A Nonlinear Simulation Method and System for High-Resolution Multispectral Remote Sensing Imagery

Abstract

This invention provides a nonlinear simulation method and system for high-resolution multispectral remote sensing images. Based on the principle of image scale invariance, this invention derives a nonlinear simulation method for remote sensing images by utilizing the spatial and spectral detail differences between images at different spatial scales. This method has no limitation on the number of bands and can predict the simulation results of remote sensing images. Based on the objective of achieving a suitable balance between multispectral correlation coefficient and panchromatic correlation coefficient, this invention provides an optimized feature extraction coefficient k. I The design feature fusion coefficient is the reciprocal of the feature extraction coefficient. This method provides two image simulation modes: panchromatic band injection into multispectral band and multispectral band injection into panchromatic band, which can realize high-fidelity simulation of the spectral and spatial information of remote sensing images.

Description

Technical Field

[0001] This invention relates to the field of remote sensing image fusion and enhancement technology, and more specifically, to a nonlinear simulation method and system for high-resolution multispectral remote sensing images. Background Technology

[0002] Satellite remote sensing image data is diverse, with different data possessing varying spatial, temporal, and spectral resolutions. Remote sensing image fusion refers to combining the basic features of different remote sensing images into a single new image. Commonly used methods include Brovy fusion and CN fusion.

[0003] For example, patent CN112464834A uses the Brovey fusion method, also known as the color normalization transformation fusion method. It multiplies three multispectral bands with a high spatial resolution panchromatic image, simplifying the image fusion operation coefficients and preserving more of the spectral information of the source image. However, it also has the limitation of only being able to fuse three multispectral bands. Furthermore, existing fusion methods suffer from unpredictable results due to nonlinear simulation. Summary of the Invention

[0004] In view of the above problems, the purpose of this invention is to provide a nonlinear simulation method and system for high-resolution multispectral remote sensing images, which overcomes the unpredictability of traditional nonlinear simulation results of remote sensing images, and proposes a scientific selection method for image feature fusion parameters, which can maintain a high degree of inheritance of the spectral information of the original multispectral image and the spatial detail information of the original panchromatic image in the image simulation results.

[0005] The first aspect of this invention provides a nonlinear simulation method for high-resolution multispectral remote sensing images, the method comprising:

[0006] S1: Acquire satellite remote sensing images, which include a high spatial resolution panchromatic image P and a low spatial resolution multispectral image. The multispectral image includes images of n bands, denoted as M1, M2, ..., Mn. n ;

[0007] S2: Spatial registration of all images to make the geometric spatial position of the same ground object in the panchromatic band image P and the multispectral band image consistent, and resample the multispectral band image participating in the simulation as a high spatial resolution image. The multispectral band image participating in the simulation is denoted as M.

[0008] S3: Constructing intermediate band image I using low-resolution multispectral imagery:

[0009]

[0010] Where l is the preset number of bands in the multispectral band image that participates in the construction of intermediate band image I, and l≤n;

[0011] S4: Select either panchromatic band injection into multispectral band image simulation or multispectral band injection into panchromatic band image simulation, and construct the corresponding image simulation operator, specifically:

[0012] When panchromatic bands are injected into multispectral band images for simulation, a pair of product simulation operators P are constructed. M and I M for:

[0013]

[0014] Where: μ P Let μ be the mean value of the panchromatic image P. I Mean value of intermediate band image I;

[0015] When multispectral bands are injected into panchromatic image simulation, a pair of product simulation operators M are constructed. P and I P for:

[0016]

[0017] Where, μ M Let μ be the mean value of the multispectral band image M. I Mean value of intermediate band image I;

[0018] S5: Select an image simulation method and determine the feature extraction coefficient k based on a moderate balance between multispectral correlation coefficient and panchromatic correlation coefficient. I and feature fusion coefficient k E ,make High-resolution multispectral band image simulation calculations are performed based on remote sensing images, image simulation methods, and characteristic parameters, specifically as follows:

[0019] When panchromatic bands are injected into multispectral band image simulation:

[0020]

[0021] Among them, M f For the simulated low spatial resolution multispectral band M, μ represents the high resolution multispectral band corresponding to the high resolution multispectral band. M , σ Mf M and P respectively M I M The mean square error of Mf, μ M , μ Mf M and P respectively M IM The mean of Mf;

[0022] When multispectral bands are injected into panchromatic image simulation:

[0023]

[0024] Where Mf is the high-resolution multispectral band corresponding to the simulated low spatial resolution multispectral band M, and μ M μ P , μ Mf M, P, M respectively P I P The mean square error of Mf, μ M μ P , μ Mf M, P, M respectively P I P The mean of Mf.

