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WO2018192023A1 - Procédé et dispositif de classification d'image de télédétection hyperspectrale - Google Patents

Procédé et dispositif de classification d'image de télédétection hyperspectrale Download PDF

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Publication number
WO2018192023A1
WO2018192023A1 PCT/CN2017/083766 CN2017083766W WO2018192023A1 WO 2018192023 A1 WO2018192023 A1 WO 2018192023A1 CN 2017083766 W CN2017083766 W CN 2017083766W WO 2018192023 A1 WO2018192023 A1 WO 2018192023A1
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remote sensing
sensing image
gabor
hyperspectral remote
features
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PCT/CN2017/083766
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English (en)
Chinese (zh)
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贾森
邓琳
沈琳琳
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深圳大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/253—Fusion techniques of extracted features

Definitions

  • the invention relates to the field of image processing, and in particular to a method and a device for classifying hyperspectral remote sensing images.
  • Hyperspectral remote sensing image refers to the hyperspectral image data obtained by imaging the sensor in different wavelengths in the visible, near-infrared, mid-infrared and thermal infrared bands of the electromagnetic spectrum. Therefore, hyperspectral remote sensing images contain a wealth of spatial, radiation and spectral triple information, which provides a possibility for the fine classification and identification of surface materials.
  • the amplitude information of three-dimensional Gabor features since the amplitude information of three-dimensional Gabor features has good stability, it is usually used directly for classification, but for hyperspectral remote sensing images, the three-dimensional Gabor features are rich. The phase characteristics, so using only the amplitude characteristics of the Gabor feature to classify the surface features will make the classification accuracy low.
  • the embodiment of the invention provides a method and a device for classifying hyperspectral remote sensing images, so as to improve the classification accuracy of the features.
  • an embodiment of the present invention provides a method for classifying a hyperspectral remote sensing image, where the method includes:
  • N is a positive integer
  • the N three-dimensional Gabor amplitude features and the N three-dimensional Gabor phase features are feature-fused by a preset feature fusion algorithm to determine a feature category in the hyperspectral remote sensing image.
  • an embodiment of the present invention provides a hyperspectral remote sensing image classification device, wherein the device includes:
  • An acquiring module configured to acquire N preset Gabor filters, where N is a positive integer
  • An extracting module configured to acquire N three-dimensional Gabor amplitude features and N three-dimensional Gabor phase features of the hyperspectral remote sensing image based on the N preset Gabor filters;
  • a determining module configured to perform feature fusion on the N three-dimensional Gabor amplitude features and the N three-dimensional Gabor phase features by using a preset feature fusion algorithm to determine a feature category in the hyperspectral remote sensing image.
  • N preset Gabor filters are first acquired, and then N three-dimensional Gabor amplitude features of the hyperspectral remote sensing image are acquired based on the N preset Gabor filters.
  • N three-dimensional Gabor phase features and finally feature fusion of the N three-dimensional Gabor amplitude features and the N three-dimensional Gabor phase features by using a preset feature fusion algorithm to determine a category of the features in the hyperspectral remote sensing image .
  • phase feature of the hyperspectral remote sensing image includes rich phase features
  • the three-dimensional Gabor amplitude feature is complementary to the three-dimensional Gabor phase feature
  • the three-dimensional Gabor amplitude feature and the three-dimensional Gabor are merged by the hyperspectral remote sensing image in the embodiment of the present invention.
  • the phase feature is used to determine the feature categories in hyperspectral remote sensing images and improve the accuracy of feature classification for hyperspectral remote sensing images.
  • the three-dimensional Gabor phase information has extremely high sensitivity to the spatial position of the ground object, the three-dimensional Gabor amplitude feature and the three-dimensional Gabor phase feature of the hyperspectral remote sensing image are used for classification, which reduces the classification robustness.
  • FIG. 1 is a schematic flow chart of a first embodiment of a hyperspectral remote sensing image classification method according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of a frequency domain relationship of a three-dimensional Gabor feature according to an embodiment of the present invention
  • 3 is a three-dimensional Gabor filter parallel to the spectral dimension direction observed from three different viewing angles according to an embodiment of the present invention
  • FIG. 4-a is a hyperspectral remote sensing image according to an embodiment of the present invention.
