CN111553852B - Method and device for generating optical remote sensing image fast view - Google Patents

Abstract

This application discloses a method and device for generating a snapshot of an optical remote sensing image. The method includes: resampling the optical remote sensing image to be processed to obtain sampled pixels, and determining the characteristic information of the sampled pixels; The characteristic information is linearly stretched to obtain the enhanced first characteristic information; the characteristic parameters of the pixels in each band are calculated according to the first characteristic information, and it is judged whether the characteristic parameters are less than a preset first threshold ; If less than, perform gamma stretching on the first feature information to obtain enhanced second feature information, and generate a quick view based on the second feature information. This application solves the technical problem of poor adaptability of the optical remote sensing image snapshot generated by the existing technology.

Description

A method and device for generating a quick view of optical remote sensing images

Technical field

The present application relates to the field of optical remote sensing technology, and in particular to a method and device for generating a snapshot of an optical remote sensing image.

Background technique

The size and data volume of optical remote sensing image snapshots (or browsing maps) are much smaller than the image itself. Generally, snapshots are mainly used for image selection, rapid distribution and emergency use of images. Through the quick view, users can initially identify the image coverage area and evaluate the quality and cloud cover of the image itself, thereby enabling preliminary optimization of images. In order to facilitate users to initially select images through the quick view, the quick view is generally required to have clear ground objects, moderate contrast, and easy interpretation. Generally, the number of quantization bits for single-band pixel values in optical remote sensing images is generally 10-12 bits, that is, the value range is 0-1023/2047/4095, while the number of quantization bits for fast view remote sensing images is 8 bits (0-255 ), therefore, in the process of generating a quick view of an optical remote sensing image, the optical remote sensing image needs to be downgraded and enhanced.

Currently, in the process of generating fast views of optical remote sensing images, a single image reduction and enhancement algorithm is often used. For example, a single image enhancement algorithm includes a linear enhancement algorithm or a gamma enhancement algorithm. Due to the different ground objects, lighting, and weather conditions during the optical remote sensing image imaging process,. Therefore, in the snapshots generated by the existing technology, especially when there are bright objects such as clouds and snow on the image, some images may have clear sky areas that are too dark, overexposed, or have color distortion, which affects the quality of the snapshots of optical remote sensing images. It is unstable, which leads to poor applicability of the generated optical remote sensing image snapshot.

Contents of the invention

The technical problem solved by this application is: Aiming at the problem of poor adaptability of optical remote sensing image snapshots generated by the existing technology, a method and device for generating optical remote sensing image snapshots are provided. The solution provided by the embodiments of this application , by linearly stretching the feature information of the sampled pixels, and performing gamma enhanced stretching on the stretched pixel feature information according to the actual situation of the linearly stretched pixel feature information, to avoid single enhanced stretching The resulting optical remote sensing image snapshot has stable quality, thereby improving the applicability of the optical remote sensing image snapshot.

In a first aspect, embodiments of the present application provide a method for generating a snapshot of an optical remote sensing image. The method includes:

Resample the optical remote sensing image to be processed to obtain the sampled pixels, and determine the characteristic information of the sampled pixels;

Linearly stretch the feature information to obtain enhanced first feature information;

Calculate the characteristic parameters of the pixels in each band according to the first characteristic information, and determine whether the characteristic parameters are less than a preset first threshold;

If it is less than, gamma stretching is performed on the first feature information to obtain enhanced second feature information, and a quick view is generated based on the second feature information.

In the solution provided by the embodiment of the present application, the characteristic information of the sampled pixels is obtained by sampling the optical remote sensing image to be processed, linearly stretching the characteristic information to obtain the first characteristic information, and based on the first characteristic information The characteristic parameters of the pixels in each band are calculated. If the characteristic parameters are less than the preset first threshold, the first characteristic information is gamma stretched to obtain the enhanced second characteristic information, and a quick response is generated based on the second characteristic information. view. Therefore, the solution provided by the embodiments of the present application linearly stretches the feature information of the sampled pixels, and performs gamma on the stretched pixel feature information according to the actual situation of the linearly stretched pixel feature information. Horse enhanced stretching avoids the stable quality of optical remote sensing image quick views caused by single enhanced stretching, thereby improving the applicability of optical remote sensing image quick views.

