阅读说明：本技术 基于直方图匹配的遥感图像匀色方法、系统、设备及介质 (Remote sensing image color homogenizing method, system, equipment and medium based on histogram matching ) 是由 陶炳成 臧文乾 黄祥志 刘川 王栋 王更科 余涛 王帅 靳博文 于 2021-08-27 设计创作，主要内容包括：本申请提供有一种基于直方图匹配的遥感图像匀色方法。本技术方案分别获取与待校正图像对应的预设数量的第一预设波段和与底图图像对应同样数量的第二预设波段,分别逐个计算第一预设波段和第二预设波段的中位数,并逐一对齐第一预设波段的中位数和第二预设波段的中位数。以每个第一预设波段的中位数为基准将每个第一预设波段分割为第一单元集合和第二单元集合；并以第二预设波段的中位数为基准将每个第二预设波段分割为第三单元集合和第四单元集合；将第一单元集合拉伸至与其中位数对应的第三单元集合的范围内,并将第二单元集合拉伸至与其中位数对应的第四单元集合的范围内。(The application provides a remote sensing image color homogenizing method based on histogram matching. According to the technical scheme, a first preset wave band with a preset number corresponding to an image to be corrected and a second preset wave band with the same number corresponding to a base image are obtained respectively, the median of the first preset wave band and the median of the second preset wave band are calculated one by one respectively, and the median of the first preset wave band and the median of the second preset wave band are aligned one by one. Dividing each first preset wave band into a first unit set and a second unit set by taking the median of each first preset wave band as a reference; dividing each second preset wave band into a third unit set and a fourth unit set by taking the median of the second preset wave band as a reference; the first set of cells is stretched into the range of a third set of cells corresponding to their median, and the second set of cells is stretched into the range of a fourth set of cells corresponding to their median.)

1. A remote sensing image color homogenizing method based on histogram matching is characterized by comprising the following steps:

acquiring a pixel gray value of an image to be corrected and forming a first pixel set;

dividing gray values in the first pixel set into a first preset wave band with a preset number;

counting an upper limit value, a median and a lower limit value of the gray value of each first preset wave band;

acquiring a pixel gray value of the base image and forming a third pixel set;

dividing the gray value in the third pixel set into a second preset wave band with a preset number;

counting an upper limit value, a median and a lower limit value of the gray value of each second preset wave band;

aligning the median of the first preset wave band and the median of the second preset wave band one by one;

the first preset wave band is divided one by one as follows: a first unit set with a gray value range between a lower limit value and a median in the band and a second unit set with a gray value range between a median +1 and an upper limit value in the band;

the second preset wave band is divided one by one as follows: a third unit set with a gray value range between the lower limit value and the median in the band and a fourth unit set with a gray value range between the median +1 and the upper limit value in the band;

stretching the first set of cells to within a range of a third set of cells corresponding to the median thereof;

stretching the second unit set to be within a range of a fourth unit set corresponding to the median;

and summarizing the stretched first unit set and the stretched second unit set one by one to obtain a corrected first preset wave band so as to obtain a first pixel set.

2. The remote sensing image color homogenizing method based on histogram matching as claimed in claim 1, characterized in that:

before calculating the upper limit value, the median and the lower limit value of the gray value of each first preset wave band, the method also comprises the following steps:

traversing all first preset wave bands in the first pixel set and correcting the gray value in each first preset wave band one by one to obtain a second pixel set; and calculating an upper limit value, a median and a lower limit value of each first preset waveband gray value in the second pixel set.

3. The remote sensing image color homogenizing method based on histogram matching as claimed in claim 2, characterized in that:

in the step of correcting the gray value in each first preset band one by one, the method further includes: multiplying the gray value in each first preset wave band by 255 and then opening a square root, and obtaining a corrected gray value after rounding; a second set of pixels is formed.

4. The remote sensing image color homogenizing method based on histogram matching according to any one of claims 1 to 3, characterized by comprising the following steps:

and in the step of calculating the upper limit value, the median and the lower limit value of the gray value of each first preset waveband in the first pixel set, excluding pixels with the gray value of 0.

