CN109740468A - An Adaptive Gaussian Low-Pass Filtering Algorithm for Extracting Organic Matter Information from Black Soil - Google Patents
An Adaptive Gaussian Low-Pass Filtering Algorithm for Extracting Organic Matter Information from Black Soil Download PDFInfo
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Abstract
本发明属于信息提取技术领域，具体涉及一种用于黑土有机质信息提取的自适应高斯低通滤波算法；本发明的目的是针对现有技术的缺陷，提供一种用于黑土有机质信息提取的自适应高斯低通滤波算法；包括以下步骤：步骤一、黑土地高光谱预处理；步骤二、频率域转化；步骤三、自适应高斯滤波；步骤四、分级提取；步骤五、有机质提取结果；将各个级别含量的有机质进行综合制图，形成有机质含量的航空高光谱提取结果；将总体精度和Kappa系数最高的参数设置σ1＝0.5，ε1＝1.1应用到黑土地有机质的运算中，分级之后，根据地面分析数据，给出每一级别的有机质含量，得出黑土有机质含量的空间分布情况。The invention belongs to the technical field of information extraction, and in particular relates to an adaptive Gaussian low-pass filtering algorithm for black soil organic matter information extraction. Adaptive Gaussian low-pass filtering algorithm; including the following steps: step 1, black soil hyperspectral preprocessing; step 2, frequency domain transformation; step 3, adaptive Gaussian filtering; step 4, hierarchical extraction; step 5, organic matter extraction result; The organic matter of each level of content is comprehensively mapped to form the aerial hyperspectral extraction results of the organic matter content; the parameters with the highest overall accuracy and Kappa coefficient are set to σ 1 =0.5, ε 1 =1.1 are applied to the calculation of black soil organic matter, after classification, According to the ground analysis data, the organic matter content of each level is given, and the spatial distribution of the organic matter content of black soil is obtained.
Description
Technical field
The invention belongs to information extraction technology fields, and in particular to a kind of for the adaptive of organic matter in black soil information extraction
Gassian low-pass filter algorithm.
Background technique
China's black soil of Northeast China is rich in organic matter, and introducing Airborne Hyperspectral data can provide for Scientific evaluation black earth geological measuring
New technological means.In the organic matter in black soil content identification based on high-spectral data, spectroscopic data is interpreted as reflection black earth
The energy profile of component content target electromagnetic radiation characteristic, this processing merely depict the Spectral Properties of atural object Energy distribution
Property, it is a kind of method of black earth list Pixel Analysis, the black earth Limited information that can be extracted.It is passed to grasp remote sensing optical system information
Rule is passed, from the frequency domain channel matrix derivation of remote sensing optical system, imaging system frequency domain is believed from the angle analysis of frequency domain
It ceases Transfer Parameters and carries out numerical value calculating, research achievement plays reference role to the design of remote sensing optical system.
Conventional method is to carry out the characterization of the energy expression of blackland pixel spectrum and frequency-domain spectrum energy to separate place
Original spectral data cannot be redistributed to another space, can not reflect the change of energy in spatial domain picture by reason, this processing
Law, so that the space characteristics such as the tone of black earth, texture, trend and boundary fail to participate in soil property assessment, assessment knot
Fruit is often not a comprehensive black earth characteristic set, limits the precision of organic matter in black soil information extraction.
Therefore, there is an urgent need on the basis of analyzing black earth spectrum signature, design a kind of adaptive Gauss low-pass filtering calculation
Method achievees the purpose that blackland classification classification by gradually excluding the data of different-energy grade.Joint blackland EO-1 hyperion picture
Member spatial context information and spectral information classify to EO-1 hyperion pixel, break through image spatial information break through tradition by
The performance bottleneck of pixel spectra classification, charts for organic matter in black soil and provides support, with a wide range of precise to solve existing method
Drawback.
Summary of the invention
It is a kind of for the adaptive of organic matter in black soil information extraction the purpose of the present invention is in view of the drawbacks of the prior art, providing
Answer Gassian low-pass filter algorithm.