[0025] A second aspect of the present invention provides a nonlinear simulation system for high-resolution multispectral remote sensing images, including a memory and a processor. The memory includes a nonlinear simulation method program for high-resolution multispectral remote sensing images. When the nonlinear simulation method program for high-resolution multispectral remote sensing images is executed by the processor, it implements the steps of the nonlinear simulation method for high-resolution multispectral remote sensing images.

[0026] A third aspect of the present invention provides a computer-readable storage medium including a nonlinear simulation method program for high-resolution multispectral remote sensing images. When the nonlinear simulation method program for high-resolution multispectral remote sensing images is executed by a processor, it implements the steps of the nonlinear simulation method for high-resolution multispectral remote sensing images.

[0027] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0028] This invention provides a nonlinear simulation method, system, and readable storage medium for high-resolution multispectral remote sensing images. Based on the principle of image scale invariance, this invention derives a nonlinear simulation method for remote sensing images by utilizing the spatial and spectral detail differences between images at different spatial scales. This method has no limitation on the number of bands and can predict the simulation results of remote sensing images. Based on the objective of achieving a suitable balance between multispectral correlation coefficients and panchromatic correlation coefficients, this invention provides an optimized feature extraction coefficient k. I The design feature fusion coefficient is the reciprocal of the feature extraction coefficient, i.e. This method provides two image simulation modes: panchromatic band injection into multispectral bands and multispectral band injection into panchromatic bands, which can achieve high-fidelity simulation of the spectral and spatial information of remote sensing images.

[0029] Visual analysis of the simulated high-resolution multispectral imagery results validated the effectiveness of the proposed scheme. Compared to the original multispectral imagery, the simulated high-resolution multispectral imagery significantly enriches the spatial information of ground features, including geometric details, textures, edges, and layers. Furthermore, compared to the original multispectral imagery, the spectral characteristics and color display of ground features such as water bodies, vegetation, exposed surfaces, and buildings remain largely stable in the simulated multispectral imagery. Attached Figure Description

[0030] Figure 1 This is a flowchart of the nonlinear simulation method for high-resolution multispectral remote sensing images in Example 1.

[0031] Figure 2 This is a panchromatic image (0.5-meter resolution).

[0032] Figure 3 This is a multispectral NRG composite image (2-meter resolution).

[0033] Figure 4 This is a multispectral RGB composite image (2-meter resolution).

[0034] Figure 5 This is an image of the intermediate band I (2-meter resolution).

[0035] Figure 6 The standard false-color image is a composite of the near-infrared (Nf), red (Rf), and green (Gf) bands simulated in Scheme 1, using the red, green, and blue channels.

[0036] Figure 7 This is a true-color image synthesized from the red (Nf), green (Gf), and blue (Bf) bands after simulation according to Scheme 1, based on the red, green, and blue channels.

[0037] Figure 8 The standard false-color image is a composite of the near-infrared band (Nf), red band (Rf), and green band (Gf) after simulation according to the red, green, and blue channels in Scheme 2.

[0038] Figure 9 This is a true-color image synthesized from the red band (Nf), green band (Gf), and blue band (Bf) according to the red, green, and blue channels after simulation in Scheme 2. Detailed Implementation

[0039] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0040] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0041] Example 1

[0042] like Figure 1 As shown, this embodiment provides a nonlinear simulation method for high-resolution multispectral remote sensing images, the method comprising:

[0043] S1: Acquire satellite remote sensing images, which include a high spatial resolution panchromatic image P and a low spatial resolution multispectral image. The multispectral image includes images of n bands, denoted as M1, M2, ..., Mn. n ;

[0044] S2: Spatial registration of all images to make the geometric spatial position of the same ground object in the panchromatic band image P and the multispectral band image consistent, and resample the multispectral band image participating in the simulation as a high spatial resolution image. The multispectral band image participating in the simulation is denoted as M.

[0045] This includes spatial registration of all images, specifically:

[0046] Preprocessing: Correcting the original image, including radiometric and atmospheric correction, to eliminate the influence of sensor characteristics and atmospheric conditions on the image;

[0047] Select control points: Determine a set of control points from the panchromatic image P;

[0048] Feature point extraction: Extracting feature points from multispectral images and matching them with control points;

[0049] Image Transformation: Determine the transformation model and transform all images using the transformation model to achieve spatial alignment.