5. A remote sensing image uniform color system based on histogram matching is characterized in that: the method comprises the following steps:

the first data acquisition module is used for acquiring a pixel gray value of an image to be corrected and forming a first pixel set; dividing gray values in the first pixel set into a first preset wave band with a preset number; counting an upper limit value, a median and a lower limit value of the gray value of each first preset wave band;

the second data acquisition module is used for acquiring the pixel gray value of the base image and forming a third pixel set; dividing the gray value in the third pixel set into a second preset wave band with a preset number; counting an upper limit value, a median and a lower limit value of the gray value of each second preset wave band;

the alignment module is used for aligning the median of the first preset wave band and the median of the second preset wave band one by one;

the first segmentation module is used for segmenting the first preset wave band one by one as follows: a first unit set with a gray value range between a lower limit value and a median in the band and a second unit set with a gray value range between a median +1 and an upper limit value in the band;

the second segmentation module is used for segmenting a second preset wave band one by one as follows: a third unit set with a gray value range between the lower limit value and the median in the band and a fourth unit set with a gray value range between the median +1 and the upper limit value in the band;

the linear stretching module is used for stretching the first unit set to a range of a third unit set corresponding to the median; stretching the second unit set to be within a range of a fourth unit set corresponding to the median;

and the output module is used for summarizing the stretched first unit set and the stretched second unit set one by one to obtain a corrected first pixel set and outputting a homogenized corrected image.

6. The remote sensing image color homogenizing method based on histogram matching as claimed in claim 5, characterized in that:

the first data acquisition module further comprises:

the data correction module is used for traversing first preset wave bands in the first pixel set and correcting the gray value in each first preset wave band one by one to obtain a second pixel set; and calculating an upper limit value, a median and a lower limit value of each first preset waveband gray value in the second pixel set.

7. A computer device, the device comprising: a memory for storing executable program code; one or more processors configured to read executable program code stored in the memory to perform the method of histogram matching based remote sensing image shading according to any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises instructions which, when run on a computer, cause the computer to perform the method for histogram matching based remote sensing image shading according to any one of claims 1 to 4.

Technical Field

The disclosure specifically discloses a remote sensing image color homogenizing method, system, equipment and medium based on histogram matching.

Background

The remote sensing technology is a technology for detecting and identifying a target by sensing electromagnetic waves, visible light and infrared rays reflected by the target or radiated by the target from a long distance. Remote sensing images obtained based on remote sensing technology often need further processing to be able to obtain an interpretable final image map which is convenient for naked eye recognition. With the rapid development of computers and the continuous development of remote sensing technology, manual processing is assisted by making corresponding processing flows through computer algorithms, so that manpower is reduced to a great extent, efficiency is increased, and the method is a major breakthrough in the field of remote sensing.

With the development of the remote sensing field, the related remote sensing technology is continuously appeared, and the remote sensing technology is widely applied to military reconnaissance, earth resource detection, environmental pollution detection, earthquake and volcanic eruption prediction and the like. And the field and the use amount of the remote sensing image homogenizing and splicing technology are increasingly expanded in recent years. For satellite images, unmanned aerial vehicle images and the like, the remote sensing images and images actually wanted to be obtained have certain errors due to the influences of factors such as weather, sensors, human factors and the like. The occurrence of these errors may cause the acquired image data to be different in brightness and color, resulting in color difference and visual effect when the acquired images are stitched. Therefore, the images need to be subjected to color homogenizing treatment before splicing each obtained remote sensing image.

The currently existing color homogenizing processing methods probably include the following methods: 1. the color can be directly adjusted to the color closest to the base image through human eyes in sense of touch by directly and manually adjusting the color through PS; 2. linear stretching: finding out the most suitable stretching range of the image according to the gray mean value and the gray variance of the image; 3. histogram matching method: based on the histogram of one image, making the gray distribution of the original image similar to that of the reference image (i.e. converting the histogram of one image to make it similar to that of the other image) is a function for finding a gray level transformation in terms of algorithm design. Generally, a linear relation between the mean and the variance of two images is established; 4. the color homogenizing method of the Wallis filter comprises the following steps: is a local image transformation that maps the mean and standard deviation of the image to be processed to the mean and standard deviation of the reference image.

