear transformation refers to processing pixel by pixel in a segment within an image gray scale range, and transforms the dynamic range of the brightness value of the original image to a specified range or the whole dynamic range according to a linear relation (linear function). Given 2 luminance intervals in the actual operation, a certain luminance value interval [ a, b ] of the input image is mapped to a luminance value interval [ c, d ] of the output image. The gray scale of each pixel of the image is linearly transformed according to the linear proportion, and the visual effect of the image is improved.
One type of linear transformation is standard deviation stretching, which is performed by taking a certain multiple of standard deviation as an extreme value range, mapping values exceeding the standard deviation range directly to the extreme value of the target pixel range, and linearly stretching the rest values to 0-255. The standard deviation stretching operation process of the invention is as follows:
calculating the mean value and the standard deviation of the image, and setting a standard deviation range; max = avg + k × std; min = avg-k std; wherein min is the minimum value of the standard deviation range, max is the maximum value of the standard deviation range, avg is the mean value of the original image, std is the standard deviation of the original image, and k is the set standard deviation multiple (taking 0-15). And if the minimum value of the standard deviation range is less than 0, taking the minimum value of the standard deviation range as 0.
And comparing whether the pixel value of each pixel in the image is in the standard deviation range, setting the pixel value smaller than the minimum value of the standard deviation range as 0, and setting the pixel value larger than the maximum value of the standard deviation range as 255. The remaining values are stretched between 0 and 255 with the stretching formula j = (i-min)/(max-min) × 255; wherein i is the pixel value of a certain pixel in the original image, and j is the stretched pixel value.
And repeating the two steps, and respectively stretching the image to be stretched, namely the pretreatment result by 0-15 times of standard deviation.
And S140, fitting gamma distribution of the image gray value of the stretching result to obtain a fitting result.
In this embodiment, the fitting result refers to the image grayscale value of the fitting stretching result.
In an embodiment, referring to fig. 5, the step S140 may include steps S141 to S142.
And S141, fitting the standard image and the gray value of the stretching result by gamma distribution respectively to obtain a fitting result.
As shown in fig. 6, the Gamma distribution is a continuous probability function of statistics, and is a very important distribution in probability statistics. The probability density function of the Gamma distribution is
Wherein the content of the first and second substances,
,
, 
mu is the mean value of the gamma distribution,
is the variance of the gamma distribution.
And S142, calculating a threshold of a 99.9% integral domain of the standard image fitting result, and removing the interference of the target bright point.
And using one visual optimal image to automatically acquire the optimal stretching parameters of the other images to enable the optimal stretching parameters to be close to the optimal visual effect. The optimal visual image gray value is fitted through gamma distribution, the threshold value of 99.9% of the integral domain is calculated, the interference of the target bright point is deleted, the optimal effect of sea surface texture is achieved, and the stripes such as wind, waves and the like of the sea surface can be clearly displayed.
S150, calculating a standard deviation multiple corresponding to the minimum error to obtain an optimal standard deviation multiple;
in the present embodiment, the optimum standard deviation multiple refers to an optimum multiple of image stretching.
Specifically, the square error of the standard image and the stretched image fitting result between 0 and the threshold of the 99.9% integral domain of the standard image fitting result is calculated, and the standard deviation stretch multiple with the smallest square error is taken as the optimal standard deviation multiple.
The standard deviation stretching and gamma distribution are combined, so that the stretching parameter with the minimum error between the standard SAR image and the sea surface SAR image to be stretched is the optimal stretching parameter, the method is suitable for automatically stretching the sea surface SAR image in different polarization modes of sentinel No. one and high-resolution No. three, the parameters do not need to be manually modified, and the method is convenient and fast.
And S160, outputting the optimal standard deviation multiple and the stretching result.
