CN112258433B - Gray histogram stretching method for enhanced display of internal wave features in remote sensing data - Google Patents
Gray histogram stretching method for enhanced display of internal wave features in remote sensing data Download PDFInfo
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- 238000005315 distribution function Methods 0.000 claims description 6
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Abstract
The invention provides a gray level histogram stretching method for enhancing display of internal wave characteristics in remote sensing data, which comprises the following steps: s1: selecting an original remote sensing picture; s2: intercepting a picture window; s3: the gray value is truncated to obtain an effective gray value; s4: zero-equalizing the gray value to obtain a histogram of the zero-equalizing gray value; s5: first linear stretching; s6: secondarily stretching to obtain a secondarily stretched gray value histogram; s7: and (5) carrying out normalization processing to obtain a picture for enhancing the internal wave characteristics. Compared with the existing contrast-enhanced image processing means, the method provided by the invention performs the heavy stretching processing on the gray value interval of the characteristic internal wave through the operation of secondary stretching, can highlight the expression form of the internal wave in the remote sensing picture, and provides an effective pretreatment method for marking and identifying the internal wave.
Description
Technical Field
The invention relates to the technical field of ocean engineering, in particular to a gray level histogram stretching method for enhancing display of internal wave characteristics in remote sensing data.
Background
The ocean internal wave is often characterized by large amplitude and large energy, the strong shear flow caused by the ocean internal wave is a great threat to ocean engineering and underwater submarines, and the distribution of the ocean internal wave is clear through actual measurement data and has great engineering significance. The observation mode for ocean internal waves is generally divided into acoustic measurement, temperature chain measurement, satellite remote sensing measurement and the like, wherein the satellite remote sensing has a large data set and wide coverage, and is an effective observation mode.
The satellite remote sensing data is used for observing the ocean internal wave, the ocean internal wave is in an solitary wave form according to the ocean internal wave, the radiation aggregation and the radiation scattering phenomenon can cause diffuse reflection and specular reflection of the reflected light distribution of the water surface, and further the optical signal received by the remote sensing satellite is in a stripe with alternate brightness. The identification of ocean internal waves can be completed by searching light and dark alternate stripes with internal wave characteristics in satellite remote sensing data. However, as the remote sensing satellite has wide field of view and large ocean internal wave scale, the gray value range in the observation field of view is large, the picture expression of the internal wave features is compressed, and the picture is difficult to directly identify. The existing methods for enhancing the contrast of the picture are mostly aimed at stretching the global gray level histogram, and although the methods can enhance the overall contrast of the picture, the expression of the internal wave characteristics is not emphasized, and the displayed information is still difficult to meet the requirements, so that a picture processing method for enhancing the internal wave characteristics in the remote sensing picture is needed.
Disclosure of Invention
According to the technical problems, a gray level histogram stretching method for enhancing display of internal wave features in remote sensing data is provided. The invention carries out simple pretreatment by intercepting the effective gray value range and zero-equalizing the picture. On the basis, the gray value range is changed by linear stretching through histogram secondary stretching operation, and then the second histogram stretching is carried out by standard normal distribution, so that the enhancement of the internal wave characteristics is realized.
The invention adopts the following technical means:
a gray level histogram stretching method for enhancing display of internal wave features in remote sensing data comprises the following steps:
s1: selecting a spectrum frequency band of ocean water color in satellite remote sensing data as an original remote sensing picture;
s2: intercepting the original remote sensing picture into a picture window matched with the wavelength scale of the ocean internal wave;
s3: carrying out gray value truncation on the picture window, removing cloud layers and land features, and obtaining an effective gray value;
s4: zero-equalizing the gray value of the effective gray value to make the average value of the effective gray value zero and obtain a histogram of the zero-equalizing gray value;
s5: performing first linear stretching on positive and negative value distribution of the histogram of the zero-mean gray value to enable the positive and negative value distribution to fall between 3 times of variance of standard normal distribution, and obtaining a gray value histogram of the first linear stretching;
s6: performing secondary stretching on the gray value histogram subjected to primary linear stretching by taking the standard normal distribution function as a kernel function to obtain a secondary stretched gray value histogram;
s7: and carrying out normalization processing on the secondary stretching gray value histogram to obtain a picture for enhancing the internal wave characteristics.
