CN1431523A - Optimization method of remote sensing image by use of syncretic bilinear interpolation and wavelet transformation - Google Patents
Optimization method of remote sensing image by use of syncretic bilinear interpolation and wavelet transformation Download PDFInfo
- Publication number
- CN1431523A CN1431523A CN 03114921 CN03114921A CN1431523A CN 1431523 A CN1431523 A CN 1431523A CN 03114921 CN03114921 CN 03114921 CN 03114921 A CN03114921 A CN 03114921A CN 1431523 A CN1431523 A CN 1431523A
- Authority
- CN
- China
- Prior art keywords
- image
- data block
- wavelet
- high spectrum
- remote sensing
- 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
Images
Landscapes
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
The re-sampling optimization calculation is carried out for high spectrum image by using the bilinear interpolation arithmetic formula to make its spatial resolution equal to the full color image. The wavelet decompose is carried out for the full color image and the re-sampled high spectrum image to obtain the each approximate low frequency and detailed high frequency components. The wavelet decompose approximate components of the high spectrum image are as the approximate components of the new image of the wavelet coefficients syncretic remote sensing images. The linear computation obtains the syncretic detailed components. Based on the wavelength coefficient of the new image data blocks obtained by syncretized coefficients, the wavelet restoration and the data block merge are carried out to generate the remote sensing syncretized image.
Description
Technical field:
The present invention relates to the optimization method that a kind of remote sensing images bilinear interpolation wavelet transformation merges, be that high-spectrum remote sensing is after bilinearity resamples, the fusion optimization method that carries out high precision when merging and calculate fast with the high spatial resolution full-colour image, be a core technology of multi-source remote sensing image post-processing stages, in fields such as all kinds of military or civilian Remote Sensing Information Processing System, digital city space information system, all can be widely used.
Background technology:
Two during the last ten years, and remote sensing technology has obtained fast development, and various multi-platform, multidates towards the complicated applications background, multispectral and high resolution ratio satellite remote-sensing system be emerge in multitude thereupon also, and it is widely used in fields such as the classification of surface vegetation and environmental observation.In order to carry out categorised decision or increase bearing accuracy more accurately, very urgent to the demand of high spatial and spectral resolution image.Desire realizes this target, and two approach are arranged: on the one hand, adopt the remote sensor of high spatial and spectral resolution, for example the ground resolution of the XS1 of remote sensor, XS3 wave band is 10m * 10m on the French SPOT satellite; The PAN image ground resolution of its IPS is 5.8m * 5.8m.The 1CLISS III of IPS then is 23.5 * 23.5m.Panchromatic (PAN) image ground resolution of American I KONOS satellite is 1m * 1m, wavelength 0.45-0.90 μ m; Multispectral image is blue 0.45-0.52 μ m, green 0.52-0.60 μ m, red 0.63-0.69 μ m, near infrared 0.69-0.90 μ m for 4m * 4m, its wavelength coverage then, and also the spectrum with the 1-4 wave band of Landsat LAND SAT is identical for this.And on the other hand, then utilize image processing techniques, promptly carry out the fusion of different spectrum and spatial resolution remote sensing images, or claim the multispectral image spatial resolution to strengthen the sharpening (Sharpening) of (Enhancement of the Spatial resolution ofMulti-spectral Image), multispectral image, can save a large amount of reasearch funds.Therefore, based on the Theory of Information Fusion of multisensor, the multi-source remote sensing image information merges (Remote Sensing ImageInformation Fusion) research to be convenient to arise at the historic moment the eighties in 20th century.
In general, remote sensing image fusion is exactly the image synthetic technology, different spaces and spectral resolution image same or that different sensors is obtained on the different platform (satellite and airborne) are handled by specific algorithm, so that the new images that is produced has the multispectral characteristic and the high ground resolution of original image simultaneously, realize different application demands.At present, use more remote sensing image fusion and be the fusion of multispectral (Multi-Spectral) with PAN (Panchromatic) image co-registration or high spectrum (Hyper-Spectral) image and the PAN image of different resolution, main method has IHS conversion, principal component analysis (PCA) (PCA), BROVERY conversion and wavelet transformation to close etc.