The technical scheme is that
A kind of adaptive Gauss low-pass filtering algorithm for organic matter in black soil information extraction, comprising the following steps:
Step 1: blackland EO-1 hyperion pre-processes；
Radiant correction locates high-spectral data using the FLAASH algorithm based on MODTRAN4+ radiative transfer model in advance
Reason.Needed when calculating metadata include observation field angle, sun angle, sea level on the average, Atmospheric models, aerosol type and
Visibility range；Geometric correction realizes that inertial navigation system and positioning system have recorded each on machine using airborne 510 system of POS
The location parameter X, Y, Z and attitude parameter Roll, Pitch, Heading of pixel will be sat by the GPS time of each frame image
Mark is assigned to the picture dot；
Step 2: frequency domain converts；
Original blackland high-spectral data is subjected to frequency domain conversion, generates frequency domain data；
Step 3: adaptive Gaussian mixture model；
In conjunction with the interpolated data of organic prime number evidence known in black earth geographical space, the gaussian filtering that design can be adaptive
Device, so that selecting different σ on the basis of retaining black earth image local feature, realizing the classification of original image；This process
In, it is constantly compared with ground analysis data, interative computation, is required until meeting classification；
Step 4: grading extraction；
A kind of adaptive Gauss low-pass filtering algorithm is designed, by gradually excluding the data of different-energy grade, is realized black
The purpose of soil content of organic matter classification classification；
Step 5: organic matter extracts result；
The organic matter of each rank content is subjected to composite mapping, the Airborne Hyperspectral for forming the content of organic matter extracts knot
Fruit；By overall accuracy and the highest parameter setting σ of Kappa coefficient1=0.5, ε1=1.1 are applied to the operation of blackland organic matter
In, after classification, according to Ground analysis data, the content of organic matter of each rank is provided, obtains the sky of organic matter in black soil content
Between distribution situation.
The step 1 the following steps are included:
Step 1.1 radiant correction
Calculate the received pixel spectral radiance at bloom spectrum sensor:
In formula, L is the received global radiation brightness of sensor；ρ is pixel surface reflectivity；ρeFor average surface around pixel
Reflectivity；S is atmosphere spherical albedo；LαFor atmospheric backscatter radiance；A, B is the coefficient of atmosphere and geometrical condition；Side
The mechanism of method is using the atmospheric radiation transmission based on MODTRAN4+；
Step 1.2 geometric correction
The GPS time that each frame image is had recorded in original image generates a time tag after radiant correction
File * .att file is recorded, the GPS time of each frame image is had recorded in this document, which is included in POS system
In the period of destination file, by the time compare and coordinate projection transformation be obtained with each frame image attitude data and
Position data carries out geometric correction to image；
The step 2 the following steps are included:
Step 2.1 establishes basic Filtering Model
In frequency domain, basic Filtering Model are as follows:
G (u, v)=H (u, v) F (u, v)
In formula, F (u, v) is the Fourier transformation image filtered；H (u, v) is filter transform function；After decaying
Smoothed out image G (u, v) is generated after high-frequency information.
The foundation of step 2.2 gauss low frequency filter
Transform of spatial domain can be turned to the gauss low frequency filter of frequency domain are as follows:
In formula, D (u, v) is the distance away from transformation origin, and d is the degree of Gaussian curve extension, i.e. cutoff frequency.Design one
Kind adaptive Gaussian mixture model device automatically selects different d according to the black earth content characteristics for being smoothed image, so that after treatment
Result images in obtain the organic matter data of corresponding content.
The step 3 the following steps are included:
The expression of step 3.1 Gaussian smoothing
Assuming that the Gaussian smoothing of black earth image is indicated with following formula:
I0(x, y)=Id(x,y)+ed(x,y)
In formula, for the pixel at (x, y), I0(x, y) is the gray value of original high-spectral data, Id(x, y) is cut-off
Low pass gray value under frequency d, εd(x, y) is the residual values under cutoff frequency d.