[0050] Specifically, the multispectral band images participating in the simulation will be resampled as high spatial resolution images, as follows:

[0051] S201: Given that the resolution of the high spatial resolution panchromatic image is p meters × p meters, and the resolution of the low spatial resolution multispectral band image is m meters × m meters, determine the upsampling factor s:

[0052]

[0053] S202: Insert pixels into multispectral band images to increase both the horizontal and vertical pixels of the multispectral band images by s times.

[0054] In step S202, the nearest neighbor interpolation method can be used to insert pixels into the multispectral band image, that is, assign the value of the nearest neighbor pixel to the new pixel. This method is simple to calculate and fast, but it may cause discontinuities in the brightness of the image, and is suitable for scenarios where high precision is not required.

[0055] In step S202, a bicubic convolution interpolation method can also be used to insert pixels into the multispectral band image. This involves using the 16 pixel values ​​surrounding the interpolation point and performing interpolation using a cubic convolution function. This method results in less loss of high-frequency information, enhances edges, and is suitable for scenarios requiring high precision.

[0056] S3: Constructing intermediate band image I using low-resolution multispectral imagery:

[0057]

[0058] Where l is the preset number of bands in the multispectral band image that participates in the construction of intermediate band image I, and l≤n;

[0059] S4: Select either panchromatic band injection into multispectral band image simulation or multispectral band injection into panchromatic band image simulation, and construct the corresponding image simulation operator, specifically:

[0060] When panchromatic bands are injected into multispectral band images for simulation, a pair of product simulation operators P are constructed. M and I M for:

[0061]

[0062] Where: μ P Let μ be the mean value of the panchromatic image P. I Mean value of intermediate band image I;

[0063] When multispectral bands are injected into panchromatic image simulation, a pair of product simulation operators M are constructed. P and I P for:

[0064]

[0065] Where, μ M Let μ be the mean value of the multispectral band image M. I Mean value of intermediate band image I;

[0066] S5: Select an image simulation method and determine the feature extraction coefficient k based on a moderate balance between multispectral correlation coefficient and panchromatic correlation coefficient. I and feature fusion coefficient k E High-resolution multispectral band image simulation calculations are performed based on remote sensing images, image simulation methods, and characteristic parameters, specifically as follows:

[0067] When panchromatic bands are injected into multispectral band image simulation:

[0068]

[0069] In the formula:

[0070]

[0071] Where r() represents the correlation coefficient, Mf is the high-resolution multispectral band corresponding to the simulated low spatial resolution multispectral band M, and σ M , σ Mf M and P respectively M I M M f The mean square error, μ M , μ Mf M and P respectively M I M The mean of Mf;

[0072] When multispectral bands are injected into panchromatic image simulation:

[0073]

[0074] In the formula:

[0075]

[0076] Where r() represents the correlation coefficient, Mf is the high-resolution multispectral band corresponding to the simulated low spatial resolution multispectral band M, and σ M σ P , σ Mf M, P, M respectively P I P The mean square error of Mf, μ M μ P , μ Mf M, P, M respectively P I P The mean of Mf.

[0077] The principle of the nonlinear simulation method for high-resolution multispectral remote sensing images of this invention is explained below:

[0078] 1. Scale-invariant transformation of the product image of the mean-filtered image and the ratio image.

[0079] Generally, satellite remote sensing images have multiple low-resolution multispectral bands M1, M2, ..., M n And a high-resolution panchromatic band P. Assume that there exists a low-resolution panchromatic band I (intermediate band) corresponding to the high-resolution panchromatic band P, and a high-resolution multispectral band Mf corresponding to the low-resolution multispectral band M.

[0080] Let μ M μ P μ I μ Mf σ represents the mean values ​​of low-resolution multispectral, high-resolution panchromatic, low-resolution panchromatic, and high-resolution multispectral images, respectively. M σ P σ I σ Mf , respectively, represent the root mean square errors of low-resolution multispectral, high-resolution panchromatic, low-resolution panchromatic, and high-resolution multispectral images. r(P,I) is the correlation coefficient between P and I, and r(Mf,M) is the correlation coefficient between Mf and M.

[0081] Option 1: Panchromatic Injection Multispectral Band Nonlinear Simulation Mode

[0082] (1) The panchromatic product image is proportional to k1

[0083] make:

[0084]

[0085] Inferred from the scale invariance of remote sensing images: (P-μ) P )=k1(I-μ I )

[0086] We can obtain:

[0087] P M =k1I M

[0088] That is, under scale invariance, the product image of the panchromatic mean filtered image and the image of the same dimension are also proportional, and the correlation coefficient between the two product images and any image of the same dimension is equal.