However, the above-mentioned color homogenizing treatment method still has many disadvantages, such as: 1. the color matching method directly and manually by PS has the following defects: the color closest to the base map is adjusted through human eyes in a sense, so that the operation is not facilitated no matter how much data is, time and labor are consumed, a plurality of factors which can influence the result are caused, and different people can process the same image and have different results. 2. Simple linear stretching drawback: sometimes even if a pixel is stretched to a specified range, there may still be a large color difference. 3. The disadvantages of the histogram matching method are: histogram matching causes a range of gray levels to be too concentrated if the background range in the image is too large to be processed for background removal. 4. The homogeneous color method of the Wallis filter has the following disadvantages: a very complex step is required to be able to level the image to be corrected with the reference image that can be verified.

Disclosure of Invention

In view of the foregoing defects or shortcomings in the prior art, the present application aims to provide a method, a system, a device, and a medium for homogenizing remote sensing images based on histogram matching, which are capable of facilitating batch homogenization processing and effectively avoiding generation of large color difference compared to the prior art.

In a first aspect, a remote sensing image color homogenizing method based on histogram matching comprises the following steps: acquiring a pixel gray value of an image to be corrected and forming a first pixel set; dividing gray values in the first pixel set into a first preset wave band with a preset number; counting an upper limit value, a median and a lower limit value of the gray value of each first preset wave band; acquiring a pixel gray value of the base image and forming a third pixel set; dividing the gray value in the third pixel set into a second preset wave band with a preset number; counting an upper limit value, a median and a lower limit value of the gray value of each second preset wave band; aligning the median of the first preset wave band and the median of the second preset wave band one by one; the first preset wave band is divided one by one as follows: a first unit set with a gray value range between a lower limit value and a median in the band and a second unit set with a gray value range between a median +1 and an upper limit value in the band; the second preset wave band is divided one by one as follows: a third unit set with a gray value range between the lower limit value and the median in the band and a fourth unit set with a gray value range between the median +1 and the upper limit value in the band; stretching the first set of cells to within a range of a third set of cells corresponding to the median thereof; stretching the second unit set to be within a range of a fourth unit set corresponding to the median; and summarizing the stretched first unit set and the stretched second unit set one by one to obtain a corrected first preset wave band so as to obtain a first pixel set.

According to the technical scheme provided by the embodiment of the application, before calculating the upper limit value, the median and the lower limit value of the gray value of each first preset waveband, the method further comprises the following steps: traversing all first preset wave bands in the first pixel set and correcting the gray value in each first preset wave band one by one to obtain a second pixel set; and calculating an upper limit value, a median and a lower limit value of each first preset waveband gray value in the second pixel set.

According to the technical scheme provided by the embodiment of the application, the step of correcting the gray value in each first preset waveband one by one further comprises: multiplying the gray value in each first preset wave band by 255 and then opening a square root, and obtaining a corrected gray value after rounding; a second set of pixels is formed.

According to the technical scheme provided by the embodiment of the application, in the step of calculating the upper limit value, the median and the lower limit value of the gray value of each first preset waveband in the first pixel set, the pixels with the gray value of 0 are excluded.

