CN113591708B - Meteorological disaster monitoring method based on satellite-borne hyperspectral image - Google Patents
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Abstract
The invention discloses a meteorological disaster monitoring method based on a satellite-borne hyperspectral image. Firstly, performing saliency detection on images of each wave band of a hyperspectral image by using a saliency detection algorithm pulse cosine transform to obtain a saliency result image of each wave band of the hyperspectral image, then performing motion detection on the saliency view of the hyperspectral image by using a Lucas-Kanade optical flow detection method to obtain motion detection results of the atmosphere of each wave band, and finally performing weighted optimization on the atmosphere motion detection results of all wave bands by using a genetic algorithm to obtain the motion state of an atmosphere cloud layer so as to achieve the aim of monitoring meteorological disasters.
Description
Technical Field
The invention relates to the field of meteorological disaster monitoring, in particular to a meteorological disaster monitoring method based on a satellite-borne hyperspectral image.
Background
In recent years, with the rapid development of technology, aviation traffic has greatly developed, and convection clouds are generated by rising condensation of water vapor carried by atmospheric convection motion, and are important signs of occurrence of convective weather. The convective weather is likely to form disasters, and particularly the strong convective weather such as short-time strong precipitation, thunderstorm, hail, downburst storm, tornado and the like has the characteristics of strong burst and locality, short life history, heavy disasters and the like. For example, gravitational waves and atmospheric turbulence created by ram convection excitation can propagate hundreds of kilometers in both vertical and horizontal directions, and aircraft traversing these areas can create severe bumps and even threaten flight safety. However, the predictive consistency of the convective cloud is a difficulty in the development of science and technology,
therefore, it is needed to provide a method for detecting the convective cloud to increase the accuracy of meteorological monitoring.
Disclosure of Invention
In order to solve the above-mentioned problems. The invention provides a meteorological disaster monitoring method based on a satellite-borne hyperspectral image, which is used for effectively monitoring a convection cloud and achieving the purpose of:
the invention provides a meteorological disaster monitoring method based on a satellite-borne hyperspectral image, which comprises the following specific steps of:
step 1: collecting information of atmospheric convection cloud hyperspectral images by using a satellite-borne hyperspectral imager, wherein the size of each image is N x ×N y ×N λ Wherein x represents the width of the hyperspectral image space dimension, y represents the height of the hyperspectral image space dimension, and λ represents the number of the spectral dimensions;
step 2: performing significance detection on the data of each wave band of the hyperspectral image by using an improved PCT algorithm to obtain a significance result graph of each wave band of the hyperspectral image;
step 3: removing hyperspectral images with weak salience by using threshold selection to obtain useful hyperspectral salience images;
step 4: performing motion detection on the hyperspectral saliency view by using a Lucas-Kanade optical flow detection method to obtain a motion detection result of the atmosphere in each wave band;
step 5: and carrying out weighted average on detection results of all the wave bands to obtain an evaluation value of the atmospheric movement state, and optimizing the weight of each wave band by using a genetic algorithm.
As a further improvement of the invention, in the step 2, the improved PCT algorithm can be expressed as follows
C＝cos(Y) (2)
Cr＝cos -1 (S) (4)
A＝abs(cr) (5)
P＝G(A) (6)
Wherein X represents a hyperspectral image space dimension image,
representing the mean value of the pixels of the hyperspectral image space dimension image, Y representing the image of the hyperspectral image space dimension image X after DC removal, cos (-) representing the discrete cosine transform, C representing the coefficient of the discrete cosine transform of the signal Y, C (i, j) representing the pixel value of the image C in the ith row and the jth column, S representing the image after the sign of C is taken, S (i, j) representing the pixel value of the image S in the ith row and the jth column, T 1 Representing symbol taking threshold, cos -1 (. Cndot.) represents inverse discrete cosine transform, cr represents coefficients obtained by taking inverse discrete cosine transform on symbol S, and A represents absolute value of CrFor values abs (-) represents the absolute function, P represents the low frequency image of a and G (-) represents the low pass filter.