Step 3.2 realizes process from the algorithm for being suitable for gaussian filtering
Input: black earth Hyperspectral imaging x=(x1,x2,…,xi)∈Rd×i, filtering content of organic matter rank C ∈ (1,2,3,
4,5), black earth image classification set ξ={ 1,2 ..., n }, training set Tm={ (x1,c1),…,(xm,cm)}∈(Rd×ξ)m,
In, c ∈ ξ and m represent the quantity of training sample；
A): input training set constructs gauss low frequency filter, carries out classification pixel-by-pixel to EO-1 hyperion black earth image and obtains just
Beginning classification results c；
B): being face domain information by ground chemical examination data interpolating, be as a result expressed as class probability graph ξn, n=1,2 ..., N；
C): initial cutoff frequency d is carried out to Hyperspectral imaging x1It calculates, obtains initial variance σ1With residual values ε1；
D): if d1=dbest then
E): extracted information meets a certain rank content of organic matter section, then is denoted as c1
F): end
G): else
H): d1'=d1++；
I): initial cutoff frequency d is carried out to Hyperspectral imaging x1' calculate, obtain new variances sigma1' and residual values ε1'；
J): until, extracted information meets a certain rank content of organic matter section, then is denoted as c1；
K): inverse transformation being carried out to high-spectral data x, removes respective pixel in airspace, generates x '；
L): end
M): initial cutoff frequency d is carried out to Hyperspectral imaging x '2It calculates, obtains initial variance σ2With residual values ε2；
N): d~m step is repeated, until generating c1、c2、c3、c4And c5
Output: content of organic matter classification results c=(c1,c2,…,ci), each pixel i is endowed one in classification results
A label ci∈ξ
The step 4 the following steps are included:
Step 4.1 calculates cutoff frequency d
Design a kind of method for calculating cutoff frequency d, reach can smooth black earth high-spectral data realize classification classification and
It is able to maintain the purpose closest with laboratory values.
Design a kind of calculation method based on energy function:
In formula, c is constant term, is determined according to the organic matter analysis data of sampled point；σ is variance；ε is residual values.
Step 4.2 smoothsort and details keep principle
It obtains, in the case where c is known constant item, optimal cutoff frequency d needs variances sigma as big as possible, and high-spectral data is become
It is changed to more smooth data.And residual epsilon must as small as possible, i.e., the reflection after gaussian filtering, at original pixels (x, y)
Rate value amplitude of variation is the smaller the better.In this way, establishing a kind of synthesis gaussian filtering method in the factor content of organic matter, reach
Smoothsort and the balance for keeping minutia.
The beneficial effects of the present invention are:
First is that adaptive Gauss low-pass filtering algorithm not only makes smooth region pixel classifications precision improvement, while making score
Field content of organic matter edge detection in grade result is more accurate.Plot smothing filtering can effectively combine the space of image
With spectral information, on the basis of effectively removing salt-pepper noise caused by similar conventional sorting methods, so that black earth in classification results
The profile on ground and true blackland plot profile are almost the same；
Second is that compared to traditional hyperspectral classification algorithm, this chapter propose frequency domain and extraction algorithm computational efficiency compared with
Height can quickly calculate the result data of each grade after clear content of organic matter grade；Frequency domain method is from new angle
High-spectral data is described and is characterized, on the basis of solution high-spectral data is uncertain, this following method may energy
The restraining factors such as traditional atmospheric correction and radiant correction are enough broken through, completely new method is brought.
Specific embodiment
The present invention is further introduced below with reference to embodiment:
Spatially and spectrally information reference data can will be used as simultaneously to realize in the classification of black earth Hyperspectral Image Classification
The adaptive Gauss low-pass filtering algorithm (Adv of organic matter in black soil content information is added in frequency domain combining classifiers for target
Gauss filter)。
The core concept of the algorithm is that one of foundation can carry out energy conversion to high spectrum image, realizes that energy is low
The Gaussian low pass wave pattern for just extracting (or excluding) reach black by gradually excluding the data of different-energy grade
The purpose of land classification classification.When Gauss model is smoothed image, Gaussian function determines filtering by calculating variances sigma
As a result.In conjunction with the interpolated data of organic prime number evidence known in black earth geographical space, the Gaussian filter that design can be adaptive makes
It obtains on the basis of retaining black earth image local feature, selects different σ, realize the classification of original image.