Referring to fig. 7 to 13, in GF3 HH polarization mode, the best stretch ratio of the image is 4.5, the gamma distribution of the best stretch ratio is fit to the graph and the error between the standard deviation stretch of different ratios and the gamma distribution of the standard image is shown in fig. 14 and 15; the standard deviation stretch algorithm under GF3 HV polarization mode gave an optimal stretch standard deviation multiple of 6 for the image, and a gamma distribution fit plot of the optimal stretch multiple and the error between the standard deviation stretch at different multiples and the gamma distribution for the standard image are shown in fig. 16-17. Under GF3 VV polarization mode, the best stretch ratio of the image is 5.5, the gamma distribution of the best stretch ratio is fitted by standard deviation stretching algorithm, and the error between the standard deviation stretch of different times and the gamma distribution of the standard image is shown in figure 18 and figure 19. Under the sentinel number one HV polarization mode, the optimal stretching multiple of the image is 5 through a standard deviation stretching algorithm, and a gamma distribution fitting graph of the optimal stretching multiple and errors of the standard deviation stretching of different multiples and the gamma distribution of the standard image are shown in figures 20 to 21. Under the sentinel number one VV polarization mode, the optimal stretching multiple of the image is 2 by the standard deviation stretching algorithm, and the gamma distribution fitting graph of the optimal stretching multiple and the errors of the standard deviation stretching of different multiples and the gamma distribution of the standard image are shown in fig. 22 to fig. 23.
For stretching sea surface SAR images with different times of standard deviations in different polarization modes than the standard deviation of the third high-resolution image and the first sentinel, it can be found that the image obtained by the optimal stretching parameter calculated by the method of the embodiment is the closest to the standard image in the aspects of brightness and texture details.
According to the SAR satellite ocean observation image self-adaptive stretching method, after the SAR image is obtained and preprocessed, the preprocessed result is stretched by adopting a standard deviation stretching algorithm, gamma distribution is used for fitting the standard image and the gray value of the stretching result, the threshold value of 99.9% integral domain of the fitting result of the standard image is calculated, the interference of a target bright spot is removed, the standard deviation multiple corresponding to the minimum error is calculated as the optimal standard deviation multiple, the stretching parameter does not need to be manually modified, the interference of the target bright spot in the sea surface SAR image is removed, the optimal effect of sea surface texture is achieved, and the wind, wave and other stripes on the sea surface can be clearly displayed.
Fig. 24 is a schematic block diagram of an adaptive stretching device 300 for a SAR satellite marine observation image according to an embodiment of the present invention. As shown in fig. 24, the present invention further provides an adaptive stretching device 300 for the SAR satellite marine observation image, corresponding to the above adaptive stretching method for the SAR satellite marine observation image. The SAR satellite marine observation image adaptive stretching apparatus 300 includes a unit for performing the above-described SAR satellite marine observation image adaptive stretching method, and the apparatus may be configured in a server. Specifically, referring to fig. 24, the SAR satellite marine observation image adaptive stretching device 300 includes an image acquisition unit 301, a preprocessing unit 302, a stretching unit 303, a fitting unit 304, a calculation unit 305, and an output unit 306.
An image acquisition unit 301 configured to acquire an SAR image; a preprocessing unit 302, configured to preprocess the SAR image to obtain a preprocessing result; a stretching unit 303, configured to perform standard deviation stretching on the preprocessing result to obtain a stretching result; a fitting unit 304, configured to fit gamma distribution of the image gray values of the stretching result to obtain a fitting result; a calculating unit 305, configured to calculate a multiple of standard deviation corresponding to the minimum error to obtain an optimal multiple of standard deviation; an output unit 306, configured to output the optimal multiple of standard deviation and the stretching result.
In one embodiment, the preprocessing unit 302 includes a syndrome subunit and a cutting subunit.
The corrector subunit is used for carrying out relative radiation correction and geometric correction on the SAR image so as to obtain a correction result; and the cutting subunit is used for carrying out image cutting on the correction result to obtain a preprocessing result.
In an embodiment, the stretching unit 303 is configured to perform 0-fold to 15-fold standard deviation stretching on the pre-processing result to obtain a stretching result.
In one embodiment, the stretching unit 303 includes a calculating subunit and a comparing subunit.
The standard deviation calculating subunit is used for determining a standard image, calculating the mean value and the standard deviation of the preprocessing result, and setting a standard deviation range; and the comparison subunit is used for comparing whether the pixel value of each pixel in the preprocessing result is within the standard deviation range, setting the pixel value smaller than the minimum value of the standard deviation range as 0, setting the pixel value larger than the maximum value of the standard deviation range as 255, and stretching the rest values between 0 and 255 to obtain a stretching result.
