CN112258433A - Gray level histogram stretching method for enhancing display of internal wave features in remote sensing data - Google Patents

Gray level histogram stretching method for enhancing display of internal wave features in remote sensing data Download PDF

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CN112258433A
CN112258433A CN202011141497.0A CN202011141497A CN112258433A CN 112258433 A CN112258433 A CN 112258433A CN 202011141497 A CN202011141497 A CN 202011141497A CN 112258433 A CN112258433 A CN 112258433A
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histogram
stretching
gray value
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CN112258433B (en
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邹丽
张九鸣
王学宇
孙铁志
闻泽华
马鑫宇
于宗冰
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Dalian University of Technology
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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: cutting off the gray value to obtain an effective gray value; s4: carrying out zero equalization on the gray value to obtain a histogram of the zero equalized gray value; s5: performing primary linear stretching; s6: performing secondary stretching to obtain a secondary stretching gray value histogram; s7: and carrying out normalization processing to obtain the picture with enhanced inner wave characteristics. Compared with the existing image processing means for enhancing the contrast, the method provided by the invention performs the important stretching processing on the gray value interval for representing the internal wave through the secondary stretching operation, can highlight the representation form of the internal wave in the remote sensing picture, and provides an effective preprocessing method for marking and identifying the internal wave.

Description

Gray level histogram stretching method for enhancing display of internal wave features in remote sensing data
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 waves are characterized by large wave amplitude and large energy, the strong shear flow caused by the ocean internal waves is a great threat to ocean engineering and underwater submerged bodies, and the ocean internal wave distribution is determined through actual measurement data, so that the ocean internal wave distribution has important engineering significance. The observation methods for marine internal waves are 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 a wide coverage range, and is an effective observation means.
The marine internal waves are observed through satellite remote sensing data and are expressed in an isolated wave form according to the fact that the marine internal waves are mostly reflected, diffuse reflection and mirror reflection can be caused to occur in reflected light distribution on the water surface due to the caused radiation convergence and radiation phenomena, and then optical signals received by a remote sensing satellite are expressed as light and shade alternate stripes. The identification of the ocean internal wave can be completed by finding the light and dark alternate stripes with the internal wave characteristics in the satellite remote sensing data. However, the remote sensing satellite has wide visual field and large marine internal wave scale, so that the gray value range in the observation visual field is large, the picture representation of the internal wave features is compressed, and the direct recognition from the picture is difficult. The existing methods for enhancing the contrast of the picture are mostly directed at stretching the global gray histogram, and although these methods can enhance the overall contrast of the picture, the expression of the internal wave features is not processed in an emphasized manner, and the represented information still cannot meet the requirements, so that a picture processing method for enhancing the internal wave features in the remote sensing picture is needed.
Disclosure of Invention
According to the technical problem, a gray level histogram stretching method for enhancing display of internal wave features in remote sensing data is provided. The invention performs simple preprocessing by intercepting the effective gray value range and by zero-averaging the picture. On the basis, the gray value range is changed by linear stretching through histogram secondary stretching operation, and then secondary histogram stretching is carried out by standard normal distribution, so that the enhanced expression of the internal wave characteristics is realized.
The technical means adopted by the invention are as follows:
a gray level histogram stretching method for enhancing display of internal wave features in remote sensing data comprises the following steps:
s1: selecting a spectral 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 grey value truncation on the picture window, and removing cloud layer and land features to obtain an effective grey value;
s4: carrying out gray value zero equalization on the effective gray value to enable the average value of the effective gray value to be zero, and obtaining a histogram of the zero equalized gray value;
s5: performing primary linear stretching on the 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 the standard normal distribution, and obtaining a gray value histogram of the primary linear stretching;
s6: performing secondary stretching on the gray value histogram subjected to the primary linear stretching by taking the standard normal distribution function as a kernel function to obtain a secondary stretching gray value histogram;
s7: and carrying out normalization processing on the secondary stretching gray value histogram to obtain the picture with enhanced internal wave characteristics.
Further, in the step S6, the standard normal distribution function is:
Figure BDA0002738415300000021
the quadratic stretching function is:
Figure BDA0002738415300000022
wherein G is a gray value histogram of the first linear stretching, GnIs a quadratic stretching gray value histogram.
Further, in the step S5, the negative value of the histogram of the zero-averaged gradation value is linearly stretched into the [ -3,0] section, and the positive value of the histogram of the zero-averaged gradation value is linearly stretched into the [0,3] section.
Compared with the prior art, the invention has the following advantages:
the invention can effectively enhance the expression of the internal wave characteristics in the remote sensing data and provide a preprocessing method for marking and identifying the internal wave.
The method can be expanded to the picture enhancement application in other fields to solve the problem of contrast enhancement similar to the condition of the gray level distribution characteristics of the internal waves in the remote sensing picture.
Based on the reasons, the invention can be widely popularized in the fields of ocean engineering and the like.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
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 the original remote sensing picture in step S2 in the embodiment of the present invention.
Fig. 3 is a schematic diagram of the gray scale value truncation in step S3 according to the embodiment of the present invention.
Fig. 4 is a remote sensing picture truncated in step S3 in the embodiment of the present invention.
Fig. 5 is a remote sensing picture after zero averaging in step S4 in the embodiment of the present invention.
Fig. 6 is a remote sensing picture after the first linear stretching in step S5 in the embodiment of the present invention.