These blending algorithms are concluded, and their common trait is all high spectrum image to be resampled for the new images that equates with the full-colour image spatial resolution, for further processing before the fusion.For this reason, the problem that faces two aspects needs to solve:
1. high spectrum image resampling algorithm is to the spectrum and the spatial resolution influence of fused images;
2. because the size of high spectrum image is generally the order of magnitude of hundreds of million, when becoming complexity, must consider the arithmetic speed of computing machine as if its resampling algorithm.
At present, when carrying out above-mentioned remote sensing image fusion, under the condition of precision permission, mainly use neighbour's interpolation method, to satisfy the processing speed requirement of algorithm.Here the principle of neighbour's interpolation algorithm of Cai Yonging is, in the pixel interpolation search process, if the locus of certain vegetarian refreshments certain pixel in the new images of resampling algorithm gained is nearest in the high spectrum image, then with the pixel value of its pixel value as the new images corresponding point.This method generally can satisfy the Flame Image Process requirement.But if require spectral information loss hour, then it is difficult to the fusion accuracy that reaches required, traces it to its cause, and is because the limitation of nearest neighbor algorithm itself causes, so need study new algorithm.
Summary of the invention:
The objective of the invention is to above-mentioned deficiency at prior art, the optimization method that provides a kind of remote sensing images bilinear interpolation wavelet transformation to merge, spectral information that not only can the original high spectrum image of the most effective maintenance, and can also be when improving the spectral remote sensing image spatial resolution, the operational speed of a computer is accelerated, satisfied application demand.
For realizing such purpose, in the technical scheme of the present invention, the development language that utilizes remote sensing image processing and development platform to provide, file size and computer processing speed at high spectrum image, adopt the bilinear interpolation formula to the high spectrum image computation optimization that resamples, make its spatial resolution equal full-colour image.Then, according to wavelet decomposition theory to full-colour image with resample after high spectrum image carry out wavelet decomposition, obtain separately approximate low frequency and details high fdrequency component.When the wavelet coefficient of remote sensing images merged, with the approximate component of the wavelet decomposition of the high spectrum image approximate component as new images, and the details component merged, and then utilizes linear calculating to obtain, and helps keeping the spectral information of high spectrum image like this.At last, the wavelet coefficient according to wavelet coefficient fusion gained new image data piece carries out the splicing of wavelet reconstruction and data block, generates required remote sensing fused images.
Method of the present invention comprises following concrete steps:
1. the extraction of data characteristic: according to demands of applications, high spectrum and panchromatic remote sensing to be merged behind the acquisition registration
Image file by the interface function that remote sensing image processing and development platform provide, is obtained their characteristic
Data comprise file size, the wave band number of spatial resolution and image high-spectrum remote sensing etc.
2. the bilinearity of high spectrum image resamples: the spatial resolution with full-colour image is a standard, obtains computing machine
Internal memory and index such as CPU processing speed after, according to the big young pathbreaker of the data volume of high spectrum file correspondence its
Be divided into the data block (number is calculator memory amount/high spectrum image data volume) of several successive, press two-wire
The property interpolation rule, to each data block respectively pointwise search for, interpolation, generate and have and the full-colour image sky
Between the identical new peak spectral image data piece of resolution.
3. the deblocking of full-colour image according to corresponding deblocking number of second step, is divided into full-colour image and warp
The data block that the new peak spectral image data piece number of gained equates after the bilinear interpolation.
4. corresponding panchromatic and high spectrum image data block is called in circulation, and the 2-d wavelet that carries out each data block decomposes.
Independently of one another carry out one time one dimension to panchromatic with the high spectrum image data block in level and vertical direction exactly
Wavelet transformation.Be equivalent in the horizontal direction image be carried out low pass and high-pass filtering respectively earlier, and adopt under doing
Sample is implemented same operation then in vertical direction.The final low-frequency approximation branch that obtains under each yardstick
The high frequency details component of amount and three directions (level, vertical, oblique line), they constitute the little of corresponding data piece
The wave system number.