In a second aspect, a remote sensing image color homogenizing system based on histogram matching includes: the first data acquisition module is used for acquiring a pixel gray value of an image to be corrected and forming a first pixel set; dividing gray values in the first pixel set into a first preset wave band with a preset number; counting an upper limit value, a median and a lower limit value of the gray value of each first preset wave band; the second data acquisition module is used for acquiring the pixel gray value of the base image and forming a third pixel set; dividing the gray value in the third pixel set into a second preset wave band with a preset number; counting an upper limit value, a median and a lower limit value of the gray value of each second preset wave band; the alignment module is used for aligning the median of the first preset wave band and the median of the second preset wave band one by one; the first segmentation module is used for segmenting the first preset wave band one by one as follows: a first unit set with a gray value range between a lower limit value and a median in the band and a second unit set with a gray value range between a median +1 and an upper limit value in the band; the second segmentation module is used for segmenting a second preset wave band one by one as follows: a third unit set with a gray value range between the lower limit value and the median in the band and a fourth unit set with a gray value range between the median +1 and the upper limit value in the band; the linear stretching module is used for stretching the first unit set to a range of a third unit set corresponding to the median; stretching the second unit set to be within a range of a fourth unit set corresponding to the median; and the output module is used for summarizing the stretched first unit set and the stretched second unit set one by one to obtain a corrected first pixel set and outputting a homogenized corrected image.

According to the technical solution provided by the embodiment of the present application, the first data obtaining module further includes: the data correction module is used for traversing first preset wave bands in the first pixel set and correcting the gray value in each first preset wave band one by one to obtain a second pixel set; and calculating an upper limit value, a median and a lower limit value of each first preset waveband gray value in the second pixel set.

In a third aspect, a computer device, the device comprising: a memory for storing executable program code; one or more processors configured to read executable program code stored in the memory to perform the histogram matching based method of color homogenization of remote sensing images according to the first aspect.

In a fourth aspect, a computer-readable storage medium comprises instructions which, when run on a computer, cause the computer to perform the method for remote sensing image color homogenization based on histogram matching as described in the first aspect.

Has the advantages that:

in summary, the present application discloses a remote sensing image color homogenizing method based on histogram matching. According to the technical scheme, a first preset wave band with a preset number corresponding to an image to be corrected and a second preset wave band with the same number corresponding to a base image are obtained respectively, the median of the first preset wave band and the median of the second preset wave band are calculated one by one respectively, and the median of the first preset wave band and the median of the second preset wave band are aligned one by one. Dividing each first preset wave band into a first unit set and a second unit set by taking the median of each first preset wave band as a reference; dividing each second preset wave band into a third unit set and a fourth unit set by taking the median of the second preset wave band as a reference; the first set of cells is stretched into the range of a third set of cells corresponding to their median, and the second set of cells is stretched into the range of a fourth set of cells corresponding to their median. Based on the design, under the condition that the histogram obtained by the first pixel set has offset, the center of data distribution can be better captured by aligning the median of the first preset waveband and the median of the second preset waveband one by one, and then the first preset waveband is re-segmented and linearly stretched again based on the second preset waveband to obtain the corrected first pixel set, so that the corrected image subjected to color homogenizing is finally output.

Further, the present technical solution also provides a technical solution for correcting gray values in all first preset bands in the first pixel set, that is: traversing all first preset wave bands in the first pixel set and correcting the gray value in each first preset wave band one by one to obtain a second pixel set; and calculating an upper limit value, a median and a lower limit value of each first preset waveband gray value in the second pixel set. Based on the design, the gray value in the first pixel set under the influence of the to-be-corrected pixel can be integrally lightened, the risk of overexposure is reduced, the data are more concentrated, and the offset degree is reduced.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:

FIG. 1 is a schematic diagram of a histogram of an image to be corrected;

FIG. 2 is a histogram diagram of a base image;

FIG. 3 is a schematic diagram of a histogram of a corrected image after color smoothing;

FIG. 4a is a schematic illustration of an unprocessed image;

FIG. 4b is a schematic illustration of a processed image;

fig. 5 is a schematic diagram of a hardware structure of the computer device.