  • Figure 4-b is a three-dimensional Gabor amplitude feature set provided by an embodiment of the present invention.
  • Figure 4-c is a phase feature set and a coding feature set of a hyperspectral remote sensing image according to an embodiment of the present invention
  • FIG. 5 is a schematic diagram showing classification of a hyperspectral remote sensing image provided by an embodiment of the present invention.
  • FIG. 6 is a schematic flow chart of a second embodiment of a hyperspectral remote sensing image classification method according to an embodiment of the present invention.
  • FIG. 7 is a schematic structural diagram of a first embodiment of a hyperspectral remote sensing image classification apparatus according to an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of a second embodiment of a hyperspectral remote sensing image classification apparatus according to an embodiment of the present invention.
  • the embodiment of the invention provides a method and a device for classifying hyperspectral remote sensing images, so as to improve the classification accuracy of the features.
  • N is a positive integer
  • a fusion algorithm performs feature fusion on the N Gabor amplitude features and the N three-dimensional Gabor phase features to determine a feature category in the hyperspectral remote sensing image.
  • FIG. 1 is a schematic flowchart diagram of a first embodiment of a hyperspectral remote sensing image classification method according to an embodiment of the present invention.
  • the hyperspectral remote sensing image classification method provided by the embodiment of the present invention includes the following steps:
  • the N is a positive integer
  • the N preset Gabor filters are used for subsequent extraction of amplitude features and phase features of the hyperspectral remote sensing image.
  • the N preset Gabor filters are Gabor filters that are parallel to the spectral dimension direction of the hyperspectral remote sensing image.
  • N Gabor filters parallel to the spectral dimension direction of the hyperspectral remote sensing image can be obtained by the following formula
  • f is the frequency of the preset Gabor filter
  • I the angle between the preset Gabor filter and the ⁇ axis
  • ⁇ is the angle between the preset Gabor filter and the uv plane.
  • ⁇ is the width of the Gaussian envelope.
  • FIG. 2 is a schematic diagram of a frequency domain relationship of a three-dimensional Gabor feature according to an embodiment of the present invention.
  • the direction indicated by the frequency f is a spectrum of a hyperspectral remote sensing image. Dimension direction.
  • FIG. 3 is a three-dimensional Gabor filter parallel to the spectral dimension direction observed from three different perspectives according to an embodiment of the present invention.
  • the Gabor filter may be a Log-Gabor filter, a haar-Gabor filter.
  • a three-dimensional Gabor feature is obtained by convoluting a hyperspectral remote sensing image with N preset Gabor filters, and then encoding each pixel of the hyperspectral image to further obtain a high Amplitude and phase characteristics of the spectral image.
  • the hyperspectral remote sensing image is convoluted with N three-dimensional Gabor filters to obtain i three-dimensional Gabor features.
  • R represents the hyperspectral remote sensing image
  • G i represents the i-th three-dimensional Gabor feature, where i is an arbitrary integer between 1 and N.
  • the amplitude characteristics M i (x, y, b) and phase characteristics F i (x, y, b) of the hyperspectral remote sensing image are calculated by the following formula:
  • M i (x, y, b) abs(G i (x, y, b));
  • Re(G i (x, y, b)) and Im(G i (x, y, b)) are the real and imaginary parts of the Gabor feature, respectively.
  • the four three-dimensional Gabor filters shown in FIG. 3 are first acquired, and the hyperspectral remote sensing image and the generated four three-dimensional Gabor filters are further performed.
  • the convolution operation four three-dimensional Gabor filter features are obtained, and four three-dimensional Gabor amplitude features G i (x, y, b) and three-dimensional Gabor phase features F i (x, y, b) are further obtained by the above formula. ), where i takes any integer between 1 and 4.
  • 4-a is a hyperspectral remote sensing image according to an embodiment of the present invention, and two Gabor filters in four three-dimensional Gabor filters are taken as For example, the three-dimensional Gabor amplitude feature set of Figure 4-b and the three-dimensional Gabor phase feature set of Figure 4-c are obtained.