5. the wavelet coefficient of high spectrum and each data block of full-colour image merges: the low frequency of high spectrum image data block is near
Like the low frequency component of component as the fusion image data piece, and the high fdrequency component of fusion image data piece, then
Use the linear weighted function algorithm to obtain with the high frequency details component high spectrum image data block, thus structure by panchromatic
Build the wavelet coefficient (low frequency and high fdrequency component) of new images.
6. the splicing of reconstruction of the wavelet transformation of fusion image data piece and data block: with the fusion image data piece that obtains
Wavelet coefficient press the reconfiguration principle of wavelet transformation, carry out image reconstruction, obtain required high-space resolution
The data block of rate and spectral resolution is then with the cut apart order of these data blocks by aforementioned high spectrum image
Splice, form new images.
Characteristic of the present invention is, owing to differ greatly on the characteristics such as the spatial resolution of high spectrum of areal and panchromatic remote sensing images and spectral resolution, after by the bilinearity rule high spectrum image being resampled, utilize wavelet transformation to merge, in the spectral information that keeps high spectrum image, can improve its spatial resolution, make the demonstration that atural object space structure and texture features can be clearer and more definite.Simultaneously,, each data block is called in calculator memory successively, can improve Computer Processing speed significantly owing to carried out deblocking.In a word, the method that the present invention proposes can be widely used in the fusion treatment of remote sensing image data, obtains fused image quality more reliably.
Description of drawings:
Fig. 1 is the The general frame of disposal route proposed by the invention.
As shown in Figure 1, the interface function that at first utilizes ENVI/IDL remote sensing image processing and development platform based on Windows to provide obtains the performance data of high spectrum and full-colour image.High spectrum image is carried out each piece being implemented bilinearity again behind the deblocking according to a certain percentage resample, make the spatial resolution of each data block equal the spatial resolution of full-colour image.After this, in identical ratio full-colour image is carried out deblocking.Circulation is called in panchromatic and the back high spectrum image data block that resamples, and each corresponding data block is carried out wavelet decomposition, obtains the low-frequency approximation component and the high frequency details component of wavelet decomposition.The data block that circulation is called in after the wavelet decomposition is carried out the wavelet coefficient fusion, the wavelet coefficient of structure new image data piece.At last, the new image data piece is carried out the splicing of wavelet transformation reconstruction and data block, obtain required fused images.
Fig. 2 is the employed interpolation model of bilinearity resampling algorithm.
Fig. 3 is panchromatic and the computation process synoptic diagram of high spectrum image corresponding data piece wavelet transformation fusion.C wherein
a p, C
h p, C
v p, C
d pBe respectively the vertical and high frequency oblique line component of low frequency component, high frequency level, high frequency after the full-colour image data block wavelet decomposition;
C
a m, C
h m, C
v m, C
d mBe respectively resample the vertical and high frequency oblique line component of low frequency component, high frequency level, high frequency after the data block wavelet decomposition of back of high spectrum image.
Fig. 4 is the syncretizing effect contrast of method and other method of the present invention's employing.
Wherein, the Land TM image of Fig. 4 (a) is a high spectrum image, and present embodiment is got its wave band 4,3,2, file size 1007 * 560, spatial resolution 28m.The SPOT remote sensing image of Fig. 4 (b) is a full-colour image, file size 2820 * 1569, basement resolution 10m.Fig. 4 (c) is neighbour's interpolating wavelet syncretizing effect, and Fig. 4 (d) is a bilinear interpolation small echo syncretizing effect, and therefrom as can be seen, the bilinear interpolation wavelet transformation merges the spectral information that helps keeping high spectrum image.
Embodiment:
In order to understand technical scheme of the present invention better, embodiments of the present invention are further described below in conjunction with accompanying drawing.
A kind of The general frame that is used for high spectrum and full-colour image bilinear interpolation wavelet transformation fusion optimization method that Fig. 1 proposes for the present invention, the input of data file is that the Land TM image of Fig. 4 (a) is a high spectrum image, get its wave band 4,3,2, file size 1007 * 560, spatial resolution 28m.The SPOT remote sensing image of Fig. 4 (b) is a full-colour image, file size 2820 * 1569, spatial resolution 10m.The concrete implementation detail of each several part is as follows:
1. the extraction of data characteristic, the performance data that obtains high spectrum and full-colour image is utilized the ENVI DISPLAY BANDS function of ENVI/IDL development platform, can demonstrate size, wave band number and the space branch resolution etc. of file.