Detailed Description

The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

A remote sensing image color homogenizing method based on histogram matching comprises the following steps:

acquiring a pixel gray value of an image to be corrected and forming a first pixel set; and dividing the gray value in the first pixel set into a preset number of first preset wave bands. Specifically, for an image to be corrected, extracting a gray value corresponding to each pixel point on the image; a first pixel set is obtained, and a histogram corresponding to the first pixel set is shown in fig. 1. The histogram in the graph shown in fig. 1, with a shift to the left, appears clearly. Furthermore, the histogram in fig. 1 is a preset number of first preset bands, namely: are divided into R \ G \ B respectively. And counting the upper limit value, the median and the lower limit value of the gray value of each first preset wave band. Optionally, in the step of calculating the upper limit value, the median and the lower limit value of the gray value of each first preset waveband in the first pixel set, the pixel with the gray value of 0 is excluded to avoid the influence of the black background part on the image color correction result.

Acquiring a pixel gray value of the base image and forming a third pixel set; and dividing the gray value in the third pixel set into a preset number of second preset wave bands. Specifically, for the base image, extracting the gray value corresponding to each pixel point on the base image; a third pixel set is obtained, and a histogram corresponding to the third pixel set is shown in fig. 2. The histogram shown in fig. 2 is a preset number of second preset bands, namely: are divided into R \ G \ B respectively. And counting the upper limit value, the median and the lower limit value of the gray value of each second preset wave band. Optionally, in the step of calculating the upper limit value, the median and the lower limit value of the gray value of each first preset waveband in the first pixel set, the pixel with the gray value of 0 is excluded to avoid the influence of the black background part on the image color correction result.

Aligning the median of the first preset wave band and the median of the second preset wave band one by one, namely: the median of each first predetermined band (R \ G \ B) in FIG. 1 is aligned with the median of each second predetermined band (R \ G \ B) in FIG. 2 in a one-to-one correspondence. Specifically, since the mean is susceptible to outliers (e.g., the mean of five numbers 4, 5, 5, 6, 100 is 24, and is affected by 100 to be significantly out of range in the data set, but the median is 5, and is less affected by 100), in the case of a histogram having a shift, the median can better capture the center of the data distribution than the mean, reducing the shift in the histogram, i.e.: the shape of the histogram can be modified to more closely approximate the effect of the base map.

The first preset wave band is divided one by one as follows: the first unit set with the gray value range between the lower limit value and the median in the band and the second unit set with the gray value range between the median +1 and the upper limit value in the band. The first preset band is composed of a first unit set and a second unit set.

The second preset wave band is divided one by one as follows: a third unit set with a gray value range between the lower limit value and the median in the band and a fourth unit set with a gray value range between the median +1 and the upper limit value in the band; the second predetermined band is composed of a third set of cells and a fourth set of cells.

Stretching the first set of cells to within a range of a third set of cells corresponding to the median thereof; stretching the second unit set to be within a range of a fourth unit set corresponding to the median; and summarizing the stretched first unit set and the stretched second unit set one by one to obtain a corrected first preset wave band so as to obtain a first pixel set. Please refer to fig. 3 (R \ G \ B) modified by the above method.

Based on the design, under the condition that the histogram obtained by the first pixel set has offset, the center of data distribution can be better captured by aligning the median of the first preset waveband and the median of the second preset waveband one by one, then the first preset waveband is re-segmented and linearly stretched again based on the second preset waveband, the corrected first preset waveband is obtained, and the first pixel set is further obtained, so that the corrected image subjected to color homogenizing is finally output.

Please refer to the pre-processing image shown in fig. 4a, wherein the image to be corrected is shown in the area a, and the base image is shown in the area B; please refer to the processed image shown in fig. 4b, wherein the area a' shows the smoothed corrected image; therefore, the remote sensing image color homogenizing method has excellent effect and is suitable for popularization and application.

In a preferred embodiment, before calculating the upper limit value, the median and the lower limit value of the gray-scale value of each first preset waveband, the following steps are further performed: traversing all first preset wave bands in the first pixel set and correcting the gray value in each first preset wave band one by one to obtain a second pixel set; and calculating an upper limit value, a median and a lower limit value of each first preset waveband gray value in the second pixel set. Specifically, the step of correcting the gray scale value in each first preset band one by one further includes: multiplying the gray value in each first preset wave band by 255 and then opening a square root, and obtaining a corrected gray value after rounding; a second set of pixels is formed.