  • 4b is a three-dimensional Gabor amplitude feature set provided by an embodiment of the present invention;
  • FIG. 4-c is a phase feature set and a coding feature set of the hyperspectral remote sensing image provided by the embodiment of the present invention.
  • the first column represents the three-dimensional Gabor phase features obtained by the two three-dimensional Gabor filters; the second column represents the features obtained when the real part of the first column of the three-dimensional Gabor features is encoded; A feature obtained by encoding an imaginary part of a first column of three-dimensional Gabor features.
  • other methods may also be used to acquire the three-dimensional Gabor phase features, for example, based on a fusion coding method or a contention based coding method.
  • the preset feature fusion algorithm refers to an algorithm for combining three-dimensional Gabor amplitude features and phase features to simultaneously use three-dimensional Gabor amplitude features and three-dimensional Gabor phase features for classification of remote sensing images.
  • the N-dimensional Gabor amplitude feature and the N three-dimensional Gabor phase features are feature-fused by a preset feature fusion algorithm to determine the hyperspectral remote sensing.
  • the feature categories in the image including:
  • a category similarity measure p based on a Gabor phase characteristics from any of the N-dimensional Gabor phase characteristics of the acquired remote sensing images F i and any of the categories of P P is a positive integer;
  • Determining the confidence in the p categories Measure distance from the similarity
  • the category in which the sum of the squared differences under the N Gabor filters is the largest is the feature class in the hyperspectral remote sensing image.
  • confidence is between 0 and 1, which is used to reflect the probability that the hyperspectral remote sensing image belongs to the p-th category, thus the confidence level
  • the Hamming distance The value is between 0 and 1, which is used to reflect the matching degree between the hyperspectral remote sensing image and the category p, when the Hamming distance The smaller the smaller, the greater the probability that the hyperspectral remote sensing image belongs to the p-class.
  • the basis of any of the N-dimensional Gabor amplitude characteristic of a three-dimensional Gabor amplitude characteristic M i acquires the image sensing P belongs to any one of categories category p Confidence include:
  • the D is a decision matrix of the remote sensing image acquired by the support vector machine, and the n p is the number of non-zero elements of the p-th row in the decision matrix D.
  • the decision matrix P can be obtained by assuming that there is a P-type feature, and for each test sample t, P ⁇ (P-1) are established by using a one-to-one strategy.
  • the confidence is between 0 and 1, and when the confidence is The larger the larger, the greater the probability that the hyperspectral remote sensing image belongs to category p.
  • the addition and multiplication can also be used to calculate the confidence.
  • the similarity measure distance For Hamming distance the acquiring a similarity metric distance between the hyperspectral remote sensing image and any one of the P categories based on any of the N three-dimensional Gabor phase features F i include:
  • the similarity measure between the hyperspectral remote sensing image t and any training sample s in the training set A is obtained by the following formula:
  • B is the spectral dimension of the hyperspectral image
  • the Hamming distance The value is between 0 and 1, and when Hamming distance The smaller the time, the greater the probability that the hyperspectral remote sensing image belongs to the category p, and the Hamming distance for the optimal matching of the hyperspectral remote sensing image and the category p Zero.
  • the foregoing Hamming distance may also be calculated by using a classification based on sparse representation, K-inline classification, and the like.
  • the similarity metric distance may also be other distances, such as a Levinstein distance and a Li distance.
  • the above confidence Distance from Hamming The parameters in the calculation formula are all determined parameters, so when using the above formula to calculate, no parameter estimation is needed, which will make the calculation more accurate.
  • the E p is defined as the confidence Distance from the Hamming The sum of the squared differences under the N Gabor filters, ie Because of confidence The larger the probability, the higher the probability that the hyperspectral remote sensing image belongs to the category p, when the Hamming distance The smaller the hyperspectral imagery greater probability of belonging to the category p, whereby when the three-dimensional integration and three-dimensional Gabor Gabor phase amplitude characteristic feature, the larger E p, the hyperspectral imagery greater probability of belonging to the category p, and finally The category corresponding to the value at which E p is the largest is the feature type in the hyperspectral remote sensing image.