Optionally, resample the optical remote sensing image to be processed to obtain sampled pixels, including:

Determine the band information and size information of the optical remote sensing image to be processed, and determine at least one band for generating a quick view based on the band information;

The pixels of the at least one band are extracted, a sampling ratio is determined based on the size information, and the pixels of the at least one band are down-sampled according to the sampling ratio to obtain the sampled pixels.

Optionally, determining at least one band based on the band information includes:

If the band information is a panchromatic band, the at least one band is the panchromatic band; or if the band information is multiple bands, the at least one band is red, green, and blue.

Optionally, determining the sampling ratio based on the size information includes:

Determine the sampling ratio according to the following formula:

Where, n represents the number of pixels of the optical remote sensing image to be processed; imgh represents the number of rows of pixels of the optical remote sensing image to be processed; imgw represents the number of pixels of the optical remote sensing image to be processed. Rows.

Optionally, perform linear stretching on the feature information, including:

Calculate a cumulative histogram of the at least one band according to the characteristic information, and determine the gray value of the pixels at 2% and the gray value of the pixels at 99% of the cumulative histogram;

The feature information is linearly stretched according to the gray value of the pixel at 2% and the gray value of the pixel at 99%.

Optionally, linearly stretching the feature information according to the gray value of the pixel at 2% and the gray value of the pixel at 99% includes:

The feature information is linearly stretched according to the following formula:

Among them, g(x, y) represents the characteristic information at (x, y) in the image after stretching; f(x, y) represents the characteristic information at (x, y) in the image before stretching; min represents 2% The gray value of the pixel at 99%; max represents the gray value of the pixel at 99%.

Optionally, calculating the characteristic parameters of the pixels in each band according to the first characteristic information includes:

The number of pixels, the pixel value of each pixel, and the median are determined according to the first feature information, where the median refers to the pixel value of 50% of the pixels in the cumulative histogram. ;

Calculate the pixel mean value of each pixel according to the number of the pixels and the pixel value of each pixel;

The pixel mean square error of each pixel is calculated based on the pixel mean value of each pixel.

Optionally, perform gamma stretching on the first feature information to obtain enhanced second feature information, including:

The first feature information is gamma stretched according to the following formula:

Among them, γ represents the gamma stretching coefficient, medianv represents the median, t ₁ and t ₂ represent the preset second threshold; h(x, y) represents the second feature information of the pixel at (x, y).

In the solution provided by the embodiment of the present application, if the characteristic parameter of the linearly stretched pixel is less than the preset first threshold, the gamma stretching coefficient is adaptively determined through the actual information of the optical remote sensing image, and the gamma stretching coefficient is determined according to the gamma The gamma stretching coefficient performs gamma stretching on the feature information of the pixel. Therefore, in the solution provided by the embodiment of the present application, the actual information of the optical remote sensing image is used to adaptively determine the coefficient of gamma stretching, avoiding over-darkness or over-exposure during the stretching process, and ensuring the true color of the quick view. Improved the quality of quick views.

Optionally, before linearly stretching the feature information, it also includes:

The reflectivity of the top of the atmosphere of each band is calculated according to the characteristic information, and the characteristic information is preprocessed according to the reflectivity.

In a second aspect, embodiments of the present application provide a device for generating a snapshot of an optical remote sensing image, which device includes:

The sampling unit is used to resample the optical remote sensing image to be processed to obtain sampled pixels, and determine the characteristic information of the sampled pixels;

A stretching unit, used to linearly stretch the feature information to obtain enhanced first feature information;

A judging unit, configured to calculate the characteristic parameters of the pixels in each band according to the first characteristic information, and judge whether the characteristic parameters are less than a preset first threshold;

The generation unit, if less than the preset first threshold, performs gamma stretching on the first feature information to obtain enhanced second feature information, and generates a quick view based on the second feature information.

Optionally, the sampling unit is specifically used for:

Determine the band information and size information of the optical remote sensing image to be processed, and determine at least one band for generating a quick view based on the band information;

The pixels of the at least one band are extracted, a sampling ratio is determined based on the size information, and the pixels of the at least one band are down-sampled according to the sampling ratio to obtain the sampled pixels.