2. the bilinearity of high spectrum image resamples, utilize the C language interface of ENVI/IDLIDL to call the api function that WINDOWS2000 operating system provides, obtain the ardware feature of computing machine, behind CPU processing speed, memory size, high spectrum image is carried out the bilinearity resampling and carries out the piecemeal of high-spectral data file in the ratio of internal memory/high spectrum file data amount in method shown in Figure 2.
For certain pixel in the high spectrum image (x, y) and on every side point (0,0), (0,1), (1,0), (1,1), the gradation of image value of their correspondences be respectively f (x, y), f (0,0), f (0,1), f (1,0) and f (1,1), then interpolation process is:
At first, draw f (x, 0) by the first-order linear interpolation:
f(x，0)＝f(0，0)+x[f(1，0)-f(0，0)] (1)
Similarly, f (x, 1) is carried out the first-order linear interpolation:
f(x，1)＝f(0，1)+x[f(1，1)-f(0，1)] (2)
At last, vertical direction is carried out the first-order linear interpolation, with determine f (x, y)
f(x，y)＝f(x，0)+x[f(x，1)-f(x，0)] (3)
3. the deblocking of full-colour image
Numerical value by the calculator memory amount/high spectrum image data volume that obtains in second step is divided into the data block that equates with the high spectrum image number of data blocks to full-colour image.
4. corresponding panchromatic and high spectrum image data block is called in circulation, carry out successively each data block wavelet decomposition.
Fig. 3 provides each succinct data block small echo to merge the level framework, wherein panchromatic and high-spectral data piece is divided into two parts: approximate component (Approximate) and details component (Detail), and the latter can be divided into level, vertical and oblique line component, and calculating formula is:
If ψ (t) ∈ is L
2(R) (L
2(R) represent square-integrable real number space, i.e. the limited signal space of energy), its Fourier transform is ψ
*(ω), work as ψ
*(ω) satisfy enabled condition:
The time, claim that ψ (t) is a basic small echo or female small echo, it after flexible and translation, just can be obtained a small echo sequence ψ
A, b(t):
A, b ∈ R; A is a scale factor in a ≠ 0 (5) formula, and b is a shift factor.
For arbitrary function f (t) ∈ L
2(R) continuous wavelet transform W
f(a b) has
In the formula, tape symbol "-" expression complex conjugate.
At present, formed the little wave system of multiple practicality, they respectively possess some good points, and in this programme, select the little wave system of Symlet for use.
5. each data block wavelet coefficient of high spectrum and full-colour image merges
After above-mentioned each data block wavelet decomposition of calling in calculator memory successively, carry out wavelet coefficient and merge.If the low frequency component of the wavelet coefficient of fusion image data piece is C
a *, high fdrequency component is respectively C
h *, C
v *, C
d *, then, the calculating formula that new image data piece wavelet coefficient adopts is:
A in the formula
x, b
xBe respectively in the pixel of data block, from C
x PTo C
x MThe slope of straight line and intercept, the then desirable h of x, v, d, m represents high spectrum image, p represents full-colour image.
6. the wavelet transformation of fusion image data piece is rebuild and the data block splicing
The pixel value of fusion image data piece see as function f (x, y), f (x, y) ∈ V then
j 2(j ∈ Z), it is at V
j 2The space be projected as A
jF (x, y), V here
j 2Be L
2(R) a multiscale space subdivision is so have
A
jF (x, y)=A
J+1F (x, y)+D
J+1 1F (x, y)+D
J+1 2F (x, y)+D
J+1 3(x is y) in (8) formula for f
i＝1，2，3
C
J+1(m
1, m
2)---the fusion image data piece is at scale-of-two grid 2
J+1Discrete approximation under the yardstick (low frequency profile)
D
J+1 i(m
1, m
2)---the fusion image data piece is at scale-of-two grid 2
J+1Discrete details under the yardstick (high frequency texture) corresponds respectively to level, vertical and oblique line component.