  • the three-dimensional Gabor amplitude feature and the three-dimensional Gabor phase feature of the hyperspectral remote sensing image can be simultaneously used in determining the feature category in the hyperspectral remote sensing image, thereby making the determined category more accurate.
  • the fusion feature value E p may also be defined in other forms, such as a square form, an exponential form, and the like.
  • the four three-dimensional Gabor filters shown in FIG. 3 are first obtained, and then the three-dimensional Gabor amplitudes shown in FIG. 4-b are calculated by using step S102.
  • the three-dimensional Gabor amplitude features shown in Figure 4-c are calculated by using step S102.
  • the hyperspectral remote sensing image 4-a is obtained as the confidence level of each class.
  • the hyperspectral remote sensing image is obtained for each class of Hamming distance.
  • FIG. 5 is a schematic diagram showing classification of hyperspectral remote sensing images according to an embodiment of the present invention.
  • N preset Gabor filters are first acquired, and then N three-dimensional Gabor amplitude features and N three-dimensional Gabors of the hyperspectral remote sensing image are acquired based on the N preset Gabor filters. Phase features, and finally feature fusion of the N three-dimensional Gabor amplitude features and the N three-dimensional Gabor phase features using a preset feature fusion algorithm to determine feature categories in the hyperspectral remote sensing image.
  • the phase feature of the hyperspectral remote sensing image includes rich phase features
  • the three-dimensional Gabor amplitude feature is complementary to the three-dimensional Gabor phase feature
  • the three-dimensional Gabor amplitude feature and the three-dimensional Gabor phase are obtained by fusing the hyperspectral remote sensing image in the embodiment of the present invention.
  • the feature is used to determine the classification of the features in the hyperspectral remote sensing image and improve the accuracy of the classification of the features of the hyperspectral remote sensing image.
  • the three-dimensional Gabor phase information has extremely high sensitivity to the spatial position of the ground object, the three-dimensional Gabor amplitude feature and the three-dimensional Gabor phase feature of the hyperspectral remote sensing image are used for classification, which reduces the classification robustness.
  • FIG. 6 is a schematic flowchart diagram of a second embodiment of a hyperspectral remote sensing image classification method according to an embodiment of the present invention.
  • the same or similar content as the method shown in FIG. 1 can be referred to the detailed description in FIG. 1, and details are not described herein again.
  • the hyperspectral remote sensing image classification method provided by the embodiment of the present invention includes the following steps:
  • S605. Determine the confidence level in the p categories. Measure distance from the similarity The category in which the sum of the squared differences under the N Gabor filters is the largest is the feature class in the hyperspectral remote sensing image.
  • acquiring the target image first acquiring N preset Gabor filters, and then acquiring N three-dimensional Gabor amplitude features of the hyperspectral remote sensing image based on the N preset Gabor filters N three-dimensional Gabor phase features, and finally feature fusion of the N three-dimensional Gabor amplitude features and the N three-dimensional Gabor phase features by using a preset feature fusion algorithm to determine a category of the features in the hyperspectral remote sensing image .
  • the phase feature of the hyperspectral remote sensing image includes rich phase features
  • the three-dimensional Gabor amplitude feature is complementary to the three-dimensional Gabor phase feature
  • the three-dimensional Gabor amplitude feature and the three-dimensional Gabor phase are obtained by fusing the hyperspectral remote sensing image in the embodiment of the present invention.
  • the features are used to determine the feature categories in hyperspectral remote sensing images and improve the accuracy of feature classification for hyperspectral remote sensing images.
  • the three-dimensional Gabor phase information has extremely high sensitivity to the spatial position of the ground object, the three-dimensional Gabor amplitude feature and the three-dimensional Gabor phase feature of the hyperspectral remote sensing image are used for classification, which reduces the classification robustness.