Further, in the step S6, the standard normal distribution function is:
the second stretching function is:
wherein G is a gray value histogram of first linear stretching, G n Is a secondary stretched gray value histogram.
Further, in the step S5, the negative value of the histogram of the zero-averaged gray value is linearly stretched into the [ -3,0] interval, and the positive value of the histogram of the zero-averaged gray value is linearly stretched into the [0,3] interval.
Compared with the prior art, the invention has the following advantages:
the invention can effectively enhance the internal wave characteristic expression in the remote sensing data and provides a preprocessing method for internal wave marking and identification.
The invention can be expanded to the picture enhancement application in other fields, and is used for solving the contrast enhancement problem similar to the internal wave gray scale distribution characteristic in the remote sensing picture.
Based on the reasons, the invention can be widely popularized in the fields of ocean engineering and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flowchart of a gray level histogram stretching method for enhancing display of internal wave features in remote sensing data according to an embodiment of the present invention.
Fig. 2 is an original remote sensing picture in step S2 in the embodiment of the present invention.
Fig. 3 is a schematic diagram illustrating gray value truncation in step S3 according to an embodiment of the present invention.
Fig. 4 is a remote sensing picture after truncation in step S3 according to an embodiment of the present invention.
Fig. 5 is a remote sensing image after zero-averaging in step S4 according to an embodiment of the present invention.
Fig. 6 is a remote sensing image after the first linear stretching in step S5 according to an embodiment of the present invention.
Fig. 7 is a remote sensing image after the second stretching and normalization in steps S6 and S7 according to an embodiment of the present invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
The relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise. Meanwhile, it should be clear that the dimensions of the respective parts shown in the drawings are not drawn in actual scale for convenience of description. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate. In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
In the description of the present invention, it should be understood that the azimuth or positional relationships indicated by the azimuth terms such as "front, rear, upper, lower, left, right", "lateral, vertical, horizontal", and "top, bottom", etc., are generally based on the azimuth or positional relationships shown in the drawings, merely to facilitate description of the present invention and simplify the description, and these azimuth terms do not indicate and imply that the apparatus or elements referred to must have a specific azimuth or be constructed and operated in a specific azimuth, and thus should not be construed as limiting the scope of protection of the present invention: the orientation word "inner and outer" refers to inner and outer relative to the contour of the respective component itself.
Spatially relative terms, such as "above … …," "above … …," "upper surface at … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial location relative to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "above" or "over" other devices or structures would then be oriented "below" or "beneath" the other devices or structures. Thus, the exemplary term "above … …" may include both orientations of "above … …" and "below … …". The device may also be positioned in other different ways (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
In addition, the terms "first", "second", etc. are used to define the components, and are only for convenience of distinguishing the corresponding components, and the terms have no special meaning unless otherwise stated, and therefore should not be construed as limiting the scope of the present invention.
As shown in fig. 1 to 7, a gray level histogram stretching method for enhancing display of internal wave characteristics in remote sensing data includes the following steps:
s1: selecting a spectrum frequency band of ocean water color in satellite remote sensing data as an original remote sensing picture;
s2: intercepting the original remote sensing picture into a picture window matched with the wavelength scale of the ocean internal wave;
s3: carrying out gray value truncation on the picture window, removing cloud layers and land features, and obtaining an effective gray value;
s4: zero-equalizing the gray value of the effective gray value to make the average value of the effective gray value zero and obtain a histogram of the zero-equalizing gray value;
s5: performing first linear stretching on positive and negative value distribution of the histogram of the zero-mean gray value to enable the positive and negative value distribution to fall between 3 times of variance of standard normal distribution, and obtaining a gray value histogram of the first linear stretching;
s6: performing secondary stretching on the gray value histogram subjected to primary linear stretching by taking the standard normal distribution function as a kernel function to obtain a secondary stretched gray value histogram;
s7: and carrying out normalization processing on the secondary stretching gray value histogram to obtain a picture for enhancing the internal wave characteristics.