In the specific correction process, in the calculation process, on one hand, the gray value of the pixel with the gray value of 0 is kept unchanged; on the other hand, pixels with smaller gray scale values will brighten more significantly (e.g., pixels with 10 gray scale values will brighten to 50 and pixels with 50 gray scale values will brighten to 112), and pixels with larger gray scale values will change less to reduce the risk of overexposure (e.g., pixels with 200 gray scale values will brighten to 225 gray scale values and pixels with 250 gray scale values will brighten to 252 gray scale values), so this process will make the data relatively more concentrated and reduce the degree of right-hand skew.

A remote sensing image color homogenizing system based on histogram matching comprises: the first data acquisition module is used for acquiring a pixel gray value of an image to be corrected and forming a first pixel set; dividing gray values in the first pixel set into a first preset wave band with a preset number; counting an upper limit value, a median and a lower limit value of the gray value of each first preset wave band; the second data acquisition module is used for acquiring the pixel gray value of the base image and forming a third pixel set; dividing the gray value in the third pixel set into a second preset wave band with a preset number; counting an upper limit value, a median and a lower limit value of the gray value of each second preset wave band; the alignment module is used for aligning the median of the first preset wave band and the median of the second preset wave band one by one; the first segmentation module is used for segmenting the first preset wave band one by one as follows: a first unit set with a gray value range between a lower limit value and a median in the band and a second unit set with a gray value range between a median +1 and an upper limit value in the band; the second segmentation module is used for segmenting a second preset wave band one by one as follows: a third unit set with a gray value range between the lower limit value and the median in the band and a fourth unit set with a gray value range between the median +1 and the upper limit value in the band; the linear stretching module is used for stretching the first unit set to a range of a third unit set corresponding to the median; stretching the second unit set to be within a range of a fourth unit set corresponding to the median; and the output module is used for summarizing the stretched first unit set and the stretched second unit set one by one to obtain a corrected first pixel set and outputting the homogenized image to be corrected.

Based on the method, the remote sensing image color homogenizing method can be manufactured into a transferable device by utilizing the first data acquisition module, the second data acquisition module, the alignment module, the first segmentation module, the second segmentation module, the linear stretching module and the output module, so that the use is convenient and fast.

In a preferred embodiment, the first data obtaining module further includes: the data correction module is used for traversing first preset wave bands in the first pixel set and correcting the gray value in each first preset wave band one by one to obtain a second pixel set; and calculating an upper limit value, a median and a lower limit value of each first preset waveband gray value in the second pixel set.

In a preferred embodiment, the present application also provides a computer apparatus, the apparatus comprising: a memory for storing executable program code; one or more processors for reading executable program code stored in the memory to perform the histogram matching based remote sensing image shading method as described above. Please refer to fig. 5 for a hardware structure diagram of the computer device.

The computer system includes a Central Processing Unit (CPU)501, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section into a Random Access Memory (RAM) 503. In the RAM503, various programs and data necessary for system operation are also stored. The CPU 501, ROM 502, and RAM503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output section including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The drives are also connected to the I/O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.

In particular, according to an embodiment of the present invention, the process described above for the histogram matching based remote sensing image color homogenizing method may be implemented as a computer software program. For example, an embodiment of the present invention, which relates to a method for remote sensing image color homogenizing based on histogram matching, comprises a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section, and/or installed from a removable medium. The above-described functions defined in the system of the present application are executed when the computer program is executed by the Central Processing Unit (CPU) 501.

It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of various histogram matching based remote sensing image color homogenizing methods, apparatus, and computer program products according to the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present invention may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves. The described units or modules may also be provided in a processor, and may be described as: a processor comprises a first generation module, an acquisition module, a search module, a second generation module and a merging module. Wherein the designation of a unit or module does not in some way constitute a limitation of the unit or module itself.

As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method for remote sensing image shading based on histogram matching as described in the above embodiments.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.

The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.