  • the embodiment of the invention further provides a hyperspectral remote sensing image classification device, comprising:
  • An acquiring module configured to acquire N preset Gabor filters, where N is a positive integer
  • An extracting module configured to acquire a hyperspectral remote sensing image based on the N preset Gabor filters Three-dimensional Gabor amplitude features and N three-dimensional Gabor phase features;
  • a determining module configured to perform feature fusion on the N three-dimensional Gabor amplitude features and the N three-dimensional Gabor phase features by using a preset feature fusion algorithm to determine a feature category in the hyperspectral remote sensing image.
  • FIG. 7 is a schematic structural diagram of a first embodiment of a hyperspectral remote sensing image classification device according to an embodiment of the present invention, which is used to implement a hyperspectral remote sensing image classification disclosed in an embodiment of the present invention. method.
  • a hyperspectral remote sensing image classification device 700 according to an embodiment of the present invention may include:
  • the acquisition module 710, the extraction module 720, and the determination module 730 are the acquisition module 710, the extraction module 720, and the determination module 730.
  • the obtaining module 710 is configured to acquire N preset Gabor filters, where the N is a positive integer.
  • the N is a positive integer
  • the N preset Gabor filters are used for subsequent extraction of amplitude features and phase features of the hyperspectral remote sensing image.
  • the N preset Gabor filters are Gabor filters that are parallel to the spectral dimension direction of the hyperspectral remote sensing image.
  • N Gabor filters parallel to the spectral dimension direction of the hyperspectral remote sensing image can be obtained by the following formula
  • f is the frequency of the preset Gabor filter
  • I the angle between the preset Gabor filter and the ⁇ axis
  • ⁇ is the angle between the preset Gabor filter and the uv plane.
  • ⁇ is the width of the Gaussian envelope.
  • FIG. 2 is a schematic diagram of a frequency domain relationship of a three-dimensional Gabor feature according to an embodiment of the present invention.
  • the direction indicated by the frequency f is a spectrum of a hyperspectral remote sensing image. Dimension direction.
  • FIG. 3 is a three-dimensional Gabor filter parallel to the spectral dimension direction observed from three different perspectives according to an embodiment of the present invention.
  • the Gabor filter may be a Log-Gabor filter, a haar-Gabor filter.
  • the extracting module 720 is configured to acquire N three-dimensional Gabor amplitude features and N three-dimensional Gabor phase features of the hyperspectral remote sensing image based on the N preset Gabor filters.
  • a three-dimensional Gabor feature is obtained by convoluting a hyperspectral remote sensing image with N preset Gabor filters, and then encoding each pixel of the hyperspectral image to further obtain a high Amplitude and phase characteristics of the spectral image.
  • a hyperspectral remote sensing image is convoluted with N three-dimensional Gabor filters by the following formula to obtain i three-dimensional Gabor features Gi(x, y, b), Where R represents the hyperspectral remote sensing image, and G i represents the i-th three-dimensional Gabor feature, where i is an arbitrary integer between 1 and N.
  • the amplitude characteristics M i (x, y, b) and phase characteristics F i (x, y, b) of the hyperspectral remote sensing image are calculated by the following formula:
  • M i (x, y, b) abs(G i (x, y, b));
  • Re(G i (x, y, b)) and Im(G i (x, y, b)) are the real and imaginary parts of the Gabor feature, respectively.
  • the four three-dimensional Gabor filters shown in FIG. 3 are first acquired, and the hyperspectral remote sensing image and the generated four three-dimensional Gabor filters are further performed.
  • the convolution operation four three-dimensional Gabor filter features are obtained, and four three-dimensional Gabor amplitude features G i (x, y, b) and three-dimensional Gabor phase features F i (x, y, b) are further obtained by the above formula. ), where i takes any integer between 1 and 4.
  • 4-a is a hyperspectral remote sensing image according to an embodiment of the present invention, and two Gabor filters in four three-dimensional Gabor filters are taken as For example, the three-dimensional Gabor amplitude feature set of Figure 4-b and the three-dimensional Gabor phase feature set of Figure 4-c are obtained.