A kind of adaptive Gauss low-pass filtering algorithm for organic matter in black soil information extraction, comprising the following steps:
Step 1: blackland EO-1 hyperion pre-processes；
Radiant correction locates high-spectral data using the FLAASH algorithm based on MODTRAN4+ radiative transfer model in advance
Reason.Needed when calculating metadata include observation field angle, sun angle, sea level on the average, Atmospheric models, aerosol type and
Visibility range；Geometric correction realizes that inertial navigation system and positioning system have recorded each on machine using airborne 510 system of POS
The location parameter X, Y, Z and attitude parameter Roll, Pitch, Heading of pixel will be sat by the GPS time of each frame image
Mark is assigned to the picture dot；
Step 2: frequency domain converts；
Original blackland high-spectral data is subjected to frequency domain conversion, generates frequency domain data；
Step 3: adaptive Gaussian mixture model；
In conjunction with the interpolated data of organic prime number evidence known in black earth geographical space, the gaussian filtering that design can be adaptive
Device, so that selecting different σ on the basis of retaining black earth image local feature, realizing the classification of original image；This process
In, it is constantly compared with ground analysis data, interative computation, is required until meeting classification；
Step 4: grading extraction；
A kind of adaptive Gauss low-pass filtering algorithm is designed, by gradually excluding the data of different-energy grade, is realized black
The purpose of soil content of organic matter classification classification；
Step 5: organic matter extracts result；
The organic matter of each rank content is subjected to composite mapping, the Airborne Hyperspectral for forming the content of organic matter extracts knot
Fruit；By overall accuracy and the highest parameter setting σ of Kappa coefficient1=0.5, ε1=1.1 are applied to the operation of blackland organic matter
In, after classification, according to Ground analysis data, the content of organic matter of each rank is provided, obtains the sky of organic matter in black soil content
Between distribution situation.
The step 1 the following steps are included:
Step 1.1 radiant correction
Calculate the received pixel spectral radiance at bloom spectrum sensor:
In formula, L is the received global radiation brightness of sensor；ρ is pixel surface reflectivity；ρeFor average surface around pixel
Reflectivity；S is atmosphere spherical albedo；LαFor atmospheric backscatter radiance；A, B is the coefficient of atmosphere and geometrical condition；Side
The mechanism of method is using the atmospheric radiation transmission based on MODTRAN4+；
Step 1.2 geometric correction
The GPS time that each frame image is had recorded in original image generates a time tag after radiant correction
File * .att file is recorded, the GPS time of each frame image is had recorded in this document, which is included in POS system
In the period of destination file, by the time compare and coordinate projection transformation be obtained with each frame image attitude data and
Position data carries out geometric correction to image；
The step 2 the following steps are included:
Step 2.1 establishes basic Filtering Model
In frequency domain, basic Filtering Model are as follows:
G (u, v)=H (u, v) F (u, v)
In formula, F (u, v) is the Fourier transformation image filtered；H (u, v) is filter transform function；After decaying
Smoothed out image G (u, v) is generated after high-frequency information.
The foundation of step 2.2 gauss low frequency filter
Transform of spatial domain can be turned to the gauss low frequency filter of frequency domain are as follows:
In formula, D (u, v) is the distance away from transformation origin, and d is the degree of Gaussian curve extension, i.e. cutoff frequency.Design one
Kind adaptive Gaussian mixture model device automatically selects different d according to the black earth content characteristics for being smoothed image, so that after treatment
Result images in obtain the organic matter data of corresponding content.
The step 3 the following steps are included:
The expression of step 3.1 Gaussian smoothing
Assuming that the Gaussian smoothing of black earth image is indicated with following formula:
I0(x, y)=Id(x,y)+ed(x,y)
In formula, for the pixel at (x, y), I0(x, y) is the gray value of original high-spectral data, Id(x, y) is cut-off
Low pass gray value under frequency d, εd(x, y) is the residual values under cutoff frequency d.
