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 PDFInfo
- Publication number
- 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
- Authority
- CN
- China
- Prior art keywords
- histogram
- stretching
- gray value
- remote sensing
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 26
- 230000002708 enhancing effect Effects 0.000 title claims abstract description 14
- 238000012545 processing Methods 0.000 claims abstract description 7
- 238000010606 normalization Methods 0.000 claims abstract description 4
- 238000005315 distribution function Methods 0.000 claims description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 6
- 230000003595 spectral effect Effects 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 abstract description 4
- 230000014509 gene expression Effects 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 230000009466 transformation Effects 0.000 description 4
- 238000012935 Averaging Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000003672 processing method Methods 0.000 description 2
- 230000005855 radiation Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/40—Image enhancement or restoration using histogram techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/90—Dynamic range modification of images or parts thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
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
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:
the quadratic stretching function is:
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.
Drawings
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:
the quadratic stretching function is:
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:
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 ];
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
The gray level histogram is subjected to secondary stretching transformation as follows:
in step S7, the gray values after the second stretching transformation are finally normalized:
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:
the quadratic stretching function is:
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 ].
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011141497.0A CN112258433B (en) | 2020-10-22 | 2020-10-22 | Gray histogram stretching method for enhanced display of internal wave features in remote sensing data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011141497.0A CN112258433B (en) | 2020-10-22 | 2020-10-22 | Gray histogram stretching method for enhanced display of internal wave features in remote sensing data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112258433A true CN112258433A (en) | 2021-01-22 |
| CN112258433B CN112258433B (en) | 2024-03-29 |
Family
ID=74263348
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011141497.0A Active CN112258433B (en) | 2020-10-22 | 2020-10-22 | Gray histogram stretching method for enhanced display of internal wave features in remote sensing data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112258433B (en) |
Citations (5)
| 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 |
-
2020
- 2020-10-22 CN CN202011141497.0A patent/CN112258433B/en active Active
Patent Citations (5)
| 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)
| 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 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112258433B (en) | 2024-03-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | Sea ice concentration estimation during melt from dual-pol SAR scenes using deep convolutional neural networks: A case study | |
| CN107563303B (en) | Robust ship target detection method based on deep learning | |
| CN101214851B (en) | Intelligent all-weather actively safety early warning system and early warning method thereof for ship running | |
| Mattyus | Near real-time automatic marine vessel detection on optical satellite images | |
| Piñeros et al. | Detecting tropical cyclone genesis from remotely sensed infrared image data | |
| US8170282B1 (en) | Technique for ship/wake detection | |
| CN109376600A (en) | Multi-spectrum remote sensing image comprehensive characteristics cloud detection method of optic and device | |
| US10803590B2 (en) | Multiple-wavelength images analysis electro optical system for detection of accident ship and submerged person and analysis method thereof | |
| Osorio et al. | An algorithm for the measurement of shoreline and intertidal beach profiles using video imagery: PSDM | |
| CN109064479B (en) | Sea-sky-line detection method based on gray dynamic features of adjacent video frames | |
| KR20190055373A (en) | Machine learning-based satellite sea fog detection apparatus and method | |
| CN113486819A (en) | Ship target detection method based on YOLOv4 algorithm | |
| CN112348758A (en) | Optical remote sensing image data enhancement method and target identification method | |
| CN113744249A (en) | Marine ecological environment damage investigation method | |
| CN110069987B (en) | Single-stage ship detection algorithm and device based on improved VGG network | |
| Park et al. | Estimation of ship size from satellite optical image using elliptic characteristics of ship periphery | |
| Song et al. | Ship detection in haze and low-light remote sensing images via colour balance and DCNN | |
| CN110348442A (en) | A kind of shipborne radar image sea oil film recognition methods based on support vector machines | |
| Xu et al. | Marine radar oil spill monitoring technology based on dual-threshold and c–v level set methods | |
| Reddy | Digital image processing: principles and applications | |
| Urbański et al. | Object-oriented classification of QuickBird data for mapping seagrass spatial structure | |
| CN112258433A (en) | Gray level histogram stretching method for enhancing display of internal wave features in remote sensing data | |
| Sakieh et al. | An integrated spectral-textural approach for environmental change monitoring and assessment: analyzing the dynamics of green covers in a highly developing region | |
| CN111222470A (en) | Visible light remote sensing image ship detection method based on multivariate Gaussian distribution and PCANet | |
| Sankpal et al. | A review on image enhancement and color correction techniques for underwater images |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |