CN112558017B - Polarization target three-component decomposition result color visualization method and system - Google Patents
Polarization target three-component decomposition result color visualization method and system Download PDFInfo
- Publication number
- CN112558017B CN112558017B CN202011224654.4A CN202011224654A CN112558017B CN 112558017 B CN112558017 B CN 112558017B CN 202011224654 A CN202011224654 A CN 202011224654A CN 112558017 B CN112558017 B CN 112558017B
- Authority
- CN
- China
- Prior art keywords
- color
- component
- span
- target
- power
- 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.)
- Active
Links
- 238000000354 decomposition reaction Methods 0.000 title claims abstract description 45
- 230000010287 polarization Effects 0.000 title claims abstract description 23
- 238000007794 visualization technique Methods 0.000 title abstract description 9
- 238000000034 method Methods 0.000 claims abstract description 38
- 238000012800 visualization Methods 0.000 claims abstract description 16
- 238000013507 mapping Methods 0.000 claims abstract description 14
- 239000011159 matrix material Substances 0.000 claims abstract description 14
- 238000010606 normalization Methods 0.000 claims abstract description 13
- 239000002131 composite material Substances 0.000 claims abstract description 9
- 238000012545 processing Methods 0.000 claims abstract description 9
- 238000004590 computer program Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims 2
- 230000000694 effects Effects 0.000 abstract description 6
- 238000001514 detection method Methods 0.000 abstract description 2
- 230000000007 visual effect Effects 0.000 description 8
- 230000006872 improvement Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 239000003086 colorant Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000003786 synthesis reaction Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 230000001427 coherent effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/04—Display arrangements
- G01S7/046—Display arrangements using an intermediate storage device, e.g. a recording/reproducing device
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9021—SAR image post-processing techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/024—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using polarisation effects
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a polarization target three-component decomposition result color visualization method and a polarization target three-component decomposition result color visualization system, wherein the method comprises the following steps: reading in full polarization synthetic aperture radar data to obtain a target coherence matrix [ T ]]The method comprises the steps of carrying out a first treatment on the surface of the For target coherence matrix [ T ]]Freeman-Durden three-component decomposition is performed, resulting in three power components: surface scattering power component P s Dihedral angle scattered power component P d And the volume scattering component P v The method comprises the steps of carrying out a first treatment on the surface of the For three power components P s 、P d And P v Normalizing; calculating a total scattering power Span; carrying out rapid median iteration normalization processing on the total scattered power Span to obtain Span'; and selecting a color model and a corresponding color channel mapping scheme according to the requirements, and displaying the pseudo-color composite image. The method can effectively enhance the color visualization effect of the three-component decomposition result of the polarized target of the full-polarized synthetic aperture radar data, increase the readability of images and promote the detection, identification and classification of the target.
Description
Technical Field
The invention relates to the field of polarized target decomposition result visualization, in particular to a polarized target three-component decomposition result color visualization method and a polarized target three-component decomposition result color visualization system.
Background
In analysis of polarized synthetic aperture radar (Polarimetric Synthetic Aperture Radar, polSAR) data, model-based polarized target incoherent decomposition is an effective and popular tool, which is an important component of target decomposition theory, and has the advantages of clear physical interpretation, convenient implementation, easy visual interpretation, and the like. The method aims at representing measured multi-view polarized SAR data as a combination of different scattering mechanism models, each model describing an actual physical scattering process. In 1998, freeman and Durden were the earliest to propose a three-component target decomposition method, which opened the way of model-based polarization target decomposition (A.Freeman, S.L.Durden.A three-component scattering model for polarimetric SAR [ J ]. IEEE Transactions on Geo-science and Remote Sensing,1998,36 (3): 963-973). From this point, as one of the important means for extracting radar echo target information, the method has made a lot of new progress, and has remained a focus of attention in the field of polarized target decomposition research so far.
The rapid median iteration normalization technology can adjust the dynamic range of the synthetic aperture radar image, increase the target detail and improve the image display effect through three operations of rapid median iteration processing, logarithmic processing and maximum value-minimum value normalization processing.
According to the definition of the total scattering power Span, the total scattering power Span can be understood as incoherent superposition of the power of each polarized channel, in the process, a certain speckle suppression effect can be obtained, and in addition, the Span in a reasonable dynamic range can better keep the edge, texture, size and shape information of polarized SAR data, so that the Span can be integrated into the display of the polarized SAR image.