(m
1, m
2)---expression fusion image data piece locations of pixels
Use H
r, H
cAnd G
r, G
cRepresent the mirror image conjugate filter respectively, H, G act on array { C
j(m
1, m
2), (m
1, m
2) ∈ Z
2Row and column, then the second order Mallat decomposition computation formula used of this programme is
Correspondingly, its reconstruction formula is:
H, G are respectively the conjugation of H, G in the formula.
After the fusion image data piece wavelet reconstruction, carry out data splicing, can obtain required fused images according to the order that original high spectrum image is cut apart.
Claims (1)
1, the optimization method that merges of a kind of remote sensing images bilinear interpolation wavelet transformation is characterized in that comprising following concrete steps: after 1) obtaining high spectrum and panchromatic remote sensing images file to be merged behind the registration, by remote sensing image processing and exploitation
The interface function that platform provides is obtained their performance data, comprises file size, spatial resolution and
The wave band number of image high-spectrum remote sensing etc.; 2) spatial resolution with full-colour image is a standard, obtains indexs such as the internal memory of computing machine and CPU processing speed,
It presses calculator memory amount/high spectrum image data volume according to the big young pathbreaker of the data volume of high spectrum file correspondence
Ratio value be divided into the data block of several successive, to each data block respectively pointwise carry out bilinearity and resample,
Generation has the new peak spectral image data piece identical with the full-colour image spatial resolution; 3) full-colour image is divided into the number that equates with the new peak spectral image data piece number of gained after bilinear interpolation
According to piece; 4) corresponding panchromatic and high spectrum image data block is called in circulation, and the 2-d wavelet that carries out each data block decomposes, promptly
In level and vertical direction it is carried out one-dimensional wavelet transform independently of one another one time, earlier in the horizontal direction to figure
Picture carries out low pass and high-pass filtering respectively, and makes down-sampling, implements same operation then in vertical direction,
Obtain low frequency component and the level under each yardstick, the high fdrequency component of vertical, three directions of oblique line, it is right to constitute
Answer the wavelet coefficient of data block; 5) wavelet coefficient that carries out high spectrum and each data block of full-colour image merges, with the low frequency of high spectrum image data block
Component is as the low frequency component of fusion image data piece, and the high fdrequency component of fusion image data piece is then used
The linear weighted function algorithm obtained by panchromatic and the high frequency coefficient high spectrum image data block, makes up new images thus
Wavelet coefficient; 6) wavelet coefficient of the fusion image data piece that obtains is pressed the reconfiguration principle of wavelet transformation, is carried out image reconstruction,
Obtain required spatial resolution and spectral resolution data block, then with these data blocks aforesaid cut apart suitable
Preface is spliced, and forms new images.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 03114921 CN1284975C (en) | 2003-01-16 | 2003-01-16 | Optimization method of remote sensing image by use of syncretic bilinear interpolation and wavelet transformation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 03114921 CN1284975C (en) | 2003-01-16 | 2003-01-16 | Optimization method of remote sensing image by use of syncretic bilinear interpolation and wavelet transformation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1431523A true CN1431523A (en) | 2003-07-23 |
| CN1284975C CN1284975C (en) | 2006-11-15 |
Family
ID=4790525
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 03114921 Expired - Fee Related CN1284975C (en) | 2003-01-16 | 2003-01-16 | Optimization method of remote sensing image by use of syncretic bilinear interpolation and wavelet transformation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN1284975C (en) |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1296871C (en) * | 2004-08-19 | 2007-01-24 | 复旦大学 | Remote sensitive image fusing method based on residual error |