Fig. 7 is a diagram of the twice stretched and normalized remote sensing pictures in steps S6 and S7 according to the embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It 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 invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the 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. Any specific values in all examples shown and discussed herein are to be construed as exemplary only and not as limiting. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
In the description of the present invention, it is to be understood that the orientation or positional relationship indicated by the directional terms such as "front, rear, upper, lower, left, right", "lateral, vertical, horizontal" and "top, bottom", etc., are generally based on the orientation or positional relationship shown in the drawings, and are used for convenience of description and simplicity of description only, and in the absence of any contrary indication, these directional terms are not intended to indicate and imply that the device or element so referred to must have a particular orientation or be constructed and operated in a particular orientation, and therefore should not be considered as limiting the scope of the present invention: the terms "inner and outer" refer to the inner and outer relative to the profile of the respective component itself.
Spatially relative terms, such as "above … …," "above … …," "above … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial relationship 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 of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is turned over, devices described as "above" or "on" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary term "above … …" can include both an orientation of "above … …" and "below … …". The device may be otherwise variously oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
It should be noted that the terms "first", "second", and the like are used to define the components, and are only used for convenience of distinguishing the corresponding components, and the terms have no special meanings unless otherwise stated, and therefore, the scope of the present invention should not be construed as being limited.
As shown in fig. 1 to 7, a gray level histogram stretching method for enhancing display of internal wave features in remote sensing data includes the following steps:
s1: selecting a spectral 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 grey value truncation on the picture window, and removing cloud layer and land features to obtain an effective grey value;
s4: carrying out gray value zero equalization on the effective gray value to enable the average value of the effective gray value to be zero, and obtaining a histogram of the zero equalized gray value;
s5: performing primary linear stretching on the 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 the standard normal distribution, and obtaining a gray value histogram of the primary linear stretching;
s6: performing secondary stretching on the gray value histogram subjected to the primary linear stretching by taking the standard normal distribution function as a kernel function to obtain a secondary stretching gray value histogram;
s7: and carrying out normalization processing on the secondary stretching gray value histogram to obtain the picture with enhanced internal wave characteristics.
Further, in the step S6, the standard normal distribution function is:
Figure BDA0002738415300000061
the quadratic stretching function is:
Figure BDA0002738415300000062
wherein G is a gray value histogram of the first linear stretching, GnIs a quadratic stretching gray value histogram.
Further, in the step S5, the negative value of the histogram of the zero-averaged gradation value is linearly stretched into the [ -3,0] section, and the positive value of the histogram of the zero-averaged gradation value is linearly stretched into the [0,3] section.
In this embodiment, the data "myd 021km. a 2009208.0540" in the MODIS database is taken as an example to perform the enhanced display of the internal wave characteristics,
in step S1, the MODIS remote sensing data is hyperspectral data, and data of different frequency bands represent different information. Aiming at the extraction problem of the surface features of the ocean internal waves, the reflection data of the 8 th frequency band in the MODIS remote sensing data is selected so as to represent the ocean water color features. Subsequent histogram stretching processes of enhancing the inner wave texture are all processed on this data.
In step S2, the original remote sensing picture is intercepted as a picture window with the same order of magnitude as the internal wave wavelength scale, in this embodiment, it is considered that 50 × 33 is a suitable window range, the internal wave water surface characteristics appear in the longitude and latitude range 113.88-115.93 ° E, 19.45-21.86 ° N, and the original remote sensing picture in this range is shown in fig. 2. G for setting the gray information of the pictureinput tAnd (4) showing.
In step S3, the gray value G of the original picture is comparedinputTruncation is performed to remove cloud layer features. The gray values of sea information in the remote sensing data are considered to be intensively distributed in a determined range, and redundant information such as cloud layers and the like can be independently distributed outside the range. Therefore, the cloud layer truncation method comprises the following steps: drawing a grey distribution histogram of the remote sensing data as shown in FIG. 3; and taking the gray value with the number of corresponding pixels not being 0 as a limited information point, defining continuous information points as a gray characteristic distribution area, 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 floating the gray information outside the boundary position. For this example, the primary distribution area is the solid box range in fig. 3, and the information being flattened is the dashed box information. The remote sensing picture obtained by the method after being cut off is shown in figure 4, and the grey value information of the remote sensing picture is set as G at the momentOceanAnd (4) showing.
In step S4, the truncated picture is subjected to zero mean gray valueChanging the gray value to zero, and changing the gray value G after zero equalizationmeanComprises the following steps:
Figure BDA0002738415300000071
the remote sensing picture after zero averaging is shown in fig. 5.
In step S5, linearly stretching the zero-averaged histogram to linearly stretch the negative part to the range of [ -3,0] and the positive part to the range of [0,3 ];
Figure BDA0002738415300000072
Figure BDA0002738415300000073
let GmeanThe gray level histogram after the first linear stretching is G, and the corresponding remote sensing picture is shown in FIG. 6.
In step S6, the standard normal distribution is used as the kernel function
Figure BDA0002738415300000074
The gray level histogram is subjected to secondary stretching transformation as follows:
Figure BDA0002738415300000075
in step S7, the gray values after the second stretching transformation are finally normalized:
Figure BDA0002738415300000076
the resulting remote sensing picture is shown in fig. 7.
Based on MODIS satellite remote sensing data, two times of stretching transformation are used as a key process, standard normal distribution is used as a kernel function of the stretching transformation of the gray histogram, and a gray histogram stretching method for enhancing display of internal wave features in remote sensing data is provided. The method can perform self-adaptive processing on satellite remote sensing data, enhance the display contrast of the internal wave characteristics, provide an effective preprocessing means for marking and identifying the internal wave characteristics, and provide a referable processing method for similar problems.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (3)