[0089] make:

[0090] E1 = P M -k1I M =0

[0091] For any non-zero vector X1 (X1 has the same dimension as P), then:

[0092] r(E1,X1)=0

[0093] Furthermore:

[0094]

[0095] (2) The multispectral mean-filtered image is proportional to k2.

[0096] make:

[0097] E2=(Mf-μ Mf )-k2(M-μ M ) = 0

[0098] If E2 is independent of any non-zero vector X2 (X2 has the same dimension as P), then:

[0099] r(E2,X2)=0

[0100]

[0101] (3) Scale invariance corollary—the relationship between multispectral images and panchromatic product images

[0102] Depend on:

[0103]

[0104] We can obtain:

[0105]

[0106] Depend on:

[0107]

[0108] We can obtain:

[0109]

[0110] Therefore:

[0111]

[0112] m1 and m2 are any real numbers greater than zero. X1 and X2 are any remote sensing images of the same dimension as M, P, and I.

[0113] (4) Nonlinear simulation method for high-resolution multispectral images

[0114] From the above formula, we can obtain:

[0115]

[0116] Therefore, we can conclude that:

[0117]

[0118] make:

[0119]

[0120] but:

[0121]

[0122] Matching the above results to the multispectral band M, the general scheme for the nonlinear simulation panchromatic injection multispectral mode is as follows:

[0123]

[0124] in,

[0125]

[0126] These are image simulation operators P M I M The mean, These are image simulation operators P M I M The mean squared error of k. I k represents the feature extraction coefficient. E k is the feature fusion coefficient. I k E It is a constant greater than zero.

[0127] In this scheme, let X1∈{M,P,I}, aiming for a suitable balance between multispectral correlation coefficient and panchromatic correlation coefficient, generally the feature fusion coefficient should be the reciprocal of the feature extraction coefficient. In this case, the feature extraction coefficient is k. I The feature fusion coefficient is Right now:

[0128]

[0129] Option 2: Multispectral band injection panchromatic band nonlinear simulation mode (1) Panchromatic product image proportional to k1

[0130] Inferred from the scale invariance of remote sensing images: (P-μ) P )=k1(I-μ I )

[0131] We can obtain:

[0132] P=k1I+μ P -k1μ I

[0133] To make P and I proportional, that is:

[0134] P=k1I

[0135]

[0136] Must have:

[0137]

[0138] Therefore:

[0139]

[0140] (2) The multispectral mean-filtered image is proportional to k2.

[0141] When the mean-filtered image is proportional, the following generally applies:

[0142]

[0143] (3) Scale invariance corollary—The relationship between high-resolution images and low-resolution product images has been derived previously:

[0144]

[0145] make:

[0146]

[0147] m1 and m2 are any real numbers greater than zero. X1 and X2 are any remote sensing images of the same dimension as M, P, and I.

[0148] (4) Nonlinear simulation method for high-resolution multispectral images

[0149] From the above formula, we can obtain:

[0150]

[0151] make

[0152]

[0153] but:

[0154]

[0155] make:

[0156]

[0157] Matching the above results to the multispectral band M, the general scheme for nonlinear simulation of multispectral injection panchromatic mode is as follows:

[0158]

[0159] Where X1 and X2 are images of the same dimension as M, P, and I, and m1 and m2 are any real numbers greater than zero.

[0160] The image analog operator M is respectively P I P The mean, The image analog operator M is respectively P I P The mean squared error of k. I k represents the feature extraction coefficient. E is the feature fusion coefficient.

[0161] make:

[0162]

[0163] Let E respectively spe It is independent of P, M, and I, that is:

[0164] r(E spe ,P)=0

[0165] r(E spe M) = 0

[0166] r(E spe ,I)=0

[0167] We can obtain:

[0168]

[0169] Similarly, in this scheme, the goal is to achieve a proper balance between the multispectral correlation coefficient and the panchromatic correlation coefficient, and the feature fusion coefficient should generally be the reciprocal of the feature extraction coefficient.

[0170]

[0171] This method is applicable to simulating high-resolution multispectral images by using high-resolution panchromatic bands and low-resolution multispectral bands of remote sensing images, so as to enhance the spatial resolution and geometric texture information of multispectral images, while realistically preserving the original spectral information of ground features.