| CN100359336C (en) * | 2003-11-27 | 2008-01-02 | 上海交通大学 | Double platform simple angle maneuvering target interfusion and track method based on wavelet transformation |
| CN100510772C (en) * | 2006-01-23 | 2009-07-08 | 武汉大学 | Small target super resolution reconstruction method for remote sensing image |
| CN100516978C (en) * | 2007-11-07 | 2009-07-22 | 重庆大学 | Mixed optical wavelet conversion method based on white light and monochromatic light |
| CN101867721A (en) * | 2010-04-15 | 2010-10-20 | 青岛海信网络科技股份有限公司 | Implement method, implement device and imaging device for wide dynamic images |
| CN101504766B (en) * | 2009-03-25 | 2011-09-07 | 湖南大学 | Image amalgamation method based on mixed multi-resolution decomposition |
| CN102186069A (en) * | 2011-01-14 | 2011-09-14 | 王慧 | Remote sensing image data compression method capable of maintaining measurement performance |
| CN101421758B (en) * | 2006-04-18 | 2011-09-21 | 国立大学法人东京工业大学 | Image converter and image converting program |
| CN102289802A (en) * | 2011-06-09 | 2011-12-21 | 北京航空航天大学 | Method for selecting threshold value of image fusion rule of unmanned aerial vehicle based on wavelet transformation |
| CN102413104A (en) * | 2010-09-25 | 2012-04-11 | 中国科学院遥感应用研究所 | Space data network progressive transmission method and system |
| CN101754014B (en) * | 2008-12-05 | 2012-09-05 | 索尼株式会社 | Information processing apparatus and method |
| CN102800099A (en) * | 2012-07-19 | 2012-11-28 | 北京市遥感信息研究所 | Multi-feature multi-level visible light and high-spectrum image high-precision registering method |
| CN102063710B (en) * | 2009-11-13 | 2013-01-16 | 中国科学院烟台海岸带研究所 | Method for realizing fusion and enhancement of remote sensing image |
| CN109372497A (en) * | 2018-08-20 | 2019-02-22 | 中国石油天然气集团有限公司 | A kind of method of ultrasonic imaging dynamic equalization processing |
| CN111626934A (en) * | 2020-05-14 | 2020-09-04 | 山东师范大学 | Method, system, medium and apparatus for reconstruction of high spatial resolution multispectral images |
| CN111928942A (en) * | 2019-05-13 | 2020-11-13 | 清华大学 | Terahertz spectral imaging data processing method and device |
| CN112508832A (en) * | 2020-12-03 | 2021-03-16 | 中国矿业大学 | Object-oriented remote sensing image data space-time fusion method, system and equipment |
-
2003
- 2003-01-16 CN CN 03114921 patent/CN1284975C/en not_active Expired - Fee Related
Cited By (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100359336C (en) * | 2003-11-27 | 2008-01-02 | 上海交通大学 | Double platform simple angle maneuvering target interfusion and track method based on wavelet transformation |
| CN1296871C (en) * | 2004-08-19 | 2007-01-24 | 复旦大学 | Remote sensitive image fusing method based on residual error |
| CN100510772C (en) * | 2006-01-23 | 2009-07-08 | 武汉大学 | Small target super resolution reconstruction method for remote sensing image |
| CN101421758B (en) * | 2006-04-18 | 2011-09-21 | 国立大学法人东京工业大学 | Image converter and image converting program |
| CN100516978C (en) * | 2007-11-07 | 2009-07-22 | 重庆大学 | Mixed optical wavelet conversion method based on white light and monochromatic light |
| CN101754014B (en) * | 2008-12-05 | 2012-09-05 | 索尼株式会社 | Information processing apparatus and method |
| CN101504766B (en) * | 2009-03-25 | 2011-09-07 | 湖南大学 | Image amalgamation method based on mixed multi-resolution decomposition |
| CN102063710B (en) * | 2009-11-13 | 2013-01-16 | 中国科学院烟台海岸带研究所 | Method for realizing fusion and enhancement of remote sensing image |
| CN101867721A (en) * | 2010-04-15 | 2010-10-20 | 青岛海信网络科技股份有限公司 | Implement method, implement device and imaging device for wide dynamic images |
| CN102413104B (en) * | 2010-09-25 | 2015-04-01 | 中国科学院遥感应用研究所 | Space data network progressive transmission method and system |
| CN102413104A (en) * | 2010-09-25 | 2012-04-11 | 中国科学院遥感应用研究所 | Space data network progressive transmission method and system |
| CN102186069A (en) * | 2011-01-14 | 2011-09-14 | 王慧 | Remote sensing image data compression method capable of maintaining measurement performance |
| CN102186069B (en) * | 2011-01-14 | 2013-05-08 | 王慧 | Remote sensing image data compression method capable of maintaining measurement performance |