  • 4b is a three-dimensional Gabor amplitude feature set provided by an embodiment of the present invention;
  • FIG. 4-c is a phase feature set and a coding feature set of the hyperspectral remote sensing image provided by the embodiment of the present invention.
  • the first column represents the three-dimensional Gabor phase features obtained by the two three-dimensional Gabor filters; the second column represents the features obtained when the real part of the first column of the three-dimensional Gabor features is encoded; A feature obtained by encoding an imaginary part of a first column of three-dimensional Gabor features.
  • other methods may also be used to acquire the three-dimensional Gabor phase features, for example, based on a fusion coding method or a contention based coding method.
  • the determining module 730 is configured to perform feature fusion on the N three-dimensional Gabor amplitude features and the N three-dimensional Gabor phase features by using a preset feature fusion algorithm to determine a feature category in the hyperspectral remote sensing image.
  • the preset feature fusion algorithm refers to an algorithm for combining three-dimensional Gabor amplitude features and phase features to simultaneously use three-dimensional Gabor amplitude features and three-dimensional Gabor phase features for classification of remote sensing images.
  • the determining module 730 includes:
  • An acquisition unit 731 based on any of the N-dimensional Gabor amplitude characteristic of a Gabor amplitude characteristic M i of the remote sensing image acquiring a category belongs to the confidence of P p to any category And a category similarity measure p based on a Gabor phase characteristics from any of the N-dimensional Gabor phase characteristics of the acquired remote sensing images F i and any of the categories of P P is a positive integer;
  • a determining unit 732 configured to determine the confidence level in the p categories Measure distance from the similarity
  • the category in which the sum of the squared differences under the N Gabor filters is the largest is the feature class in the hyperspectral remote sensing image.
  • confidence is between 0 and 1, which is used to reflect the probability that the hyperspectral remote sensing image belongs to the p-th category, thus the confidence level
  • the Hamming distance The value is between 0 and 1, which is used to reflect the matching degree between the hyperspectral remote sensing image and the category p, when the Hamming distance The smaller the smaller, the greater the probability that the hyperspectral remote sensing image belongs to the p-class.
  • the obtaining unit 731 is specifically configured to:
  • the D is a decision matrix of the remote sensing image acquired by the support vector machine, and the n p is the number of non-zero elements of the p-th row in the decision matrix D.
  • the decision matrix P can be obtained by assuming that there is a P-type feature, and for each test sample t, P ⁇ (P-1) are established by using a one-to-one strategy.
  • the confidence is between 0 and 1, and when the confidence is The larger the larger, the greater the probability that the hyperspectral remote sensing image belongs to category p.
  • the addition and multiplication can also be used to calculate the confidence.
  • the acquiring unit 731 is specifically configured to:
  • the similarity measure between the hyperspectral remote sensing image t and any training sample s in the training set A is obtained by the following formula:
  • the Hamming distance The value is between 0 and 1, and when Hamming distance The smaller the time, the greater the probability that the hyperspectral remote sensing image belongs to the category p, and the Hamming distance for the optimal matching of the hyperspectral remote sensing image and the category p Zero.
  • the foregoing Hamming distance may also be calculated by using a classification based on sparse representation, K-inline classification, and the like.
  • the similarity metric distance may also be other distances, such as a Levinstein distance and a Li distance.
  • the above confidence Distance from Hamming The parameters in the calculation formula are all determined parameters, so when using the above formula to calculate, no parameter estimation is needed, which will make the calculation more accurate.
  • the E p is defined as the confidence Distance from the Hamming The sum of the squared differences under the N Gabor filters, ie Because of confidence The larger the probability, the higher the probability that the hyperspectral remote sensing image belongs to the category p, when the Hamming distance The smaller the hyperspectral imagery greater probability of belonging to the category p, whereby when the three-dimensional integration and three-dimensional Gabor Gabor phase amplitude characteristic feature, the larger E p, the hyperspectral imagery greater probability of belonging to the category p, and finally The category corresponding to the value at which E p is the largest is the feature type in the hyperspectral remote sensing image.