In an embodiment, the fitting unit 304 includes a gray-scale value fitting subunit and a threshold value calculating subunit.
The gray value fitting subunit is used for respectively fitting the gray values of the standard image and the stretching result by gamma distribution to obtain a fitting result; and the threshold value operator unit is used for calculating the threshold value of the 99.9% integral domain of the standard image fitting result and removing the interference of the target bright point.
In an embodiment, the calculating unit 305 is configured to calculate a square error of the standard image and the stretched image fitting result between 0 and a threshold of a 99.9% integral domain of the standard image fitting result, and take a standard deviation stretch multiple with a smallest square error as an optimal standard deviation multiple.
It should be noted that, as can be clearly understood by those skilled in the art, for the specific implementation process of the adaptive stretching device 300 for SAR satellite marine observation images and each unit, reference may be made to the corresponding description in the foregoing method embodiment, and for convenience and brevity of description, no further description is provided herein.
The SAR satellite marine observation image adaptive stretching apparatus 300 described above may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 25.
Referring to fig. 25, fig. 25 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a server, wherein the server may be an independent server or a server cluster composed of a plurality of servers.
Referring to fig. 25, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 comprises program instructions that, when executed, cause the processor 502 to perform a SAR satellite marine observation image adaptive stretching method.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 may be enabled to execute an SAR satellite marine observation image adaptive stretching method.
The network interface 505 is used for network communication with other devices. Those skilled in the art will appreciate that the configuration shown in fig. 25 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation of the computer device 500 to which the present application may be applied, and that a particular computer device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
Wherein the processor 502 is configured to run the computer program 5032 stored in the memory to implement the following steps:
acquiring an SAR image; preprocessing the SAR image to obtain a preprocessing result; performing standard deviation stretching on the pretreatment result to obtain a stretching result; fitting gamma distribution of the image gray value of the stretching result to obtain a fitting result; calculating the standard deviation multiple corresponding to the minimum error to obtain the optimal standard deviation multiple; and outputting the optimal standard deviation multiple and the stretching result.
The SAR images are SAR images of a high resolution third model and a sentinel first model under different imaging modes and polarization modes.
In an embodiment, when the processor 502 implements the step of preprocessing the SAR image to obtain a preprocessing result, the following steps are specifically implemented:
carrying out relative radiation correction and geometric correction on the SAR image to obtain a correction result; and carrying out image cutting on the correction result to obtain a preprocessing result.
In an embodiment, when the processor 502 implements the step of performing standard deviation stretching on the preprocessing result to obtain a stretching result, the following steps are specifically implemented:
stretching the pre-treatment result by 0 to 15 times standard deviation to obtain a stretching result.
In an embodiment, when the processor 502 implements the step of performing 0-fold to 15-fold standard deviation stretching on the preprocessing result to obtain the stretching result, the following steps are specifically implemented:
determining a standard image, calculating the mean value and the standard deviation of the preprocessing result, and setting a standard deviation range; and comparing whether the pixel value of each pixel in the preprocessing result is in a standard deviation range, setting the pixel value smaller than the minimum value of the standard deviation range as 0, setting the pixel value larger than the maximum value of the standard deviation range as 255, and stretching the rest values between 0 and 255 to obtain a stretching result.
In an embodiment, when the step of obtaining the fitting result by the processor 502 implementing the gamma distribution of the image gray values fitting the stretching result is implemented, the following steps are implemented:
fitting the gray values of the standard image and the stretching result by gamma distribution respectively to obtain a fitting result; and calculating a threshold of a 99.9% integral domain of a standard image fitting result, and removing the interference of the target bright point.
In an embodiment, when the processor 502 implements the step of calculating the multiple of the standard deviation corresponding to the minimum error to obtain the optimal multiple of the standard deviation, the following steps are specifically implemented:
and calculating the square error of the fitting result of the standard image and the stretched image between 0 and the threshold value of the 99.9% integral domain of the fitting result of the standard image, and taking the standard deviation stretching multiple with the minimum square error as the optimal standard deviation multiple.