| CN102289802A (en) * | 2011-06-09 | 2011-12-21 | 北京航空航天大学 | Method for selecting threshold value of image fusion rule of unmanned aerial vehicle based on wavelet transformation |
| CN102800099A (en) * | 2012-07-19 | 2012-11-28 | 北京市遥感信息研究所 | Multi-feature multi-level visible light and high-spectrum image high-precision registering method |
| CN102800099B (en) * | 2012-07-19 | 2015-02-18 | 北京市遥感信息研究所 | Multi-feature multi-level visible light and high-spectrum image high-precision registering method |
| CN109372497A (en) * | 2018-08-20 | 2019-02-22 | 中国石油天然气集团有限公司 | A kind of method of ultrasonic imaging dynamic equalization processing |
| CN111928942A (en) * | 2019-05-13 | 2020-11-13 | 清华大学 | Terahertz spectral imaging data processing method and device |
| CN111928942B (en) * | 2019-05-13 | 2021-11-23 | 清华大学 | Terahertz spectral imaging data processing method and device |
| US11422031B2 (en) | 2019-05-13 | 2022-08-23 | Tsinghua University | Method and apparatus for processing terahertz spectral imaging data |
| CN111626934A (en) * | 2020-05-14 | 2020-09-04 | 山东师范大学 | Method, system, medium and apparatus for reconstruction of high spatial resolution multispectral images |
| CN112508832A (en) * | 2020-12-03 | 2021-03-16 | 中国矿业大学 | Object-oriented remote sensing image data space-time fusion method, system and equipment |
| CN112508832B (en) * | 2020-12-03 | 2024-02-13 | 中国矿业大学 | Object-oriented remote sensing image data space-time fusion method, system and equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN1284975C (en) | 2006-11-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN1284975C (en) | Optimization method of remote sensing image by use of syncretic bilinear interpolation and wavelet transformation | |
| CN109064396A (en) | A kind of single image super resolution ratio reconstruction method based on depth ingredient learning network | |
| CN101477684B (en) | Process for reconstructing human face image super-resolution by position image block | |
| CN109523470B (en) | Depth image super-resolution reconstruction method and system | |
| CN103020939B (en) | Method for removing large-area thick clouds for optical remote sensing images through multi-temporal data | |
| CN102722865A (en) | Super-resolution sparse representation method | |
| CN1282131C (en) | Image merging method based on inseparable wavelet frame | |
| CN1136738C (en) | Miniaturized real-time stereoscopic visual display | |
| CN110533591B (en) | Super-resolution image reconstruction method based on codec structure | |
| CN1251145C (en) | Pyramid image merging method being integrated with edge and texture information | |
| CN114266957A (en) | Hyperspectral image super-resolution restoration method based on multi-degradation mode data augmentation | |
| CN116343053B (en) | Automatic solid waste extraction method based on fusion of optical remote sensing image and SAR remote sensing image | |
| CN117058367A (en) | Semantic segmentation method and device for high-resolution remote sensing image building | |
| CN115660979A (en) | Attention mechanism-based double-discriminator image restoration method | |
| CN102496144A (en) | NSCT (nonsubsampled contourlet transform) sonar image enhancement method based on HSV (hue, saturation and value) color space | |
| CN114529830A (en) | Remote sensing image space-time fusion method based on mixed convolution network | |
| CN116630625A (en) | Substation equipment leakage oil separation method based on double-graph reasoning | |
| CN115953311A (en) | Image defogging method based on multi-scale feature representation of Transformer | |
| CN117314811A (en) | SAR-optical image fusion method based on hybrid model | |
| Wang et al. | An ensemble multi-scale residual attention network (EMRA-net) for image Dehazing | |
| CN114140357A (en) | Multi-temporal remote sensing image cloud region reconstruction method based on cooperative attention mechanism | |
| CN112989919B (en) | Method and system for extracting target object from image | |
| CN117237256B (en) | Shallow sea coral reef monitoring data acquisition method, device and equipment | |
| CN116977651B (en) | Image denoising method based on double-branch and multi-scale feature extraction | |
| CN1588445A (en) | Image fusing method based on direction filter unit |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C17 | Cessation of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20061115 Termination date: 20100219 |