  • the three-dimensional Gabor amplitude feature and the three-dimensional Gabor phase feature of the hyperspectral remote sensing image can be simultaneously used in determining the feature category in the hyperspectral remote sensing image, thereby making the determined category more accurate.
  • the fusion feature value E p may also be defined in other forms, such as a square form, an exponential form, and the like.
  • the four three-dimensional Gabor filters shown in FIG. 3 are first obtained, and then the three-dimensional Gabor amplitudes shown in FIG. 4-b are calculated by using step S102.
  • the three-dimensional Gabor amplitude features shown in Figure 4-c are calculated by using step S102.
  • the hyperspectral remote sensing image 4-a is obtained as the confidence level of each class.
  • the hyperspectral remote sensing image is obtained for each class of Hamming distance.
  • FIG. 5 is a schematic diagram showing classification of hyperspectral remote sensing images according to an embodiment of the present invention.
  • the hyperspectral remote sensing image classification device 700 first acquires N preset Gabor filters, and then acquires N three-dimensional Gabor amplitudes of the hyperspectral remote sensing image based on the N preset Gabor filters. Value feature and N three-dimensional Gabor phase features, and finally feature fusion of the N three-dimensional Gabor amplitude features and the N three-dimensional Gabor phase features by using a preset feature fusion algorithm to determine features in the hyperspectral remote sensing image
  • the category of the category is a preset Gabor filters.
  • the phase feature of the hyperspectral remote sensing image includes rich phase features
  • the three-dimensional Gabor amplitude feature is complementary to the three-dimensional Gabor phase feature
  • the three-dimensional Gabor amplitude feature and the three-dimensional Gabor phase are obtained by fusing the hyperspectral remote sensing image in the embodiment of the present invention.
  • the features are used to determine the feature categories in hyperspectral remote sensing images and improve the accuracy of feature classification for hyperspectral remote sensing images.
  • the three-dimensional Gabor phase information has extremely high sensitivity to the spatial position of the ground object, the three-dimensional Gabor amplitude feature and the three-dimensional Gabor phase feature of the hyperspectral remote sensing image are used for classification, which reduces the classification robustness.
  • the hyperspectral remote sensing image classification device 700 is presented in the form of a unit.
  • a "unit” herein may refer to an application-specific integrated circuit (ASIC), a processor and memory that executes one or more software or firmware programs, integrated logic circuits, and/or other devices that provide the functionality described above. .
  • ASIC application-specific integrated circuit
  • FIG. 8 is a schematic structural diagram of a second embodiment of a hyperspectral remote sensing image classification apparatus according to an embodiment of the present invention, which is used to implement a hyperspectral remote sensing image classification method disclosed in an embodiment of the present invention.
  • the hyperspectral remote sensing image classification device 800 may include at least one bus 801, at least one processor 802 connected to the bus 801, and at least one memory 803 connected to the bus 801.
  • the processor 802 calls, by using the bus 801, code stored in the memory for acquiring N preset Gabor filters, where N is a positive integer; acquiring hyperspectral remote sensing images based on the N preset Gabor filters N three-dimensional Gabor amplitude features and N three-dimensional Gabor phase features; feature fusion of the N three-dimensional Gabor amplitude features and the N three-dimensional Gabor phase features using a preset feature fusion algorithm to determine the hyperspectral remote sensing The feature category in the image.
  • the N preset Gabor filters are Gabor filters that are parallel to a spectral dimension direction of the hyperspectral remote sensing image.
  • the processor 802 performs feature fusion on the N three-dimensional Gabor amplitude features and the N three-dimensional Gabor phase features by using a preset feature fusion algorithm to determine
  • the feature categories in the hyperspectral remote sensing image include:
  • a category similarity measure p based on a Gabor phase characteristics from any of the N-dimensional Gabor phase characteristics of the acquired remote sensing images F i and any of the categories of P P is a positive integer;
  • Determining the confidence in the p categories Measure distance from the similarity
  • the category in which the sum of the squared differences under the N Gabor filters is the largest is the feature class in the hyperspectral remote sensing image.
