CN111047528A - High-spectrum image unmixing method based on goblet sea squirt group - Google Patents

High-spectrum image unmixing method based on goblet sea squirt group Download PDF

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CN111047528A
CN111047528A CN201911181901.4A CN201911181901A CN111047528A CN 111047528 A CN111047528 A CN 111047528A CN 201911181901 A CN201911181901 A CN 201911181901A CN 111047528 A CN111047528 A CN 111047528A
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CN111047528B (en
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蔺悦
陈雷
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Tianjin University
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Abstract

The invention discloses a hyperspectral image unmixing method based on a goblet sea squirt group. Meanwhile, the goblet sea squirt group algorithm is adopted to solve the constructed objective function, and the goblet sea squirt group algorithm is utilized to solve the advantage of high accuracy, so that higher unmixing accuracy is obtained.

Description

High-spectrum image unmixing method based on goblet sea squirt group
Technical Field
The invention relates to the field of image processing, in particular to a hyperspectral image unmixing method based on a goblet sea squirt group.
Background
The remote sensing image can provide natural resource information and environment information, and the hyperspectral image unmixing technology is an important technology for analyzing the ground feature information in the remote sensing image. Due to the fact that ground surface objects in the natural environment are complex, the hyperspectral image acquired by the spectrometer is easily influenced by the atmosphere, and the hyperspectral image analysis process becomes very complex. In order to identify the ground surface and ground objects more accurately, the hyperspectral image unmixing technology becomes more important.
The goblet sea squirt group algorithm is a novel intelligent optimization algorithm, is simple and understandable in mechanism and easy to realize, and can be used for solving the optimization problem. A Shadow-based multiple linear Mixing Model (SMLM) is a general Model for the problem of hyperspectral unmixing, which not only retains the general attributes of the mixed spectrum, but also considers the Shadow effect in a real scene.
In order to obtain higher unmixing precision and accurately estimate ground feature proportion information, researchers at home and abroad propose a plurality of unmixing methods. The unmixing method based on the nonlinear model is mainly divided into a Bayes method and a gradient method, but the Bayes method is complex in calculation, low in unmixing efficiency, prone to falling into local extreme values in the gradient method and low in unmixing precision. Therefore, the hyperspectral image unmixing effect under the nonlinear model needs to be improved.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a hyperspectral image unmixing method based on a goblet sea squirt group. Meanwhile, the goblet sea squirt group algorithm is adopted to solve the constructed objective function, and the goblet sea squirt group algorithm is utilized to solve the advantage of high accuracy, so that higher unmixing accuracy is obtained.
The purpose of the invention is realized by the following technical scheme:
the hyperspectral image unmixing method based on the goblet sea squirt group comprises the following steps:
firstly, extracting image end members by adopting a geometric-based end member extraction algorithm VCA to obtain an end member spectral curve and an end member number R;
determining a search space dimension D and a position code by utilizing a hyperspectral image SMLM mixed model according to the number R of end members;
step three, setting the population scale, the iteration times and the upper and lower boundaries of the search space of the goblet sea squirt group;
step four, initializing the position of the individual goblet sea squirt group in the search space range;
step five, constructing an objective function, and calculating the current position X of each individual goblet sea squirt groupiAn adapted value for the objective function;
sixthly, the leader individual in the goblet sea squirt group moves according to the position updating formula of the leader, and the follower individual in the goblet sea squirt group moves according to the position updating formula of the follower;
step seven, calculating the individual adaptive value of each goblet ascidian after updating the position according to the objective function, if the individual adaptive value is less than the current position XiThe current position X is replaced by the updated positioniOtherwise, the current position X is still keptiThe change is not changed;
step eight, for the current position XiAbundance vector of
Figure BDA0002291489180000021
Normalization;
step nine, judging whether the current iteration times reach the preset iteration times or not, if so, ending the iteration, and outputting the position of the optimal individual in the current goblet sea squirt group
Figure BDA0002291489180000022
Thereby obtaining abundance vector
Figure BDA0002291489180000023
Non-linear parameter
Figure BDA0002291489180000024
And shadow weights
Figure BDA0002291489180000025
Otherwise, returning to the step six;
step ten, judging whether to perform unmixing on all pixel points of the hyperspectral image, and if so, finishing the calculation; otherwise, returning to the step four, and calculating the next pixel.
Further, the second step specifically comprises:
the expression of the hyperspectral image SMLM mixed model is as follows:
Figure BDA0002291489180000026
r is the number of end members, miSpectral curve representing the ith end-member, aiRepresenting the ith end-memberAbundance value of (a), abundance vector ai＝[a1,…,aR]TSatisfying the constraint of nonnegativity sum, and the nonlinear parameter P is belonged to [0,1 ∈]The shadow weight Q ∈ [0,1 ]]。
Further, the fifth step specifically comprises: and constructing an objective function by using the reconstruction error, wherein the expression is as follows:
Figure BDA0002291489180000027
wherein, | | · | | represents a two-norm, abundance
Figure BDA0002291489180000028
The non-negative sum must be satisfied as a constraint,
Figure BDA0002291489180000029
representing data reconstructed using the SMLM model, y representing observed data, non-linear parameters
Figure BDA00022914891800000210
Shadow weights
Figure BDA00022914891800000211
Further, the sixth step specifically comprises:
the location update formula for the leader is as follows:
Figure BDA00022914891800000212
wherein the content of the first and second substances,
Figure BDA00022914891800000213
representing the first individual of goblet sea squirt (leader), at the position of dimension j, FjRepresenting the position of the food source in the j dimension, will search the upper boundary ub of the spacejIs 1, lower boundary lbjIs 0, coefficient c1The definition is as follows:
Figure BDA0002291489180000031
where L represents the current iteration number, L represents the total iteration number, and parameter c2And c3Take [0,1]The random number of (a) is set,
the follower's location update formula is as follows:
Figure BDA0002291489180000032
wherein the content of the first and second substances,
Figure BDA0002291489180000033
the location of the ith goblet ascidian (follower) in dimension j is shown.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
the invention provides a goblet sea squirt group-based hyperspectral image unmixing method, which introduces an optimization thought into hyperspectral image unmixing on the basis of a shadow model, reconstructs a target function, converts the unmixing problem into an optimization problem, and avoids complex steps of controlling constraint conditions in the traditional unmixing method. By utilizing the advantage of high convergence precision of the goblet sea squirt group algorithm and solving the objective function by adopting the goblet sea squirt group algorithm, the defect that the traditional gradient optimization algorithm is easy to fall into a local extreme value is overcome, so that higher unmixing precision is achieved. Meanwhile, the search space of the improved goblet sea squirt group algorithm is controlled to meet two constraint conditions of abundance, and complicated steps in the unmixing method are simplified.
Drawings
FIG. 1 is a flow chart of the hyperspectral image unmixing method based on the goblet sea squirt group of the present invention.
FIG. 2 is a pseudo-color image of a real hyperspectral image Japser Ridge.
FIG. 3 is an end-member spectral plot extracted from Japser Ridge data using the VCA end-member extraction algorithm.
FIG. 4 is an abundance plot of Japser Ridge data obtained using the method of the invention.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
With reference to fig. 1 and 2, the following embodiments are described:
step 1: inputting a real hyperspectral image Japser Ridge, and performing end member extraction on Japser Ridge data by adopting VCA (virtual container architecture), so as to obtain an end member spectral curve (shown in FIG. 3) and an end member number R which is 4.
Step 2: determining a search space dimension D of 6 and a position code by using a hyperspectral image SMLM mixed model according to the end member number R of 4; the expression of the hyperspectral image SMLM mixed model is as follows:
Figure BDA0002291489180000041
r is the number of end members, miSpectral curve representing the ith end-member, aiRepresenting the abundance value, abundance vector a, of the ith end-memberi＝[a1,…,aR]TSatisfying the constraint of nonnegativity sum, and the nonlinear parameter P is belonged to [0,1 ∈]The shadow weight Q ∈ [0,1 ]]。
And step 3: setting the population size N of the goblet sea squirt group to be 50, the iteration number I to be 500, the upper boundary of a search space to be 1 and the lower boundary of the search space to be 0;
and 4, step 4: initializing the position of individual goblet sea squirt group in the search space range;
and 5: constructing an objective function, and calculating the current position X of each individual of the goblet sea squirt groupiAn adapted value for the objective function;
in step 5, a target function is constructed by using the reconstruction error, and the expression is as follows:
Figure BDA0002291489180000042
wherein, | | · | | represents a two-norm, abundance
Figure BDA0002291489180000043
The non-negative sum must be satisfied as a constraint,
Figure BDA0002291489180000044
representing reconstructed data, y representing observed data, non-linearity parameters
Figure BDA0002291489180000045
Shadow weights
Figure BDA0002291489180000046
Step 6: the leader individual in the goblet sea squirt group moves according to the position updating formula of the leader, and the follower individual in the goblet sea squirt group moves according to the position updating formula of the follower;
in step 6, the position updating formula of the leader is as follows:
Figure BDA0002291489180000047
wherein the content of the first and second substances,
Figure BDA0002291489180000048
representing the first individual of goblet sea squirt (leader), at the position of dimension j, FjRepresenting the position of the food source in the j dimension, will search the upper boundary ub of the spacejIs 1, lower boundary lbjIs 0, coefficient c1The definition is as follows:
Figure BDA0002291489180000049
where L denotes the current iteration number, and L-500 denotes the total iteration number. Parameter c2And c3Take [0,1]The random number of (2).
The follower's location update formula is as follows:
Figure BDA00022914891800000410
wherein the content of the first and second substances,
Figure BDA00022914891800000411
the location of the ith goblet ascidian (follower) in dimension j is shown.
And 7: calculating the individual adaptive value of each goblet ascidian after updating the position according to the objective function, if less than the current position XiThe current position X is replaced by the updated positioniOtherwise, the current position X is still keptiThe change is not changed;
and 8: for the current position XiAbundance vector of
Figure BDA0002291489180000051
Normalization;
and step 9: judging whether the current iteration times reach the preset iteration times, if so, ending the iteration, and outputting the position of the optimal individual in the current goblet sea squirt group
Figure BDA0002291489180000052
Thereby obtaining abundance vector
Figure BDA0002291489180000053
Non-linear parameter
Figure BDA0002291489180000054
And shadow weights
Figure BDA0002291489180000055
Otherwise, returning to execute the step 6;
step 10: judging whether to mix all pixel points of the hyperspectral image, and if so, finishing the calculation; otherwise, returning to execute the step 4 and calculating the next pixel.
The resulting abundance plot from experiments using the unmixing method of the present invention on Japser Ridge data is shown in FIG. 4. The method is compared with a Bayes method and an FCLS method, and a reconstruction error RE and a spectrum angle distribution SAM are used as performance indexes of experimental results. The results of the experiment are shown in table 1.
TABLE 1 comparison of unmixing results
Figure BDA0002291489180000056
The present invention is not limited to the above-described embodiments. The foregoing description of the specific embodiments is intended to describe and illustrate the technical solutions of the present invention, and the above specific embodiments are merely illustrative and not restrictive. Those skilled in the art can make many changes and modifications to the invention without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (4)