Optionally, the sampling unit is specifically used for:

If the band information is a panchromatic band, the at least one band is the panchromatic band; or if the band information is multiple bands, the at least one band is red, green, and blue.

Optionally, the sampling unit is specifically used to determine the sampling ratio according to the following formula:

Where, n represents the number of pixels of the optical remote sensing image to be processed; imgh represents the number of rows of pixels of the optical remote sensing image to be processed; imgw represents the number of pixels of the optical remote sensing image to be processed. Rows.

Optionally, the stretching unit is specifically used for:

Calculate a cumulative histogram of the at least one band according to the characteristic information, and determine the gray value of the pixels at 2% and the gray value of the pixels at 99% of the cumulative histogram;

The feature information is linearly stretched according to the gray value of the pixel at 2% and the gray value of the pixel at 99%.

Optionally, the stretching unit is specifically used to linearly stretch the feature information according to the following formula:

Among them, g(x, y) represents the characteristic information at (x, y) in the image after stretching; f(x, y) represents the characteristic information at (x, y) in the image before stretching; min represents 2% The gray value of the pixel at 99%; max represents the gray value of the pixel at 99%.

Optionally, the judgment unit is specifically used for:

The number of pixels, the pixel value of each pixel, and the median are determined according to the first feature information, where the median refers to the pixel value of 50% of the pixels in the cumulative histogram. ;

Calculate the pixel mean value of each pixel according to the number of the pixels and the pixel value of each pixel;

The pixel mean square error of each pixel is calculated based on the pixel mean value of each pixel.

Optionally, the generating unit is specifically configured to perform gamma stretching on the first feature information according to the following formula:

Among them, γ represents the gamma stretching coefficient, medianv represents the median, t ₁ and t ₂ represent the preset second threshold; h(x, y) represents the second feature information of the pixel at (x, y).

Optionally, the device further includes a computing unit; the computing unit is specifically used for:

The reflectivity of the top of the atmosphere of each band is calculated according to the characteristic information, and the characteristic information is preprocessed according to the reflectivity.

In a third aspect, this application provides a computer device, which includes:

memory for storing instructions executed by at least one processor;

A processor, configured to execute instructions stored in the memory to perform the method described in the first aspect.

In a fourth aspect, the present application provides a computer-readable storage medium that stores computer instructions. When the computer instructions are run on a computer, they cause the computer to execute the method described in the first aspect.

Description of the drawings

Figure 1 is a schematic flowchart of a method for generating a snapshot of an optical remote sensing image provided by an embodiment of the present application;

Figure 2 is a schematic structural diagram of a device for generating a snapshot of an optical remote sensing image provided by an embodiment of the present application;

Figure 3 is a schematic structural diagram of a device for generating a snapshot of an optical remote sensing image provided by an embodiment of the present application;

FIG. 4 is a schematic structural diagram of a computer device provided by an embodiment of the present application.

Detailed ways

In order to better understand the above technical solution, the technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are a detailed description of the technical solution of the present application, and This is not intended to limit the technical solution of the present application. If there is no conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

The following is a further detailed description of a method for generating a snapshot of an optical remote sensing image provided by the embodiment of the present application in conjunction with the accompanying drawings. The specific implementation of the method may include the following steps (the method flow is shown in Figure 1):

Step 101: Resample the optical remote sensing image to be processed to obtain sampled pixels, and determine the characteristic information of the sampled pixels.

Specifically, in the solution provided by the embodiment of the present application, the optical remote sensing camera acquires the optical remote sensing image to be processed, and the computer device performs down-sampling on the acquired optical remote sensing image to be processed. Specifically, there are many ways for computer equipment to down-sample optical remote sensing images to be processed. A preferred method will be described below as an example.

In a possible implementation, resampling the optical remote sensing image to be processed to obtain sampled pixels includes: determining the band information and size information of the optical remote sensing image to be processed, and determining based on the band information generate at least one band of the fast view; extract the pixels of the at least one band, determine the sampling ratio according to the size information, and down-sample the pixels of the at least one band according to the sampling ratio to obtain the The sampled pixel.