  • the processor 802 based on any of the N-dimensional Gabor amplitude characteristic of a three-dimensional Gabor amplitude characteristic M i of the remote sensing images acquired categories in P Confidence in any category p include:
  • the D is a decision matrix of the remote sensing image acquired by the support vector machine, and the n p is the number of non-zero elements of the p-th row in the decision matrix D.
  • the processor 802 is based on any three-dimensional Gabor phase feature F of the N three-dimensional Gabor phase features. i acquires the similarity Hyperspectral image P with any of the categories in a category of distance measure p include:
  • the similarity measure between the hyperspectral remote sensing image t and any training sample s in the training set A is obtained by the following formula:
  • the hyperspectral remote sensing image classification device 800 first acquires N preset Gabor filters, and then acquires N three-dimensional Gabor amplitudes of the hyperspectral remote sensing image based on the N preset Gabor filters. Value feature and N three-dimensional Gabor phase features, and finally feature fusion of the N three-dimensional Gabor amplitude features and the N three-dimensional Gabor phase features by using a preset feature fusion algorithm to determine features in the hyperspectral remote sensing image
  • the category of the category is a preset Gabor filters.
  • the phase feature of the hyperspectral remote sensing image includes rich phase features
  • the three-dimensional Gabor amplitude feature is complementary to the three-dimensional Gabor phase feature
  • the three-dimensional Gabor amplitude feature and the three-dimensional Gabor phase are obtained by fusing the hyperspectral remote sensing image in the embodiment of the present invention.
  • the features are used to determine the feature categories in hyperspectral remote sensing images and improve the accuracy of feature classification for hyperspectral remote sensing images.
  • the three-dimensional Gabor phase information has extremely high sensitivity to the spatial position of the ground object, the three-dimensional Gabor amplitude feature and the three-dimensional Gabor phase feature of the hyperspectral remote sensing image are used for classification, which reduces the classification robustness.
  • the hyperspectral remote sensing image classification device 800 is presented in the form of a unit.
  • a "unit” herein may refer to an application-specific integrated circuit (ASIC), a processor and memory that executes one or more software or firmware programs, integrated logic circuits, and/or other devices that provide the functionality described above. .
  • ASIC application-specific integrated circuit
  • the embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium can store a program, and the program includes some or all of the steps of any hyperspectral remote sensing image classification method described in the foregoing method embodiments.
  • the disclosed apparatus may be implemented in other ways.
  • the device embodiments described above are merely illustrative.
  • the division of the unit is only a logical function division.
  • there may be another division manner for example, multiple units or components may be combined or may be Integrate into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be electrical or otherwise.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention is essential or the part contributing to the prior art or the entire technical solution.
  • the portion or portion may be embodied in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, server or network device, etc.) to perform various embodiments of the present invention. All or part of the steps of the method described.
  • the foregoing storage medium includes: a U disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and the like. .

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Abstract

L'invention concerne un procédé et un dispositif de classification d'image de télédétection hyperspectrale. Le procédé comprend les étapes qui consistent : à acquérir N filtres de Gabor prédéfinis (S101) ; à acquérir N caractéristiques d'amplitude de Gabor tridimensionnelles et N caractéristiques de phase de Gabor tridimensionnelles d'une image de télédétection hyperspectrale sur la base des N filtres de Gabor prédéfinis (S102) ; et à utiliser un algorithme de fusion de caractéristiques pour effectuer une fusion de caractéristiques des N caractéristiques d'amplitude de Gabor tridimensionnelles et des N caractéristiques de phase de Gabor tridimensionnelles de façon à déterminer un type de terrain dans l'image de télédétection hyperspectrale (S103). Grâce à la fusion des caractéristiques d'amplitude de Gabor tridimensionnelles et des caractéristiques de phase de Gabor tridimensionnelles de l'image de télédétection hyperspectrale pour déterminer un type de terrain dans l'image de télédétection hyperspectrale, le procédé augmente la précision de la classification de terrain sur l'image de télédétection hyperspectrale.
PCT/CN2017/083766 2017-04-21 2017-05-10 Procédé et dispositif de classification d'image de télédétection hyperspectrale WO2018192023A1 (fr)

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