Step 3.2 realizes process from the algorithm for being suitable for gaussian filtering
Input: black earth Hyperspectral imaging x=(x1,x2,…,xi)∈Rd×i, filtering content of organic matter rank C ∈ (1,2,3,
4,5), black earth image classification set ξ={ 1,2 ..., n }, training set Tm={ (x1,c1),…,(xm,cm)}∈(Rd×ξ)m,
In, c ∈ ξ and m represent the quantity of training sample；
A): input training set constructs gauss low frequency filter, carries out classification pixel-by-pixel to EO-1 hyperion black earth image and obtains just
Beginning classification results c；
B): being face domain information by ground chemical examination data interpolating, be as a result expressed as class probability graph ξn, n=1,2 ..., N；
C): initial cutoff frequency d is carried out to Hyperspectral imaging x1It calculates, obtains initial variance σ1With residual values ε1；
D): if d1=dbest then
E): extracted information meets a certain rank content of organic matter section, then is denoted as c1
F): end
G): else
H): d1'=d1++；
I): initial cutoff frequency d is carried out to Hyperspectral imaging x1' calculate, obtain new variances sigma1' and residual values ε1'；
J): until, extracted information meets a certain rank content of organic matter section, then is denoted as c1；
K): inverse transformation being carried out to high-spectral data x, removes respective pixel in airspace, generates x '；
L): end
M): initial cutoff frequency d is carried out to Hyperspectral imaging x '2It calculates, obtains initial variance σ2With residual values ε2；
N): d~m step is repeated, until generating c1、c2、c3、c4And c5
Output: content of organic matter classification results c=(c1,c2,…,ci), each pixel i is endowed one in classification results
A label ci∈ξ
The step 4 the following steps are included:
Step 4.1 calculates cutoff frequency d
Design a kind of method for calculating cutoff frequency d, reach can smooth black earth high-spectral data realize classification classification and
It is able to maintain the purpose closest with laboratory values.
Design a kind of calculation method based on energy function:
In formula, c is constant term, is determined according to the organic matter analysis data of sampled point；σ is variance；ε is residual values.
Step 4.2 smoothsort and details keep principle
It obtains, in the case where c is known constant item, optimal cutoff frequency d needs variances sigma as big as possible, and high-spectral data is become
It is changed to more smooth data.And residual epsilon must as small as possible, i.e., the reflection after gaussian filtering, at original pixels (x, y)
Rate value amplitude of variation is the smaller the better.In this way, establishing a kind of synthesis gaussian filtering method in the factor content of organic matter, reach
Smoothsort and the balance for keeping minutia.
Claims (5)
1. a kind of adaptive Gauss low-pass filtering algorithm for organic matter in black soil information extraction, it is characterised in that: including following
Step:
Step 1: blackland EO-1 hyperion pre-processes；
Radiant correction pre-processes high-spectral data using the FLAASH algorithm based on MODTRAN4+ radiative transfer model.
It includes observation field angle, sun angle, sea level on the average, Atmospheric models, aerosol type and energy that metadata is needed when calculating
Degree of opinion range；Geometric correction realizes that inertial navigation system and positioning system have recorded each picture on machine using airborne 510 system of POS
The location parameter X, Y, Z and attitude parameter Roll, Pitch, Heading of member, by the GPS time of each frame image, by coordinate
It is assigned to the picture dot；
Step 2: frequency domain converts；
Original blackland high-spectral data is subjected to frequency domain conversion, generates frequency domain data；
Step 3: adaptive Gaussian mixture model；
In conjunction with the interpolated data of organic prime number evidence known in black earth geographical space, the Gaussian filter that design can be adaptive makes
It obtains on the basis of retaining black earth image local feature, selects different σ, realize the classification of original image；During this, no
Disconnected to be compared with ground analysis data, interative computation requires until meeting classification；
Step 4: grading extraction；
A kind of adaptive Gauss low-pass filtering algorithm is designed, by gradually excluding the data of different-energy grade, realizes blackland
The purpose of content of organic matter classification classification；
Step 5: organic matter extracts result；
The organic matter of each rank content is subjected to composite mapping, the Airborne Hyperspectral for forming the content of organic matter extracts result；It will
Overall accuracy and the highest parameter setting σ of Kappa coefficient1=0.5, ε1=1.1 are applied in the operation of blackland organic matter, point
After grade, according to Ground analysis data, the content of organic matter of each rank is provided, obtains the spatial distribution of organic matter in black soil content
Situation.