In the solution provided by the embodiment of this application, the band information of the optical remote sensing image to be processed includes a panchromatic band or multiple bands, where the panchromatic band generally refers to a single band of about 0.5 microns to 0.7 microns, that is, from green to the visible light band. Since there are multiple waveband information of optical remote sensing images, at least one waveband determined by the computer equipment for different waveband information is different. At least one band determined by different band information will be described below respectively.

In a possible implementation manner, determining at least one band based on the band information includes: if the band information is a panchromatic band, then the at least one band is the panchromatic band; or if the band information If it is multi-band, the at least one band is red, green and blue.

Further, in the solution provided by the embodiment of the present application, the size information of the optical remote sensing image to be processed includes the number of rows and columns of pixels and the number of pixels in the optical remote sensing image to be processed. After determining at least one band for generating the snapshot view, the computer device extracts feature information of at least one band. For example, the feature information includes the pixel value of the pixel and the position information of the pixel. The computer equipment performs down-resampling processing on the characteristic information of at least one band according to the size information of the optical remote sensing image. Specifically, there are many ways for the computer equipment to down-resample the characteristic information of at least one band. The following is a preferred method. The method is explained as an example.

In a possible implementation, determining the sampling ratio based on the size information includes:

Determine the sampling ratio according to the following formula:

Where, n represents the number of pixels of the optical remote sensing image to be processed; imgh represents the number of rows of pixels of the optical remote sensing image to be processed; imgw represents the number of pixels of the optical remote sensing image to be processed. Rows.

Step 102: Linearly stretch the feature information to obtain enhanced first feature information.

In the solution provided by the embodiment of the present application, after determining the characteristic information of each band, the computer device linearly stretches the characteristic information to obtain enhanced first characteristic information. Specifically, there are many ways for a computer device to linearly stretch the feature information. A preferred way will be described below as an example.

In one possible implementation, linearly stretching the feature information includes: calculating a cumulative histogram of the at least one band according to the feature information, and determining the gray value of 2% of the pixels in the cumulative histogram. degree value and the gray value of the pixel at 99%; the feature information is linearly stretched according to the gray value of the pixel at 2% and the gray value of the pixel at 99%.

In the solution provided by the embodiment of the present application, the gray value of each pixel and the number of sampled pixels are determined based on the characteristic information, and the sampled pixels are sequenced from small to large according to the gray value of the pixel. Sort the pixels to obtain the cumulative histogram, and then determine the gray value of the pixels at 2% and the gray value of the pixels at 99% of the cumulative histogram according to the number of sampled pixels. For example, if the cumulative The number of pixels in the histogram is 100, then the pixel at 2% refers to the second pixel in the cumulative histogram, and the pixel at 99% refers to the 99th pixel in the cumulative histogram, and then based on 2 The gray value of the pixel at % and the gray value of the pixel at 99% linearly stretch the feature information. Specifically, there are many ways for the computer device to linearly stretch the feature information based on the gray value of the pixels at 2% and the gray value of the pixels at 99%. The following is a better way. Take an example to illustrate.

In one possible implementation, the feature information is linearly stretched according to the gray value of the pixels at 2% and the gray value of the pixels at 99%, including:

The feature information is linearly stretched according to the following formula:

Among them, g(x, y) represents the characteristic information at (x, y) in the image after stretching; f(x, y) represents the characteristic information at (x, y) in the image before stretching; min represents 2% The gray value of the pixel at 99%; max represents the gray value of the pixel at 99%.

According to the above formula, it can be seen that the computer equipment is stretching the characteristic information of the sampled pixels. The specific process is as follows: cut out the first 2% and the last 1% of the pixel points in the cumulative image histogram, and then trim the cumulative histogram. The pixel value of the pixel point in the range of 2%-99% (floating point value range of 0-1) is linearly stretched in the range of 0-255.

Step 103: Calculate the characteristic parameters of the pixels in each band according to the first characteristic information, and determine whether the characteristic parameters are less than a preset first threshold.

In the solution provided by the embodiment of the present application, after determining the stretched first characteristic information, the computer device calculates the characteristic parameters of the pixels in each band according to the first characteristic information, where the characteristic parameters include the median , the pixel mean and mean square error of each pixel. Specifically, there are many ways for the computer device to calculate the characteristic parameters of the pixels in each band based on the first characteristic information. A preferred method is used as an example for description below.