1. A gray level histogram stretching method for enhancing display of internal wave features in remote sensing data is characterized by comprising the following steps:
s1: selecting a spectral 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 grey value truncation on the picture window, and removing cloud layer and land features to obtain an effective grey value;
s4: carrying out gray value zero equalization on the effective gray value to enable the average value of the effective gray value to be zero, and obtaining a histogram of the zero equalized gray value;
s5: performing primary linear stretching on the 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 the standard normal distribution, and obtaining a gray value histogram of the primary linear stretching;
s6: performing secondary stretching on the gray value histogram subjected to the primary linear stretching by taking the standard normal distribution function as a kernel function to obtain a secondary stretching gray value histogram;
s7: and carrying out normalization processing on the secondary stretching gray value histogram to obtain the picture with enhanced internal wave characteristics.
2. The method for stretching the gray-scale histogram of the enhanced display of the internal wave features in the remote sensing data according to claim 1, wherein:
the standard normal distribution function in step S6 is:
Figure FDA0002738415290000011
the quadratic stretching function is:
Figure FDA0002738415290000012
wherein G is a gray value histogram of the first linear stretching, GnIs a quadratic stretching gray value histogram.
3. The method for stretching the gray-scale histogram of the enhanced display of the internal wave features in the remote sensing data according to claim 1, wherein:
in step S5, the negative value of the histogram of the zero-averaged gradation value is linearly stretched into the range of [ -3,0], and the positive value of the histogram of the zero-averaged gradation value is linearly stretched into the range of [0,3 ].
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120288194A1 (en) * 2010-09-28 2012-11-15 Tsuyoshi Nakamura Image processing device, image processing method, and integrated circuit
US20140010448A1 (en) * 2012-07-06 2014-01-09 Yissum Research Development Company Of The Hebrew University Of Jerusalem Ltd. Method and apparatus for enhancing a digital photographic image
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

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120288194A1 (en) * 2010-09-28 2012-11-15 Tsuyoshi Nakamura Image processing device, image processing method, and integrated circuit
US20140010448A1 (en) * 2012-07-06 2014-01-09 Yissum Research Development Company Of The Hebrew University Of Jerusalem Ltd. Method and apparatus for enhancing a digital photographic image
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)

* Cited by examiner, † Cited by third party
Title
HOLGER FINGER 等: "Phase synchrony facilitates binding and segmentation of natural images in a coupled neural oscillator network", 《FRONTIER IN COMPUTATIONAL NEUROSCIENCE》, vol. 7, pages 1 - 21 *
JIUMING ZHANG 等: "Effects of long-term fertilization on soil humic acid composition and structure in Black Soil", 《PLOS ONE》, vol. 12, no. 11, pages 1 - 14 *
WANHYUN CHO 等: "Enhancement technique of image contrast using new histogram transformation", 《JOURNAL OF COMPUTER AND COMMUNICATIONS》, pages 52 - 56 *
YANG YANG 等: "A ROI-based high capacity reversible data hiding scheme with contrast enhancement for medical images", 《MULTIMEDIA TOOLS AND APPLICATIONS》, vol. 77, pages 18043, XP036554823, DOI: 10.1007/s11042-017-4444-0 *
巴洪 等: "增强SH波电磁超声传感器信号的方法与实验", 《传感器与微系统》, vol. 31, no. 8, pages 34 - 36 *
彭佳琦 等: "一种基于截取法的灰度级等距离拉伸图像增强研究", 《激光与红外》, vol. 38, no. 12, pages 1255 - 1257 *
杨瑞祺 等: "利用二次函数的图像增强算法及FPGA实现", 《现代电子技术》, vol. 43, no. 8, pages 72 - 76 *

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