1. A hyperspectral image unmixing method based on a goblet sea squirt group is characterized by comprising the following steps:
firstly, extracting image end members by adopting a geometric-based end member extraction algorithm VCA to obtain an end member spectral curve and an end member number R;
determining a search space dimension D and a position code by utilizing a hyperspectral image SMLM mixed model according to the number R of end members;
step three, setting the population scale, the iteration times and the upper and lower boundaries of the search space of the goblet sea squirt group;
step four, initializing the position of the individual goblet sea squirt group in the search space range;
step five, constructing an objective function, and calculating the current position X of each individual goblet sea squirt groupiAn adapted value for the objective function;
sixthly, the leader individual in the goblet sea squirt group moves according to the position updating formula of the leader, and the follower individual in the goblet sea squirt group moves according to the position updating formula of the follower;
step seven, calculating the individual adaptive value of each goblet ascidian after updating the position according to the objective function, if the individual adaptive value is less than the current position XiThe current position X is replaced by the updated positioniOtherwise, the current position X is still keptiThe change is not changed;
step eight, for the current position XiAbundance vector of
Figure FDA0002291489170000011
Normalization;
step nine, judging whether the current iteration times reach the preset iteration times or not, if so, ending the iteration, and outputting the position of the optimal individual in the current goblet sea squirt group
Figure FDA0002291489170000012
Thereby obtaining abundance vector
Figure FDA0002291489170000013
Non-linear parameter
Figure FDA0002291489170000014
And shadow weights
Figure FDA0002291489170000015
Otherwise, returning to the step six;
step ten, judging whether to perform unmixing on all pixel points of the hyperspectral image, and if so, finishing the calculation; otherwise, returning to the step four, and calculating the next pixel.
2. The method for unmixing hyperspectral images based on a goblet sea squirt group as claimed in claim 1, wherein the second step specifically comprises:
the expression of the hyperspectral image SMLM mixed model is as follows:
Figure FDA0002291489170000016
r is the number of end members, miSpectral curve representing the ith end-member, aiRepresenting the abundance value, abundance vector a, of the ith end-memberi＝[a1,…,aR]TSatisfying the constraint of nonnegativity sum, and the nonlinear parameter P is belonged to [0,1 ∈]The shadow weight Q ∈ [0,1 ]]。
3. The hyperspectral image unmixing method based on the ascidian goblet group according to claim 1, wherein step five comprises: and constructing an objective function by using the reconstruction error, wherein the expression is as follows:
Figure FDA0002291489170000017
wherein, | | · | | represents a two-norm, abundance
Figure FDA0002291489170000021
The non-negative sum must be satisfied as a constraint,
Figure FDA0002291489170000022
representing data reconstructed using the SMLM model, y representing observed data, non-linear parameters
Figure FDA0002291489170000023
Shadow weights
Figure FDA0002291489170000024
4. The hyperspectral image unmixing method based on the ascidian goblet group according to claim 1, wherein the sixth step comprises:
the location update formula for the leader is as follows:
Figure FDA0002291489170000025
wherein the content of the first and second substances,
Figure FDA0002291489170000026
representing the first individual of goblet sea squirt (leader), at the position of dimension j, FjRepresenting the position of the food source in the j dimension, will search the upper boundary ub of the spacejIs 1, lower boundary lbjIs 0, coefficient c1The definition is as follows:
Figure FDA0002291489170000027
where L represents the current iteration number, L represents the total iteration number, and parameter c2And c3Take [0,1]The random number of (a) is set,
the follower's location update formula is as follows:
Figure FDA0002291489170000028
wherein the content of the first and second substances,
Figure FDA0002291489170000029
the location of the ith goblet ascidian (follower) in dimension j is shown.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112085705A (en) * 2020-08-11 2020-12-15 温州大学 Image segmentation method and device based on improved goblet sea squirt group algorithm
CN113255138A (en) * 2021-05-31 2021-08-13 河北工业大学 Load distribution optimization method for power system