Polarization target decomposition may extract polarization characteristic parameters from the polarization data that are closely related to the nature of the target itself. The effective presentation and visual depiction of the parameters has great significance for applications such as subsequent polarized SAR image interpretation, target classification, detection and identification and the like. The traditional method for visualizing the polarized SAR target decomposition result is to display the decomposition result in the form of a gray image for visual interpretation, so that the formed polarized SAR image has poor readability and an unsatisfactory visual effect, and is not beneficial to later analysis and understanding. Human eyes vary color compared to gray scale
The method is more sensitive, so that the polarized target decomposition result can be displayed in a color image mode, the indistinguishable gray scale difference in the gray scale image can be converted into the color difference which is easy to distinguish by human eyes, more target information is reflected, and visual interpretation of the image is facilitated. The color space pseudo-color processing, namely color visualization, is a polarization target decomposition result visualization means with better effect developed on the basis of the traditional gray image visualization. Color visualization is chosen primarily for two reasons: firstly, the color is used as a powerful drawing, so that more target information and details in a polarized target decomposition result can be mined and reflected, and the polarized SAR image space is remarkably enriched, thereby being beneficial to target identification and distinction; secondly, the human eyes are more sensitive to the difference and change of the colors, namely, the human eyes have stronger distinguishing ability of the colors. Color visualization can improve the resolution of the image, thereby facilitating visual analysis and understanding of the image by the human eye. The color spaces commonly used are: RGB, HSV, HSI, CIELab, etc. Many scholars at home and abroad develop related researches aiming at color space color fusion of the decomposition result of the polarization target. One of the most widely used conventional methods is to generate a pseudo-color composite map by assigning it to three channels R, G, B of the RGB color space, respectively, according to the feature object attributes represented by the polarization object decomposition results. For example, in Freeman-Durden three-component decomposition, P is directly decomposed s To blue channel, directly P d Assigned to red channel, directly let P v Giving the green channel.
Disclosure of Invention
The invention aims to enhance the color visualization effect of the decomposition result of a polarized target and promote the interpretation of a polarized synthetic aperture radar image.
In order to achieve the above object, the present invention provides a polarization target three-component decomposition result color visualization method, the method comprising:
reading in full polarization synthetic aperture radar data to obtain a target coherence matrix [ T ];
for target coherence matrix [ T ]]Freeman-Durden three-component decomposition is performed, resulting in three power components: surface scattering power component P s Dihedral angle scattered power component P d And the volume scattering component P v ；
For three power components P s 、P d And P v Normalizing;
calculating a total scattering power Span;
carrying out rapid median iteration normalization processing on the total scattered power Span to obtain Span';
and selecting a color model and a corresponding color channel mapping scheme according to the requirements and displaying the pseudo-color composite image.
As an improvement of the above method, the pair of three power components P s 、P d And P v Normalizing; the method specifically comprises the following steps:
wherein P' s 、P′ d And P' v Representing normalized surface, dihedral and bulk scattered power components, respectively.
As an improvement of the above method, the calculating total scattered power Span; the method specifically comprises the following steps:
the target coherence matrix [ T ] is:
the total scattered power Span is:
Span＝T 11 +T 22 +T 33 。
as an improvement of the above method, the selecting a color model and a corresponding color channel mapping scheme according to the requirement, displaying a pseudo color composite image specifically includes:
when the selected color model is an RGB model, the mapping scheme is:
normalized power component P' d 、P′ v And P' s Multiplying Span after the fast median iteration normalization treatment, namely Span', and the following formula:
will beP s RGB And P v RGB Respectively mapped to the red, green and blue channels, as follows:
when the selected color model is the HSI model, the mapping scheme is:
according to normalized power component P' d 、P′ v And P' s The hue component H in the HSI model is calculated by the following method:
according to normalized power component P' d 、P′ v And P' s The saturation component S in the HSI model is calculated by the following method:
span' is assigned to the luminance component I in the HSI color space as follows:
I＝Span′
based on (H, S, I), the HSI color space is converted back to the RGB color space for display.
Embodiment 2 of the present invention proposes a polarization target three-component decomposition result color visualization system, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the above method when executing the computer program.
The invention has the advantages that:
the method and the system respectively provide two new decomposition result color visualization schemes based on two object-oriented different color models, and provide a new thought for the rapid improvement of the visual effect of the polarized synthetic aperture radar image based on different display requirements.
Drawings
FIG. 1 is a flow chart of a polarized target three-component decomposition result color visualization method of the present invention;
FIG. 2 is an optical image of Google in the Oberpfaffenhofen area of Germany;
FIG. 3 is a pseudo-color composite diagram of the Freeman-Durden three-component decomposition result of fully polarized synthetic aperture radar data using a conventional RGB direct mapping color visualization method;
FIG. 4 is a pseudo-color composite map based on RGB color space of Freeman-Durden three-component decomposition results of fully polarized synthetic aperture radar data obtained by the method of the present invention;
FIG. 5 is a pseudo-color composite plot based on the HSI color space of Freeman-Durden three-component decomposition results of fully polarized synthetic aperture radar data obtained by the method of the present invention.