[0172] Example 2

[0173] To verify the effect of nonlinear simulation of high-resolution remote sensing images, this embodiment mainly uses ENVI remote sensing image processing software, and is further described using a satellite remote sensing image with panchromatic (P), blue (B), green (G), red (R), and near-infrared (N) bands.

[0174] 1. Input remote sensing image.

[0175] Open a WV-02 remote sensing image with panchromatic (P), blue (B), green (G), red (R), and near-infrared (N) bands. Figure 2 , Figure 3 , Figure 4 These are, respectively, a panchromatic image (0.5-meter resolution, meaning one pixel represents a ground area of ​​0.5 meters × 0.5 meters), a multispectral NRG composite image (2-meter resolution, meaning one pixel represents a ground area of ​​2 meters × 2 meters), and a multispectral RGB composite image (2-meter resolution) (the result of stretching by 0.1% according to the default ENVI settings).

[0176] 2. Select the multispectral blue band (B), green band (G), red band (R), and near-infrared band (N) to construct the intermediate band I. The calculation formula is: b = fix((1.0*b1+b2+b3+b4) / 4), where b1, b2, b3, and b4 are the B, G, R, and N band images, respectively. Figure 5 This is an image of the intermediate band I (2-meter resolution).

[0177] Option 1: Panchromatic Injection Multispectral Band Nonlinear Simulation

[0178] 3. Constructing image simulation operators

[0179] Taking the panchromatic injection multispectral nonlinear simulation mode as an example, an image simulation operator P is constructed. M and I M Specifically, the simulation operator P M The calculation method is: long((b1-373.051946)*1.0*b3 / b2); Simulation operator I M The calculation method is: long((b2-383.914786)*1.0*b3 / b2); where b1, b2, and b3 are the panchromatic band P, the intermediate band I, and the multispectral band M, respectively.

[0180] 4. Using ENVI software, combine the panchromatic band P, multispectral band M, and fusion operator P. M and I M The high spatial resolution images are resampled and synthesized into a single image file. Then, the mean μ, standard deviation σ, and other image characteristic statistical parameters of each band are calculated. The basic characteristic statistical parameters of each band are shown in Tables 1 and 2.

[0181] Table 1. Statistical table of mean and standard deviation of each image band.

[0182]

[0183]

[0184] Table 2. Statistical table of correlation coefficients for different image bands.

[0185]

[0186] 5. Multispectral band nonlinear simulation calculation

[0187] This case study uses characteristic simulation parameters. Taking this as an example, we perform nonlinear simulation calculations for four bands of multispectral data. The specific simulation algorithm is as follows:

[0188] (1) The analog operation expression corresponding to the blue band is:

[0189] fix(96.636771 / 100.309513*((b3-301.598439)+1.008369*(0.675532*(b1-(-18.950312))-0.9917*0.687211*(b2-(-20.698493))))+301.598439), where b1 is the simulation factor P. B b2 is the simulation factor I B b3 represents the blue band B, and the simulated blue band (Bf) image is obtained by calculation.

[0190] (2) The analog operation expression corresponding to the green band is:

[0191] fix(163.499142 / 170.241165*((b3-409.707032)+1.018724*(0.839874*(b1-(-11.842505))-0.98162*0.86091*(b2-(-13.92145))))+409.707032), where b1 is the simulation factor P. G b2 is the simulation factor I G b3 represents the green band G, and the simulated green band (Gf) image is obtained by calculation.

[0192] (3) The analog operation expression corresponding to the red band is:

[0193] fix(199.762851 / 209.089349*((b3-361.486579)+1.032462*(1.13971*(b1-(12.199673))-0.968559*1.186003*(b2-(10.243729))))+361.486579), where b1 is the simulation factor P. R b2 is the simulation factor I Rb3 represents the red band R, and the simulated red band (Rf) image is obtained by calculation.

[0194] (4) The simulation expression for the near-infrared band is fix(263.263849 / 285.950104*((b3-464.362264)+1.181088*(1.386487*(b1-(18.904132))-0.846677*1.490177*(b2-(24.5701))))+464.362264), where b1 is the simulation factor P. N b2 is the simulation factor I N b3 represents the near-infrared band N, and the simulated near-infrared band (Nf) image is obtained by calculation.

[0195] The simulated near-infrared (Nf), red (Rf), and green (Gf) bands are combined according to the red, green, and blue channels to form a standard false-color image, as shown below. Figure 6 The simulated red band (Nf), green band (Gf), and blue band (Bf) are combined according to the red, green, and blue channels to form a true-color image, as shown below. Figure 7 .