It should be understood that in the embodiment of the present Application, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It will be understood by those skilled in the art that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program instructing associated hardware. The computer program includes program instructions, and the computer program may be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
Accordingly, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program, when executed by a processor, causes the processor to perform the steps of:
acquiring an SAR image; preprocessing the SAR image to obtain a preprocessing result; performing standard deviation stretching on the pretreatment result to obtain a stretching result; fitting gamma distribution of the image gray value of the stretching result to obtain a fitting result; calculating the standard deviation multiple corresponding to the minimum error to obtain the optimal standard deviation multiple; and outputting the optimal standard deviation multiple and the stretching result.
The SAR images are SAR images of a high resolution third model and a sentinel first model under different imaging modes and polarization modes.
In an embodiment, when the processor executes the computer program to implement the step of preprocessing the SAR image to obtain a preprocessing result, the following steps are specifically implemented:
carrying out relative radiation correction and geometric correction on the SAR image to obtain a correction result; and carrying out image cutting on the correction result to obtain a preprocessing result.
In an embodiment, when the processor executes the computer program to implement the step of performing standard deviation stretching on the preprocessing result to obtain a stretching result, the following steps are specifically implemented:
stretching the pre-treatment result by 0 to 15 times standard deviation to obtain a stretching result.
In an embodiment, when the processor executes the computer program to perform the step of performing 0-fold to 15-fold standard deviation stretching on the pre-processing result to obtain a stretching result, the following steps are specifically implemented:
determining a standard image, calculating the mean value and the standard deviation of the preprocessing result, and setting a standard deviation range; and comparing whether the pixel value of each pixel in the preprocessing result is in the standard deviation range, setting the pixel value smaller than the minimum value of the standard deviation range as 0, setting the pixel value larger than the maximum value of the standard deviation range as 255, and stretching the rest values between 0 and 255 to obtain a stretching result.
In an embodiment, when the processor executes the computer program to implement the step of fitting the gamma distribution of the image gray values of the stretching result to obtain the fitting result, the following steps are specifically implemented:
fitting the gray values of the standard image and the stretching result by gamma distribution respectively to obtain a fitting result; and calculating a threshold of a 99.9% integral domain of a standard image fitting result, and removing the interference of the target bright point.
In an embodiment, when the processor executes the computer program to implement the step of calculating the multiple of the standard deviation corresponding to the minimum error to obtain the optimal multiple of the standard deviation, the following steps are specifically implemented:
and calculating the square error of the fitting result of the standard image and the stretched image between 0 and the threshold value of the 99.9% integral domain of the fitting result of the standard image, and taking the standard deviation stretching multiple with the minimum square error as the optimal standard deviation multiple.
The storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, which can store various computer readable storage media.
Those of ordinary skill in the art will appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the components and steps of the various examples have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.
The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be merged, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partly contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
- The SAR satellite ocean observation image self-adaptive stretching method is characterized by comprising the following steps:acquiring an SAR image;preprocessing the SAR image to obtain a preprocessing result;performing standard deviation stretching on the pretreatment result to obtain a stretching result;fitting gamma distribution of the image gray value of the stretching result to obtain a fitting result;calculating the standard deviation multiple corresponding to the minimum error to obtain the optimal standard deviation multiple;and outputting the optimal standard deviation multiple and the stretching result.
- 2. The SAR satellite ocean observation image adaptive stretching method according to claim 1, wherein the SAR image is a SAR image under different imaging modes and polarization modes of high resolution three and sentinel one.
- 3. The SAR satellite ocean observation image adaptive stretching method according to claim 1, wherein the preprocessing the SAR image to obtain a preprocessing result comprises:carrying out relative radiation correction and geometric correction on the SAR image to obtain a correction result;and carrying out image cutting on the correction result to obtain a preprocessing result.
- 4. The SAR satellite ocean observation image adaptive stretching method according to claim 1, wherein the stretching the preprocessing result by standard deviation to obtain a stretching result comprises:stretching the pre-treatment result by 0 to 15 times standard deviation to obtain a stretching result.