In one possible implementation, calculating the characteristic parameters of the pixels in each band according to the first characteristic information includes: determining the number of pixels according to the first characteristic information, and the image of each pixel. element value, and the median, wherein the median refers to the pixel value of 50% of the pixels in the cumulative histogram; according to the number of the pixels and the pixel value of each pixel The pixel mean value of each pixel is calculated and obtained; and the pixel mean square error of each pixel is calculated according to the pixel mean value of each pixel.

Specifically, in the solution provided by the embodiment of the present application, calculating the pixel mean value of each pixel according to the number of the pixels and the pixel value of each pixel includes: according to the following The formula calculates the cell mean for each cell:

Among them, μ represents the mean value of the pixels in each band; m(x, y) represents the pixel value of the pixel at (x, y); p represents the number of the pixels.

Calculating the pixel mean square error of each pixel based on the pixel mean value of each pixel includes: calculating the pixel mean square error of each pixel according to the following formula:

Among them, σ represents the pixel mean square error of each pixel.

Furthermore, in the solution provided by the embodiment of the present application, the threshold value of the median, pixel mean value or pixel standard deviation is pre-stored in the database. For example, the threshold value of the sum of the pixel mean value and the pixel standard deviation is 128, The threshold for the median is 70. After calculating the median, pixel mean, or pixel standard deviation of the cumulative histogram, the computer device determines whether the sum of the median or pixel mean and the pixel standard deviation is less than a preset threshold.

Step 104: If it is less than, gamma stretch is performed on the first feature information to obtain enhanced second feature information, and a quick view is generated based on the second feature information.

In the solution provided by the embodiment of the present application, if the computer device determines that the characteristic parameter of the pixel in each band is less than the preset threshold, it performs gamma stretching on the first characteristic information to obtain the enhanced second characteristic information. Specifically, there are many ways for computer equipment to perform gamma stretching on the first feature information. A preferred method will be described below as an example.

In one possible implementation, performing gamma stretching on the first feature information to obtain enhanced second feature information includes: performing gamma stretching on the first feature information according to the following formula:

Among them, γ represents the gamma stretching coefficient, medianv represents the median, t ₁ and t ₂ represent the preset second threshold; h(x, y) represents the second feature information of the pixel at (x, y).

Further, in the solution provided by the embodiment of the present application, before step 104, the method also includes step 105. If the characteristic parameter of the pixel is greater than the preset first threshold, the snapshot view is generated according to the first characteristic information. .

Further, in order to reduce the color distortion of the image in the quick view. In the solution provided by the embodiment of the present application, before linearly stretching the characteristic information, the method further includes: calculating the reflectivity of the top of the atmosphere in each band according to the characteristic information, and calculating the reflectivity according to the reflectance. The feature information is preprocessed.

Specifically, in the solution provided by the embodiment of the present application, after determining the characteristic information of each band, the computer device determines the gray value of each sampled pixel based on the characteristic information, and then, according to the preset The radiation calibration coefficient parameters convert the gray value of each band pixel into radiance brightness (unit: Wm ^-1 .sr ^-1 .μm ^-1 ). For example, the radiation calibration coefficient parameters include radiation calibration coefficient gain, Offset etc. Specifically, the gray value is converted into radiance through the following formula:

L＝Gain×DN+Bisa

Among them, L represents the radiance of each pixel; Gain represents the radiation calibration coefficient gain; DN represents the gray value of the sampled pixel; Bisa represents the radiation calibration coefficient offset.

Furthermore, after calculating the radiance of each pixel, the computer equipment calculates the reflectivity of the top of the atmosphere in each band through the following formula:

Among them, B represents the reflectivity of the top of the atmosphere in each band; D represents the distance between the sun and the earth; ESUN represents the average solar spectral irradiance at the top of the atmosphere (unit is Wm ^-2 .sr ^-1 .μm ^-1 ); θ represents the sun’s Zenith angle.