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6665438B1 (en) * 1999-05-05 2003-12-16 American Gnc Corporation Method for hyperspectral imagery exploitation and pixel spectral unmixing
CN105809185A (en) * 2015-12-31 2016-07-27 天津大学 High-spectrum image nonlinear demixing method based on neural network and differential search
CN105975912A (en) * 2016-04-27 2016-09-28 天津大学 Hyperspectral image nonlinearity solution blending method based on neural network
CN106056524A (en) * 2016-05-25 2016-10-26 天津商业大学 Hyper-spectral image nonlinear de-mixing method based on differential search
CN109284860A (en) * 2018-08-28 2019-01-29 温州大学 A kind of prediction technique based on orthogonal reversed cup ascidian optimization algorithm

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6665438B1 (en) * 1999-05-05 2003-12-16 American Gnc Corporation Method for hyperspectral imagery exploitation and pixel spectral unmixing
CN105809185A (en) * 2015-12-31 2016-07-27 天津大学 High-spectrum image nonlinear demixing method based on neural network and differential search
CN105975912A (en) * 2016-04-27 2016-09-28 天津大学 Hyperspectral image nonlinearity solution blending method based on neural network
CN106056524A (en) * 2016-05-25 2016-10-26 天津商业大学 Hyper-spectral image nonlinear de-mixing method based on differential search
CN109284860A (en) * 2018-08-28 2019-01-29 温州大学 A kind of prediction technique based on orthogonal reversed cup ascidian optimization algorithm

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
陈涛;王梦馨;黄湘松;: "基于樽海鞘群算法的无源时差定位" *
陈雷;郭艳菊;葛宝臻;: "基于微分搜索的高光谱图像非线性解混算法" *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112085705A (en) * 2020-08-11 2020-12-15 温州大学 Image segmentation method and device based on improved goblet sea squirt group algorithm
CN112085705B (en) * 2020-08-11 2024-03-15 温州大学 Image segmentation method and device based on improved goblet sea squirt swarm algorithm
CN113255138A (en) * 2021-05-31 2021-08-13 河北工业大学 Load distribution optimization method for power system
CN113255138B (en) * 2021-05-31 2023-05-23 河北工业大学 Load distribution optimization method for power system

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