Further, in the step S6, the standard normal distribution function is:
the second stretching function is:
wherein G is a gray value histogram of first linear stretching, G n Is a secondary stretched gray value histogram.
Further, in the step S5, the negative value of the histogram of the zero-averaged gray value is linearly stretched into the [ -3,0] interval, and the positive value of the histogram of the zero-averaged gray value is linearly stretched into the [0,3] interval.
In this embodiment, the "MYD021KM.A2009008.0540" data in the MODIS database is taken as an example to perform the enhancement display of the internal wave characteristics,
in step S1, the MODIS remote sensing data is hyperspectral data, and data in different frequency bands represent different information. Aiming at the extraction problem of the ocean internal wave surface characteristics, the reflection data of the 8 th frequency band in MODIS remote sensing data is selected, so that the ocean water color characteristics are represented. The histogram stretching process of the subsequent enhanced internal wave texture is all processed on this data.
In step S2, the original remote sensing image is cut into an image window of the same order as the wavelength scale of the internal wave, in this embodiment, 50×33 is considered as a suitable window range, and the characteristics of the water surface of the internal wave appear in the latitude and longitude range 113.88-115.93 ° E and 19.45-21.86 ° N, and the original remote sensing image in this range is shown in fig. 2. G for setting gray information of the picture input t And (3) representing.
In step S3, the gray value G of the original picture input Truncation is performed to remove cloud features. Considering that the gray values of sea level information in remote sensing data should be distributed in a determined range in a concentrated manner, and redundant information such as cloud layers and the like can be distributed outside the range independently. The cloud layer cutting method comprises the following steps: drawing a gray level distribution histogram of the remote sensing data, as shown in fig. 3; and defining continuous information points as gray characteristic distribution areas by taking gray values with the number of corresponding pixels not being 0 as limited information points, and taking the distribution area with the largest gray value range span as a main distribution area. And determining the boundary position of the main distribution area, and trowelling gray information outside the boundary position. For this example, the main distribution area is the solid line box range in fig. 3, and the information to be smoothed is the broken line box information. Remote sensing map after truncation obtained by using the methodAs shown in FIG. 4, the gray value information G of the remote sensing picture is set Ocean And (3) representing.
In step S4, the truncated picture is zero-averaged to make its average value zero, and the zero-averaged gray value G mean The method comprises the following steps:
the remote sensing picture after zero-averaging is shown in fig. 5.
In step S5, linearly stretching the gray level histogram after zero equalization, linearly stretching a negative part to the [ -3,0] interval, and linearly stretching a positive part to the [0,3] interval;
set G mean The gray histogram after the first linear stretching is G, and the corresponding remote sensing picture is shown in fig. 6.
In step S6, the normal distribution is used as a kernel functionThe gray level histogram is subjected to a second stretching transformation as follows: />
In step S7, finally, normalizing the gray value subjected to the second stretching transformation:the obtained remote sensing picture is shown in fig. 7.
Based on MODIS satellite remote sensing data, a gray level histogram stretching method for enhancing and displaying internal wave features in remote sensing data is provided by taking two stretching transformations as a key flow and standard normal distribution as a kernel function of gray level histogram stretching transformations. The method can carry out self-adaptive processing on satellite remote sensing data, enhances the display contrast of internal wave characteristics, provides an effective pretreatment means for marking and identifying the internal wave characteristics, and provides a referenceable processing method for similar problems.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (3)
1. A gray level histogram stretching method for enhancing display of internal wave characteristics in remote sensing data is characterized by comprising the following steps:
s1: selecting a spectrum frequency band of ocean water color in satellite remote sensing data as an original remote sensing picture;
s2: intercepting the original remote sensing picture into a picture window matched with the wavelength scale of the ocean internal wave;
s3: carrying out gray value truncation on the picture window, removing cloud layers and land features, and obtaining an effective gray value;
s4: zero-equalizing the gray value of the effective gray value to make the average value of the effective gray value zero and obtain a histogram of the zero-equalizing gray value;
s5: performing first linear stretching on positive and negative value distribution of the histogram of the zero-mean gray value to enable the positive and negative value distribution to fall between 3 times of variance of standard normal distribution, and obtaining a gray value histogram of the first linear stretching;
s6: performing secondary stretching on the gray value histogram subjected to primary linear stretching by taking the standard normal distribution function as a kernel function to obtain a secondary stretched gray value histogram;
s7: and carrying out normalization processing on the secondary stretching gray value histogram to obtain a picture for enhancing the internal wave characteristics.