In the solution provided by the embodiment of the present application, the characteristic information of the sampled pixels is obtained by sampling the optical remote sensing image to be processed, linearly stretching the characteristic information to obtain the first characteristic information, and based on the first characteristic information The characteristic parameters of the pixels in each band are calculated. If the characteristic parameters are less than the preset first threshold, the first characteristic information is gamma stretched to obtain the enhanced second characteristic information, and a quick response is generated based on the second characteristic information. view. Therefore, the solution provided by the embodiments of the present application linearly stretches the feature information of the sampled pixels, and performs gamma on the stretched pixel feature information according to the actual situation of the linearly stretched pixel feature information. Horse enhanced stretching avoids the stable quality of optical remote sensing image quick views caused by single enhanced stretching, thereby improving the applicability of optical remote sensing image quick views.

Based on the same inventive concept as the method shown in Figure 1, embodiments of the present application provide a device for generating a snapshot of an optical remote sensing image. See Figure 2. The device includes:

The sampling unit 201 is used to resample the optical remote sensing image to be processed to obtain sampled pixels, and determine the characteristic information of the sampled pixels;

The stretching unit 202 is used to linearly stretch the feature information to obtain enhanced first feature information;

The judging unit 203 is configured to calculate the characteristic parameters of the pixels in each band according to the first characteristic information, and judge whether the characteristic parameters are less than a preset first threshold;

If the value is less than the preset first threshold, the generation unit 204 performs gamma stretching on the first feature information to obtain enhanced second feature information, and generates a snapshot based on the second feature information.

Optionally, the sampling unit 201 is specifically used for:

Determine the band information and size information of the optical remote sensing image to be processed, and determine at least one band for generating a quick view based on the band information;

The pixels of the at least one band are extracted, a sampling ratio is determined based on the size information, and the pixels of the at least one band are down-sampled according to the sampling ratio to obtain the sampled pixels.

Optionally, the sampling unit 201 is specifically used for:

If the band information is a panchromatic band, the at least one band is the panchromatic band; or if the band information is multiple bands, the at least one band is red, green, and blue.

Optionally, the sampling unit 201 is specifically used to determine the sampling ratio according to the following formula:

Where, n represents the number of pixels of the optical remote sensing image to be processed; imgh represents the number of rows of pixels of the optical remote sensing image to be processed; mgw represents the number of pixels of the optical remote sensing image to be processed. Rows.

Optionally, the stretching unit 202 is specifically used for:

Calculate a cumulative histogram of the at least one band according to the characteristic information, and determine the gray value of the pixels at 2% and the gray value of the pixels at 99% of the cumulative histogram;

The feature information is linearly stretched according to the gray value of the pixel at 2% and the gray value of the pixel at 99%.

Optionally, the stretching unit 202 is specifically configured to linearly stretch the feature information according to the following formula:

Among them, g(x, y) represents the characteristic information at (x, y) in the image after stretching; f(x, y) represents the characteristic information at (x, y) in the image before stretching; min represents 2% The gray value of the pixel at 99%; max represents the gray value of the pixel at 99%.

Optionally, the judgment unit 203 is specifically used to:

The number of pixels, the pixel value of each pixel, and the median are determined according to the first feature information, where the median refers to the pixel value of 50% of the pixels in the cumulative histogram. ;

Calculate the pixel mean value of each pixel according to the number of the pixels and the pixel value of each pixel;

The pixel mean square error of each pixel is calculated based on the pixel mean value of each pixel.

Optionally, the generating unit 204 is specifically configured to perform gamma stretching on the first feature information according to the following formula:

Among them, γ represents the gamma stretching coefficient, medianv represents the median, t ₁ and t ₂ represent the preset second threshold; h(x, y) represents the second feature information of the pixel at (x, y).

Optionally, referring to Figure 3, the device further includes a computing unit 205; the computing unit 205 is specifically used for:

The reflectivity of the top of the atmosphere of each band is calculated according to the characteristic information, and the characteristic information is preprocessed according to the reflectivity.

Referring to Figure 4, this application provides a computer device, which includes:

Memory 401, used to store instructions executed by at least one processor;

The processor 402 is configured to execute instructions stored in the memory to perform the method described in Figure 1 .

The present application provides a computer-readable storage medium that stores computer instructions. When the computer instructions are run on a computer, they cause the computer to execute the method described in the first aspect.