As a further improvement of the present invention, in the step 3, the threshold selection is expressed as
Gr＝grad(P) (7)
Wherein Gr represents a gradient image of a hyperspectral image space dimension significance image, grad (·) represents a gradient solving function, T2 represents a judging threshold, sum (·) represents a data accumulating solving function, abs (·) represents a data absolute value solving function, su represents a hyperspectral image significance flag bit, su=0 represents that the image significance content is insufficient, and then the hyperspectral image of this dimension is rejected.
As a further improvement of the invention, the method for evaluating the atmospheric motion state in the step 5 can be expressed as
R i ＝LK(Gr i ) (9)
Re＝w 1 R 1 +w 2 R 2 +...+w λ R λ (10)
Where i=1, 2,..lambda, LK (·) represents the Lucas-Kanade optical flow detection algorithm function, gr i Gradient image representing hyperspectral image spatial dimension saliency image of ith spectral dimension, R i Representation Gr i Optical flow detection result, w i R represents i Re represents an evaluation value of the atmospheric motion state.
The meteorological disaster monitoring method based on the satellite-borne hyperspectral image has the beneficial effects that:
1. the invention provides an L-KWSD algorithm for effectively predicting the flow cloud.
2. The invention uses the multidimensional hyperspectral image as a data source, and increases the predictive robustness.
3. The method has low algorithm complexity and simple realization.
Drawings
FIG. 1 is a diagram of a system training model architecture;
FIG. 2 is a system test model architecture diagram;
Detailed Description
The invention provides a meteorological disaster monitoring method based on a satellite-borne hyperspectral image, which is used for effectively monitoring a convection cloud, wherein the method is shown in fig. 1 and 2, which are respectively a system training model structure diagram and a system testing model structure diagram.
Firstly, acquiring hyperspectral image information of atmospheric convection cloud by using a satellite-borne hyperspectral imager, wherein the size of each image is N x ×N y ×N λ Wherein x represents the width of the hyperspectral image space dimension, y represents the height of the hyperspectral image space dimension, and λ represents the number of the spectral dimensions;
then, performing significance detection on the data of each wave band of the hyperspectral image by using an improved PCT algorithm to obtain a significance result graph of each wave band of the hyperspectral image;
the modified PCT algorithm can be expressed as
C＝cos(Y) (2)
Cr＝cos -1 (S) (4)
A＝abs(cr) (5)
P＝G(A) (6)
Wherein X represents a hyperspectral image space dimension image,
representing the mean value of the pixels of the hyperspectral image space dimension image, Y representing the image of the hyperspectral image space dimension image X after DC removal, cos (-) representing the discrete cosine transform, and C representing the letterThe coefficient of discrete cosine transform of the number Y, C (i, j) represents the pixel value of the image C in the ith row and the jth column, S represents the image after the symbol is given to C, S (i, j) represents the pixel value of the image S in the ith row and the jth column, T 1 Representing symbol taking threshold, cos -1 (. Cndot.) represents inverse discrete cosine transform, cr represents coefficients obtained by taking inverse discrete cosine transform on symbol S, A represents absolute value of Cr, abs (-) represents absolute value taking function, P represents low-frequency image of A, and G (-) represents low-pass filter.
Then, removing hyperspectral images with weak salience by using threshold selection to obtain useful hyperspectral salience images;
threshold selection is expressed as
Gr＝grad(P) (7)
Wherein Gr represents a gradient image of a hyperspectral image space dimension significance image, grad (·) represents a gradient solving function, T2 represents a judging threshold, sum (·) represents a data accumulating solving function, abs (·) represents a data absolute value solving function, su represents a hyperspectral image significance flag bit, su=0 represents that the image significance content is insufficient, and then the hyperspectral image of this dimension is rejected.
Finally, motion detection is carried out on the hyperspectral saliency view by using a Lucas-Kanade optical flow detection method, and a motion detection result of the atmosphere in each wave band is obtained; and carrying out weighted average on detection results of all the wave bands to obtain an evaluation value of the atmospheric movement state, and optimizing the weight of each wave band by using a genetic algorithm.