Detailed Description
The technical scheme of the invention is further described with reference to the accompanying drawings.
Referring to fig. 1, embodiment 1 of the present invention proposes a polarization target three-component decomposition result color visualization method, comprising the steps of:
step 1) reading in full polarization synthetic aperture radar data to obtain a target coherence matrix [ T ];
step 2) target coherence matrix [ T ]]Freeman-Durden three-component decomposition is performed, resulting in three power components: surface scattering power component P s Dihedral angle scattered power component P d And the volume scattering component P v ；
Step 3) for three power components P s ，P d And P v Normalizing;
step 4) calculating a total scattering power Span;
step 5), carrying out rapid median iteration normalization processing on the total scattered power Span to obtain Span';
and 6) selecting a color model and a corresponding color channel mapping scheme according to the requirement and displaying a pseudo-color image.
The steps in the method of the present invention are further described below.
In the step 1), reading in full polarization synthetic aperture radar data to obtain a coherent matrix [ T ] of a target; in one embodiment, the data read in is full polarization data acquired by the L-band on-board ESAR developed by DLR in the Oberpfaffenhofen region of Germany.
Based on the coherence matrix [ T ] of the object obtained in step 1)]In step 2), we apply to [ T ]]Freeman-Durden three-component decomposition is performed, resulting in corresponding three power components: surface scattering power component P s Dihedral angle scattered power component P d And the volume scattering component P v 。
Based on the three power components obtained by the three-component decomposition of the target coherence matrix in the embodiment obtained in step 2), in step 3), the three power components P s ，P d And P v Normalization was performed as follows:
wherein P' s 、P′ d And P' v Representing normalized surface, dihedral and bulk scattered power components, respectively.
In step 4), based on the definition of the coherence matrix [ T ] and the total scattered power Span of the target obtained in step 1), the total scattered power Span corresponding to the embodiment is calculated as follows:
Span＝T 11 +T 22 +T 33
in step 5), the total scattered power Span corresponding to the embodiment is processed by using a fast median iteration normalization processing technology to obtain Span', namely, the fast median iteration operation is performed first, then the logarithmic operation is performed, and finally the maximum-minimum normalization operation is performed.
In step 6), the color model to be used is first selected according to the display requirements: RGB or HSI, then carrying out component mapping according to a color channel mapping scheme corresponding to the selected color model:
when the RGB model is selected, the normalized power component P 'is first normalized' d 、P′ v And P' s Multiplying Span after the fast median iteration normalization treatment, namely Span', and the following formula:
and then will beP s RGB And P v RGB Respectively mapped to the red, green and blue channels, as follows:
finally, carrying out RGB pseudo-color synthesis on the three channels to obtain corresponding polarized synthetic aperture radar RGB pseudo-color images;
when the HSI model is selected,first by normalizing the power component P' d 、P′ v And P' s The hue component H in the HSI model is calculated as follows:
and then normalize the power component P' d 、P′ v And P' s The saturation component S in the HSI model is calculated by the following method:
span' is then assigned to the luminance component I in the HSI color space as follows:
I＝Span′
and finally, converting the HSI color space back to the RGB color space to obtain the polarized synthetic aperture radar pseudo-color image corresponding to the embodiment.
Fig. 2 is a google optical image of the obenpfaffenhofen area. Fig. 3 is a pseudo-color synthetic diagram of the Freeman-Durden three-component decomposition result of the full-polarization synthetic aperture radar data obtained by the conventional RGB direct mapping color visualization method, and it can be seen that the diagram has a poor display effect due to the problems of large dynamic range, etc., and the displayed target ground object information is limited and the details are not clear. In contrast, fig. 4 and fig. 5 are pseudo-color synthesis graphs based on RGB color models and HSI color models respectively of the Freeman-Durden three-component decomposition result obtained by the method of the present invention, where the total scattered power Span rapid median iteration normalization technique processes the selected iteration number k=5. The two images can be seen to have obviously enhanced display effect, obviously increased target details and characteristics, and can clearly distinguish targets such as runways and tarmac of buildings, forests, roads, residential areas and airport areas, and the effectiveness of the proposed color visualization scheme is proved. In addition, compared with the pseudo-color synthetic image obtained based on the RGB color model, the pseudo-color synthetic image obtained based on the HSI color model is more in line with the habit of human eyes, and the visual effect is better. The size of the images is 1300×1200.