Those skilled in the art will understand that embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment that combines software and hardware aspects. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, magnetic disk storage and optical storage, etc.) embodying computer-usable program code therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each process and/or block in the flowchart illustrations and/or block diagrams, and combinations of processes and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a use A device for realizing the functions specified in one process or multiple processes of the flowchart and/or one block or multiple blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory that causes a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction means, the instructions The device implements the functions specified in a process or processes of the flowchart and/or a block or blocks of the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device, causing a series of operating steps to be performed on the computer or other programmable device to produce computer-implemented processing, thereby executing on the computer or other programmable device. Instructions provide steps for implementing the functions specified in a process or processes of a flowchart diagram and/or a block or blocks of a block diagram.

Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. In this way, if these modifications and variations of the present application fall within the scope of the claims of the present application and equivalent technologies, the present application is also intended to include these modifications and variations.

Claims (3)

1. A method for generating a quick view of an optical remote sensing image, which is characterized by including: Resample the optical remote sensing image to be processed to obtain the sampled pixels, and determine the characteristic information of the sampled pixels; Resample the optical remote sensing image to be processed to obtain the sampled pixels, including: Determine the band information and size information of the optical remote sensing image to be processed, and determine at least one band for generating a quick view based on the band information; Extract the pixels of the at least one band, determine a sampling ratio according to the size information, and perform down-resampling of the pixels of the at least one band according to the sampling ratio to obtain the sampled pixels; Determining at least one band based on the band information includes: If the band information is a panchromatic band, then the at least one band is the panchromatic band; or If the band information is multi-band, the at least one band is red, green and blue; Linearly stretch the feature information to obtain enhanced first feature information; Calculate the characteristic parameters of the pixels in each band according to the first characteristic information, and determine whether the characteristic parameters are less than a preset first threshold; If it is less than, perform gamma stretching on the first feature information to obtain enhanced second feature information, and generate a quick view based on the second feature information; Linear stretching of the feature information includes: Calculate a cumulative histogram of the at least one band according to the characteristic information, and determine the gray value of the pixels at 2% and the gray value of the pixels at 99% of the cumulative histogram; Linearly stretch the feature information according to the grayscale value of the pixels at 2% and the grayscale value of the pixels at 99%; Linearly stretching the feature information according to the gray value of the pixel at 2% and the gray value of the pixel at 99% includes: The feature information is linearly stretched according to the following formula: Among them, g(x, y) represents the characteristic information at (x, y) in the image after stretching; f(x, y) represents the characteristic information at (x, y) in the image before stretching; min represents 2% The gray value of the pixel; max represents the gray value of the pixel at 99%; Calculating the characteristic parameters of the pixels in each band according to the first characteristic information includes: The number of pixels, the pixel value of each pixel, and the median are determined according to the first feature information, where the median refers to the pixel value of 50% of the pixels in the cumulative histogram. ; Calculate the pixel mean value of each pixel according to the number of the pixels and the pixel value of each pixel; Calculate the pixel mean square error of each pixel based on the pixel mean value of each pixel; Perform gamma stretching on the first feature information to obtain enhanced second feature information, including: The first feature information is gamma stretched according to the following formula: Among them, γ represents the gamma stretching coefficient, medianv represents the median, t ₁ and t ₂ represent the preset second threshold; h(x, y) represents the second feature information of the pixel at (x, y); Calculating the pixel mean value of each pixel based on the number of pixels and the pixel value of each pixel includes: calculating the pixel mean value of each pixel according to the following formula: Among them, μ represents the mean value of the pixels in each band; m(x, y) represents the pixel value of the pixel at (x, y); p represents the number of the pixels; Calculating the pixel mean square error of each pixel based on the pixel mean value of each pixel includes: calculating the pixel mean square error of each pixel according to the following formula: Among them, σ represents the pixel mean square error of each pixel. 2. The method of claim 1, wherein determining the sampling ratio according to the size information includes: Determine the sampling ratio according to the following formula: Where, n represents the number of pixels of the optical remote sensing image to be processed; imgh represents the number of rows of pixels of the optical remote sensing image to be processed; imgw represents the number of pixels of the optical remote sensing image to be processed. Number of columns. 3. The method according to any one of claims 1 to 2, characterized in that, before linearly stretching the feature information, it further includes: The reflectivity of the top of the atmosphere of each band is calculated according to the characteristic information, and the characteristic information is preprocessed according to the reflectivity.