2. a kind of adaptive Gauss low-pass filtering algorithm for organic matter in black soil information extraction according to claim 1,
It is characterized by: the step 1 the following steps are included:
Step 1.1 radiant correction
Calculate the received pixel spectral radiance at bloom spectrum sensor:
In formula, L is the received global radiation brightness of sensor；ρ is pixel surface reflectivity；ρeIt is reflected for average surface around pixel
Rate；S is atmosphere spherical albedo；LαFor atmospheric backscatter radiance；A, B is the coefficient of atmosphere and geometrical condition；Method
Mechanism is using the atmospheric radiation transmission based on MODTRAN4+；
Step 1.2 geometric correction
The GPS time that each frame image is had recorded in original image generates a time tag record after radiant correction
File * .att file, the GPS time of each frame image is had recorded in this document, which is included in POS system result
In the period of file, is compared by the time and coordinate projection converts attitude data and the position for being obtained with each frame image
Data carry out geometric correction to image.
3. a kind of adaptive Gauss low-pass filtering algorithm for organic matter in black soil information extraction according to claim 1,
It is characterized by: the step 2 the following steps are included:
Step 2.1 establishes basic Filtering Model
In frequency domain, basic Filtering Model are as follows:
G (u, v)=H (u, v) F (u, v)
In formula, F (u, v) is the Fourier transformation image filtered；H (u, v) is filter transform function；Pass through high frequency after decaying
Smoothed out image G (u, v) is generated after information.
The foundation of step 2.2 gauss low frequency filter
Transform of spatial domain can be turned to the gauss low frequency filter of frequency domain are as follows:
In formula, D (u, v) is the distance away from transformation origin, and d is the degree of Gaussian curve extension, i.e. cutoff frequency.Design one kind certainly
It adapts to Gaussian filter and automatically selects different d according to the black earth content characteristics for being smoothed image, so that knot after treatment
The organic matter data of corresponding content are obtained in fruit image.
4. a kind of adaptive Gauss low-pass filtering algorithm for organic matter in black soil information extraction according to claim 1,
It is characterized by: the step 3 the following steps are included:
The expression of step 3.1 Gaussian smoothing
Assuming that the Gaussian smoothing of black earth image is indicated with following formula:
I0(x, y)=Id(x,y)+ed(x,y)
In formula, for the pixel at (x, y), I0(x, y) is the gray value of original high-spectral data, Id(x, y) is cutoff frequency
Low pass gray value under d, εd(x, y) is the residual values under cutoff frequency d.
Step 3.2 realizes process from the algorithm for being suitable for gaussian filtering
Input: black earth Hyperspectral imaging x=(x1,x2,…,xi)∈Rd×i, it filters content of organic matter rank C ∈ (1,2,3,4,5),
Black earth image classification set ξ={ 1,2 ..., n }, training set Tm={ (x1,c1),…,(xm,cm)}∈(Rd×ξ)m, wherein c ∈
ξ and m represent the quantity of training sample；
A): input training set constructs gauss low frequency filter, carries out classification pixel-by-pixel to EO-1 hyperion black earth image and obtains initial point
Class result c；
B): being face domain information by ground chemical examination data interpolating, be as a result expressed as class probability graph ξn, n=1,2 ..., N；
C): initial cutoff frequency d is carried out to Hyperspectral imaging x1It calculates, obtains initial variance σ1With residual values ε1；
D): if d1=dbestthen
E): extracted information meets a certain rank content of organic matter section, then is denoted as c1
F): end
G): else
H): d1'=d1++；
I): initial cutoff frequency d is carried out to Hyperspectral imaging x1' calculate, obtain new variances sigma1' and residual values ε1'；
J): until, extracted information meets a certain rank content of organic matter section, then is denoted as c1；
K): inverse transformation being carried out to high-spectral data x, removes respective pixel in airspace, generates x '；
L): end
M): initial cutoff frequency d is carried out to Hyperspectral imaging x '2It calculates, obtains initial variance σ2With residual values ε2；
N): d~m step is repeated, until generating c1、c2、c3、c4And c5
Output: content of organic matter classification results c=(c1,c2,…,ci), each pixel i is endowed a mark in classification results
Remember ci∈ξ。
5. a kind of adaptive Gauss low-pass filtering algorithm for organic matter in black soil information extraction according to claim 1,
It is characterized by: the step 4 the following steps are included:
Step 4.1 calculates cutoff frequency d
A kind of method for calculating cutoff frequency d is designed, reaching smoothly black earth high-spectral data to realize classification classification and protect
Hold the purpose closest with laboratory values.