Embodiment 2 of the present invention proposes a polarization target three-component decomposition result color visualization system, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of embodiment 1 when executing the computer program.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and are not limiting. Although the present invention has been described in detail with reference to the embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the appended claims.
Claims (5)
1. A method for color visualization of a polarization target three-component decomposition result, the method comprising:
reading in full polarization synthetic aperture radar data to obtain a target coherence matrix [ T ];
for target coherence matrix [ T ]]Freeman-Durden three-component decomposition is performed, resulting in three power components: surface scattering power component P s Dihedral angle scattered power component P d And the volume scattering component P v ；
For three power components P s 、P d And P v Normalizing;
calculating a total scattering power Span;
carrying out rapid median iteration normalization processing on the total scattered power Span to obtain Span';
and selecting a color model and a corresponding color channel mapping scheme according to the requirements, and displaying the pseudo-color composite image.
2. According to claimThe method for visualizing a three-component decomposition result of a polarized object according to claim 1, wherein said pair of three power components P s 、P d And P v Normalizing; the method specifically comprises the following steps:
wherein P is s ′、P′ d And P' v Representing normalized surface, dihedral and bulk scattered power components, respectively.
3. The method for visualizing the three-component decomposition result color of a polarized target according to claim 2, wherein said calculating a total scattered power Span; the method specifically comprises the following steps:
the target coherence matrix [ T ] is:
the total scattered power Span is:
Span＝T 11 +T 22 +T 33 。
4. the method for visualizing the three-component decomposition result color of a polarized object according to claim 3, wherein said selecting a color model and a corresponding color channel mapping scheme according to the requirement displays a pseudo-color composite image, specifically comprising:
when the selected color model is an RGB model, the mapping scheme is:
normalized power component P' d 、P′ v And P s 'multiplied by Span after the fast median iterative normalization process, span', as follows:
will beAnd->Respectively mapped to the red, green and blue channels, as follows:
when the selected color model is the HSI model, the mapping scheme is:
according to normalized power component P' d 、P′ v And P s ' calculate the hue component H in the HSI model, the calculation method is as follows:
according to normalized power component P' d 、P′ v And P s ' calculate saturation component S in HSI model, the calculation method is as follows:
span' is assigned to the luminance component I in the HSI color space as follows:
I＝Span′
based on (H, S, I), the HSI color space is converted back to the RGB color space for display.
5. A polarization target three-component decomposition result color visualization system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1-4 when executing the computer program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011224654.4A CN112558017B (en) | 2020-11-05 | 2020-11-05 | Polarization target three-component decomposition result color visualization method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011224654.4A CN112558017B (en) | 2020-11-05 | 2020-11-05 | Polarization target three-component decomposition result color visualization method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112558017A CN112558017A (en) | 2021-03-26 |
| CN112558017B true CN112558017B (en) | 2023-08-08 |
Family
ID=75042879
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011224654.4A Active CN112558017B (en) | 2020-11-05 | 2020-11-05 | Polarization target three-component decomposition result color visualization method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112558017B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113436110B (en) * | 2021-07-16 | 2022-06-14 | 厦门大学 | Method for performing pseudo-color processing on synthetic aperture radar gray level image |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6603424B1 (en) * | 2002-07-31 | 2003-08-05 | The Boeing Company | System, method and computer program product for reducing errors in synthetic aperture radar signals |
| CN101344587A (en) * | 2008-08-15 | 2009-01-14 | 哈尔滨工业大学 | Multi-component decomposition method used for polarization synthetic aperture radar image |
| CN103020975A (en) * | 2012-12-29 | 2013-04-03 | 北方工业大学 | Wharf and ship segmentation method combining multi-source remote sensing image characteristics |
| CN103761752A (en) * | 2014-01-13 | 2014-04-30 | 中国科学院电子学研究所 | Method and device for processing PolSAR image |
| CN104217410A (en) * | 2014-09-19 | 2014-12-17 | 西安电子科技大学 | Fully polarized SAR (Synthetic Aperture Radar) pseudo color drawing synthesis method |
| CN104751184A (en) * | 2015-04-08 | 2015-07-01 | 西安电子科技大学 | Fully polarimetric SAR image classification method based on sparse strength statistics |