2. The gray level histogram stretching method for enhanced display of internal wave features in remote sensing data according to claim 1, wherein the method comprises the steps of:
the standard normal distribution function in said step S6 is:
the second stretching function is:
wherein G is a gray value histogram of first linear stretching, G n Is a secondary stretched gray value histogram.
3. The gray level histogram stretching method for enhanced display of internal wave features in remote sensing data according to claim 1, wherein the method comprises the steps of:
in said step S5, the negative value of the histogram of zero-averaged gray values is linearly stretched into the [ -3,0] interval, and the positive value of the histogram of zero-averaged gray values is linearly stretched into the [0,3] interval.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109345491A (en) * | 2018-09-26 | 2019-02-15 | 中国科学院西安光学精密机械研究所 | Remote sensing image enhancement method fusing gradient and gray scale information |
| CN109754368A (en) * | 2019-01-23 | 2019-05-14 | 郑州工程技术学院 | A kind of crack joining method in bridge quality testing |
| CN111724301A (en) * | 2020-06-19 | 2020-09-29 | 电子科技大学 | Self-adaptive stretching method and system based on histogram statistics |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8693801B2 (en) * | 2010-09-28 | 2014-04-08 | Panasonic Corporation | Image processing device, image processing method, and integrated circuit |
| US9256927B2 (en) * | 2012-07-06 | 2016-02-09 | Yissum Research Development Companyof The Hebrew University of Jerusalem Ltd. | Method and apparatus for enhancing a digital photographic image |
-
2020
- 2020-10-22 CN CN202011141497.0A patent/CN112258433B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109345491A (en) * | 2018-09-26 | 2019-02-15 | 中国科学院西安光学精密机械研究所 | Remote sensing image enhancement method fusing gradient and gray scale information |
| CN109754368A (en) * | 2019-01-23 | 2019-05-14 | 郑州工程技术学院 | A kind of crack joining method in bridge quality testing |
| CN111724301A (en) * | 2020-06-19 | 2020-09-29 | 电子科技大学 | Self-adaptive stretching method and system based on histogram statistics |
Non-Patent Citations (7)
| Title |
|---|
| A ROI-based high capacity reversible data hiding scheme with contrast enhancement for medical images;Yang Yang 等;《Multimedia Tools and Applications》;第77卷;第18043–18065页 * |
| Effects of long-term fertilization on soil humic acid composition and structure in Black Soil;Jiuming Zhang 等;《PLOS ONE》;第12卷(第11期);第1-14页 * |
| Enhancement technique of image contrast using new histogram transformation;Wanhyun Cho 等;《Journal of Computer and Communications》;第52-56页 * |
| Phase synchrony facilitates binding and segmentation of natural images in a coupled neural oscillator network;Holger Finger 等;《Frontier in Computational Neuroscience》;第7卷;第1-21页 * |
| 一种基于截取法的灰度级等距离拉伸图像增强研究;彭佳琦 等;《激光与红外》;第38卷(第12期);第1255-1257页 * |
| 利用二次函数的图像增强算法及FPGA实现;杨瑞祺 等;《现代电子技术》;第43卷(第8期);第72-76、81页 * |
| 增强SH波电磁超声传感器信号的方法与实验;巴洪 等;《传感器与微系统》;第31卷(第8期);第34-36、39页 * |
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