Design a kind of calculation method based on energy function:
In formula, c is constant term, is determined according to the organic matter analysis data of sampled point；σ is variance；ε is residual values.
Step 4.2 smoothsort and details keep principle
It obtains, in the case where c is known constant item, optimal cutoff frequency d needs variances sigma as big as possible, and high-spectral data is transformed to
More smooth data.And residual epsilon must as small as possible, i.e., the reflectance value after gaussian filtering, at original pixels (x, y)
Amplitude of variation is the smaller the better.In this way, establishing a kind of synthesis gaussian filtering method in the factor content of organic matter, reach smooth
Classification and the balance for keeping minutia.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112765290A (en) * | 2020-12-25 | 2021-05-07 | 北京山水云图科技有限公司 | Method for representing soil composition by using image |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5568151A (en) * | 1995-05-17 | 1996-10-22 | Merritt; David A. | Statistical averaging method for wind profiler doppler spectra |
| US20150054526A1 (en) * | 2013-08-21 | 2015-02-26 | Selex Es Inc. | Method and apparatus for distance measuring equipment (dme/normal) using alternative pulse shapes |
| CN106442696A (en) * | 2016-11-08 | 2017-02-22 | 核工业北京地质研究院 | Method for measuring ratio of boron isotopes |
| CN106650819A (en) * | 2016-12-30 | 2017-05-10 | 东北农业大学 | Soil classification method through combination of multi-layer perceptron neural networks with spectral characteristic parameters |
| CN106770054A (en) * | 2016-12-30 | 2017-05-31 | 黑龙江禾禾遥感科技有限公司 | Black earth water content fast determining method based on spectral absorption characteristics |
| CN107015074A (en) * | 2017-03-30 | 2017-08-04 | 信丰县包钢新利稀土有限责任公司 | A kind of measuring system of rear-earth-doped ferroelectric material piezoelectric modulus |
| CN104573732B (en) * | 2013-10-18 | 2017-12-22 | 核工业北京地质研究院 | A kind of target optical spectrum matching process |
| CN107576621A (en) * | 2017-10-24 | 2018-01-12 | 中国矿业大学(北京) | A kind of method of spectrum second differnce Gabor expansion detection Cu Pollution in Soil |
| CN107748140A (en) * | 2017-10-19 | 2018-03-02 | 中国矿业大学(北京) | A kind of field EO-1 hyperion Fast measurement system |
-
2018
- 2018-12-24 CN CN201811581128.6A patent/CN109740468B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5568151A (en) * | 1995-05-17 | 1996-10-22 | Merritt; David A. | Statistical averaging method for wind profiler doppler spectra |
| US20150054526A1 (en) * | 2013-08-21 | 2015-02-26 | Selex Es Inc. | Method and apparatus for distance measuring equipment (dme/normal) using alternative pulse shapes |
| CN104573732B (en) * | 2013-10-18 | 2017-12-22 | 核工业北京地质研究院 | A kind of target optical spectrum matching process |
| CN106442696A (en) * | 2016-11-08 | 2017-02-22 | 核工业北京地质研究院 | Method for measuring ratio of boron isotopes |
| CN106650819A (en) * | 2016-12-30 | 2017-05-10 | 东北农业大学 | Soil classification method through combination of multi-layer perceptron neural networks with spectral characteristic parameters |
| CN106770054A (en) * | 2016-12-30 | 2017-05-31 | 黑龙江禾禾遥感科技有限公司 | Black earth water content fast determining method based on spectral absorption characteristics |
| CN107015074A (en) * | 2017-03-30 | 2017-08-04 | 信丰县包钢新利稀土有限责任公司 | A kind of measuring system of rear-earth-doped ferroelectric material piezoelectric modulus |
| CN107748140A (en) * | 2017-10-19 | 2018-03-02 | 中国矿业大学(北京) | A kind of field EO-1 hyperion Fast measurement system |
| CN107576621A (en) * | 2017-10-24 | 2018-01-12 | 中国矿业大学(北京) | A kind of method of spectrum second differnce Gabor expansion detection Cu Pollution in Soil |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112765290A (en) * | 2020-12-25 | 2021-05-07 | 北京山水云图科技有限公司 | Method for representing soil composition by using image |
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