- 5. The SAR satellite ocean observation image adaptive stretching method according to claim 4, wherein the stretching the preprocessed result by 0-15 times standard deviation to obtain a stretched result comprises:determining a standard image, calculating the mean value and the standard deviation of the preprocessing result, and setting a standard deviation range;and comparing whether the pixel value of each pixel in the preprocessing result is in a standard deviation range, setting the pixel value smaller than the minimum value of the standard deviation range as 0, setting the pixel value larger than the maximum value of the standard deviation range as 255, and stretching the rest values between 0 and 255 to obtain a stretching result.
- 6. The SAR satellite ocean observation image adaptive stretching method according to claim 5, wherein the fitting of the gamma distribution of the image gray values of the stretching result to obtain the fitting result comprises:fitting the gray values of the standard image and the stretching result by gamma distribution respectively to obtain a fitting result;and calculating a threshold of a 99.9% integral domain of a standard image fitting result, and removing the interference of the target bright point.
- 7. The SAR satellite ocean observation image adaptive stretching method according to claim 6, wherein the calculating the standard deviation multiple corresponding to the minimum error to obtain the optimal standard deviation multiple comprises:and calculating the square error of the fitting result of the standard image and the stretched image between 0 and 99.9 percent of the integral domain threshold value of the fitting result of the standard image, and taking the standard deviation stretching multiple with the minimum square error as the optimal standard deviation multiple.
- SAR satellite ocean observation image self-adaptation stretching device which characterized in that includes:an image acquisition unit for acquiring an SAR image;the preprocessing unit is used for preprocessing the SAR image to obtain a preprocessing result;the stretching unit is used for stretching the pretreatment result in a standard deviation manner to obtain a stretching result;the fitting unit is used for fitting gamma distribution of the image gray value of the stretching result to obtain a fitting result;the calculating unit is used for calculating the standard deviation multiple corresponding to the minimum error so as to obtain the optimal standard deviation multiple;and the output unit is used for outputting the optimal standard deviation multiple and the stretching result.
- 9. A computer device, characterized in that the computer device comprises a memory, on which a computer program is stored, and a processor, which when executing the computer program implements the method according to any of claims 1 to 7.
- 10. A storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20010113535A (en) * | 2000-06-19 | 2001-12-28 | 추후제출 | Image processing method and apparatus, recording medium, and imaging apparatus |
| WO2017099346A1 (en) * | 2015-12-08 | 2017-06-15 | 한국해양과학기술원 | Method and apparatus for continuously detecting multi-satellite-based hazardous and noxious substance |
| CN109655148A (en) * | 2018-12-19 | 2019-04-19 | 南京世海声学科技有限公司 | A kind of autonomous extracting method of ship noise non-stationary low frequency spectrum lines |
| CN111611549A (en) * | 2020-05-25 | 2020-09-01 | 中国海洋大学 | Method for determining typhoon-influenced sea area design wave height based on marine environment parameters |
| CN112414309A (en) * | 2020-11-25 | 2021-02-26 | 北京交通大学 | High-speed rail contact line height-guiding and pull-out value inspection method based on airborne laser radar |
| CN114494894A (en) * | 2022-04-18 | 2022-05-13 | 中国海洋大学 | Ocean black vortex automatic identification and key parameter inversion method and device and electronic equipment |
-
2022
- 2022-07-07 CN CN202210794071.8A patent/CN114859352B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20010113535A (en) * | 2000-06-19 | 2001-12-28 | 추후제출 | Image processing method and apparatus, recording medium, and imaging apparatus |
| WO2017099346A1 (en) * | 2015-12-08 | 2017-06-15 | 한국해양과학기술원 | Method and apparatus for continuously detecting multi-satellite-based hazardous and noxious substance |
| CN109655148A (en) * | 2018-12-19 | 2019-04-19 | 南京世海声学科技有限公司 | A kind of autonomous extracting method of ship noise non-stationary low frequency spectrum lines |
| CN111611549A (en) * | 2020-05-25 | 2020-09-01 | 中国海洋大学 | Method for determining typhoon-influenced sea area design wave height based on marine environment parameters |
| CN112414309A (en) * | 2020-11-25 | 2021-02-26 | 北京交通大学 | High-speed rail contact line height-guiding and pull-out value inspection method based on airborne laser radar |
| CN114494894A (en) * | 2022-04-18 | 2022-05-13 | 中国海洋大学 | Ocean black vortex automatic identification and key parameter inversion method and device and electronic equipment |
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