The method for evaluating the atmospheric movement state can be expressed as
R i ＝LK(Gr i ) (9)
Re＝w 1 R 1 +w 2 R 2 +...+w λ R λ (10)
Where i=1, 2,..lambda, LK (·) represents the Lucas-Kanade optical flow detection algorithm function, gr i Gradient image representing hyperspectral image spatial dimension saliency image of ith spectral dimension, R i Representation Gr i Optical flow detection result, w i R represents i Re represents an evaluation value of the atmospheric motion state.
The above description is only of the preferred embodiment of the present invention, and is not intended to limit the present invention in any other way, but is intended to cover any modifications or equivalent variations according to the technical spirit of the present invention, which fall within the scope of the present invention as defined by the appended claims.
Claims (3)
1. The weather disaster monitoring method based on the satellite-borne hyperspectral image comprises the following specific steps,
step 1: collecting information of atmospheric convection cloud hyperspectral images by using a satellite-borne hyperspectral imager, wherein the size of each image is N x ×N y ×N λ Wherein x represents the width of the hyperspectral image space dimension, y represents the height of the hyperspectral image space dimension, and λ represents the number of the spectral dimensions;
step 2: performing significance detection on the data of each wave band of the hyperspectral image by using an improved PCT algorithm to obtain a significance result graph of each wave band of the hyperspectral image;
in said step 2, the modified PCT algorithm is expressed as
C＝cos(Y) (2)
Cr＝cos -1 (S) (4)
A＝abs(Cr) (5)
P＝G(A) (6)
Wherein X represents a hyperspectral image space dimension image,
representing the mean value of the pixels of the hyperspectral image space dimension image, Y representing the image of the hyperspectral image space dimension image X after DC removal, cos (-) representing the discrete cosine transform, C representing the coefficient of the discrete cosine transform of the signal Y, C (i, j) representing the pixel value of the image C in the ith row and the jth column, S representing the image after the sign of C is taken, S (i, j) representing the pixel value of the image S in the ith row and the jth column, T 1 Representing symbol taking threshold, cos -1 (. Cndot.) represents inverse discrete cosine transform, cr represents coefficients obtained by taking inverse discrete cosine transform for symbol S, A represents absolute value of Cr, abs (-) represents absolute value taking function, P represents low-frequency image of A, and G (-) represents low-pass filter;
step 3: removing hyperspectral images with weak salience by using threshold selection to obtain useful hyperspectral salience images;
step 4: performing motion detection on the hyperspectral saliency view by using a Lucas-Kanade optical flow detection method to obtain a motion detection result of the atmosphere in each wave band;
step 5: and carrying out weighted average on detection results of all the wave bands to obtain an evaluation value of the atmospheric movement state, and optimizing the weight of each wave band by using a genetic algorithm.
2. The method for monitoring meteorological disasters based on satellite-borne hyperspectral images according to claim 1, wherein the method comprises the following steps:
in the step 3, the threshold selection is expressed as
Gr＝grad(P) (7)
Wherein Gr represents a gradient image of a hyperspectral image space dimension significance image, grad (·) represents a gradient solving function, T2 represents a judging threshold, sum (·) represents a data accumulating solving function, abs (·) represents a data absolute value solving function, su represents a hyperspectral image significance flag bit, su=0 represents that the image significance content is insufficient, and then the hyperspectral image of this dimension is rejected.
3. The method for monitoring meteorological disasters based on satellite-borne hyperspectral images according to claim 1, wherein the method comprises the following steps:
the method for evaluating the atmospheric movement state in the step 5 can be expressed as
R i ＝LK(Gr i ) (9)
Re＝w 1 R 1 +w 2 R 2 +…+w λ R λ (10)
Where i=1, 2,..lambda, LK (·) represents the Lucas-Kanade optical flow detection algorithm function, gr i Gradient image representing hyperspectral image spatial dimension saliency image of ith spectral dimension, R i Representation Gr i Optical flow detection result, w i R represents i Re represents an evaluation value of the atmospheric motion state.
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