| CN108364011A (en) * | 2018-02-05 | 2018-08-03 | 中国民航大学 | PolSAR image multi-stage characteristics extract and unsupervised segmentation method |
| CN110020693A (en) * | 2019-04-15 | 2019-07-16 | 西安电子科技大学 | The Classification of Polarimetric SAR Image method for improving network with feature is paid attention to based on feature |
| CN110458876A (en) * | 2019-08-08 | 2019-11-15 | 哈尔滨工业大学 | Multidate POLSAR method for registering images based on SAR-SIFT feature |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106778884A (en) * | 2016-12-23 | 2017-05-31 | 辽宁工程技术大学 | The Classification of Polarimetric SAR Image method of plane is decomposed based on scattering entropy and three-component |
-
2020
- 2020-11-05 CN CN202011224654.4A patent/CN112558017B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6603424B1 (en) * | 2002-07-31 | 2003-08-05 | The Boeing Company | System, method and computer program product for reducing errors in synthetic aperture radar signals |
| CN101344587A (en) * | 2008-08-15 | 2009-01-14 | 哈尔滨工业大学 | Multi-component decomposition method used for polarization synthetic aperture radar image |
| CN103020975A (en) * | 2012-12-29 | 2013-04-03 | 北方工业大学 | Wharf and ship segmentation method combining multi-source remote sensing image characteristics |
| CN103761752A (en) * | 2014-01-13 | 2014-04-30 | 中国科学院电子学研究所 | Method and device for processing PolSAR image |
| CN104217410A (en) * | 2014-09-19 | 2014-12-17 | 西安电子科技大学 | Fully polarized SAR (Synthetic Aperture Radar) pseudo color drawing synthesis method |
| CN104751184A (en) * | 2015-04-08 | 2015-07-01 | 西安电子科技大学 | Fully polarimetric SAR image classification method based on sparse strength statistics |
| CN108364011A (en) * | 2018-02-05 | 2018-08-03 | 中国民航大学 | PolSAR image multi-stage characteristics extract and unsupervised segmentation method |
| CN110020693A (en) * | 2019-04-15 | 2019-07-16 | 西安电子科技大学 | The Classification of Polarimetric SAR Image method for improving network with feature is paid attention to based on feature |
| CN110458876A (en) * | 2019-08-08 | 2019-11-15 | 哈尔滨工业大学 | Multidate POLSAR method for registering images based on SAR-SIFT feature |
Non-Patent Citations (1)
| Title |
|---|
| 基于改进三分量模型的全极化SAR图像分类;徐一凡 等;电子测量技术;第40卷(第12期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112558017A (en) | 2021-03-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108629835B (en) | Indoor reconstruction method and system based on hyperspectral, true color image and point cloud complementation | |
| Yuan et al. | Remote sensing image segmentation by combining spectral and texture features | |
| US10635929B2 (en) | Saliency-based method for extracting road target from night vision infrared image | |
| Huang et al. | A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas | |
| CN103247059B (en) | A kind of remote sensing images region of interest detection method based on integer wavelet and visual signature | |
| CN103177458B (en) | A kind of visible remote sensing image region of interest area detecting method based on frequency-domain analysis | |
| CN106815601A (en) | Hyperspectral image classification method based on recurrent neural network | |
| CN104103082A (en) | Image saliency detection method based on region description and priori knowledge | |
| JP2012517652A (en) | Fusion of 2D electro-optic images and 3D point cloud data for scene interpolation and registration performance evaluation | |
| JP2012517650A (en) | Method and system for visualizing point cloud data based on scene content | |
| CN102842044B (en) | Method for detecting variation of remote-sensing image of high-resolution visible light | |
| CN111027497B (en) | Weak and small target rapid detection method based on high-resolution optical remote sensing image | |
| CN103093478B (en) | Based on the allos image thick edges detection method of quick nuclear space fuzzy clustering | |
| CN109740631B (en) | OBIA-SVM-CNN remote sensing image classification method based on object | |
| Casanova et al. | Texture analysis using fractal descriptors estimated by the mutual interference of color channels | |
| Im et al. | An automated binary change detection model using a calibration approach | |
| CN101403795A (en) | Remote sensing survey method and system for estimating tree coverage percentage of city | |
| CN105405132A (en) | SAR image man-made target detection method based on visual contrast and information entropy | |
| Shaoqing et al. | The comparative study of three methods of remote sensing image change detection | |
| CN102063627B (en) | Method for recognizing natural images and computer generated images based on multi-wavelet transform | |
| CN109308451A (en) | A kind of high score data information extraction system and method | |
| CN112558017B (en) | Polarization target three-component decomposition result color visualization method and system | |
| CN100589520C (en) | Detection method for characteristic of edge and angle point of color image | |
| Xu et al. | Vegetation information extraction in karst area based on UAV remote sensing in visible light band | |
| Chen et al. | Scene segmentation of remotely sensed images with data augmentation using U-net++ |
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 |