[0196] Option 2: Multispectral injection panchromatic nonlinear simulation

[0197] 3. Constructing image simulation operators

[0198] Taking the panchromatic band injection multispectral band image simulation mode as an example, an image simulation operator M is constructed. P and I P Specifically, the simulation operator B P The calculation formula is: long((b3-301.598439)*(1.0*b1 / b2)); G P The calculation formula is: long((b3-409.707032)*(1.0*b1 / b2)); R P The calculation formula is: long((b3-361.486579)*(1.0*b1 / b2)); N P The calculation formula is: long((b3-464.362264)*(1.0*b1 / b2)); Simulation operator I P The calculation formula is: long((b2-383.914786)*(1.0*b1 / b2)); where b1, b2, and b3 are the panchromatic band P, the intermediate band I, and the multispectral band M, respectively.

[0199] 4. Using ENVI software, combine the panchromatic band P, multispectral band M, and fusion operator M. P and I PThe high spatial resolution images are resampled and synthesized into a single image file. Then, the mean μ, standard deviation σ, and other image characteristic statistical parameters of each band are calculated. The basic characteristic statistical parameters of each band image are shown in Tables 3 and 4.

[0200] Table 3. Statistical table of mean and standard deviation of each image band.

[0201] Image bands mean / μ Root mean square deviation / σ Panchromatic P 373.05195 173.10819 Intermediate band I 383.91479 166.55612 Blue Band B 301.59844 96.636771 Green band G 409.70703 163.49914 Red band R 361.48658 199.76285 Near-infrared N 464.36226 263.26385 <![CDATA[Fusion factor B P > 1.354793 95.155261 <![CDATA[Fusion factor G P > 1.913086 161.48699 <![CDATA[Fusion factor R P > 2.422845 196.83802 <![CDATA[Fusion factor N P > -4.914095 257.04172 <![CDATA[Fusion factor I P > 0.19573 165.4889

[0202] Table 4. Statistical table of correlation coefficients for different image bands

[0203]

[0204] 5. Multispectral band simulation calculation

[0205] This case study uses characteristic simulation parameters. Taking this as an example, we perform nonlinear simulation calculations for four bands of multispectral data. The specific simulation algorithm is as follows:

[0206] (1) The analog operation expression corresponding to the blue band is:

[0207] fix(96.636771 / 182.819211*((b1-373.051946)+0.971622*(1.819218*(b3-(1.354793))-0.964747*1.046041*(b2-(0.19573))))+301.598439), where b1 is the panchromatic band P and b2 is the analog factor I. P b3 is the simulation factor B P The simulated blue band (Bf) image was obtained by calculation.

[0208] (2) The analog operation expression corresponding to the green band is:

[0209] fix(163.499142 / 178.762468*((b1-373.051946)+0.946118*(1.071964*(b3-(1.913086))-0.985545*1.046041*(b2-(0.19573))))+409.707032), where b1 is the panchromatic band P and b2 is the analog factor I. P b3 is the analog factor G P The simulated green band (Gf) image was obtained by calculation.

[0210] (3) The analog operation expression corresponding to the red band is:

[0211] fix(199.762851 / 178.353002*((b1-373.051946)+0.944052*(0.879445*(b3-(2.422845))-0.990107*1.046041*(b2-(0.19573))))+361.486579), where b1 is the panchromatic band P and b2 is the analog factor I. P b3 is the simulation factor R P The simulated red band (Rf) image was obtained by calculation.

[0212] (4) The analog calculation expression for the near-infrared band is fix(263.263849 / 192.152052*((b1-373.051946)+1.02921*(0.673463*(b3-(-4.914095))-0.840993*1.046041*(b2-(0.19573))))+464.362264), where b1 is the panchromatic band P and b2 is the analog factor I. P b3 is the simulation factor N P The simulated near-infrared (Nf) band image was obtained through calculation.

[0213] The simulated near-infrared (Nf), red (Rf), and green (Gf) bands are combined according to the red, green, and blue channels to form a standard false-color image, as shown below. Figure 8 The simulated red band (Nf), green band (Gf), and blue band (Bf) are combined according to the red, green, and blue channels to form a true-color image, as shown below. Figure 9 .

[0214] This case study statistically analyzed the band data of four types of remote sensing images: the original WV-02 multispectral image, the panchromatic injection multispectral simulated image using the method of this invention, the multispectral injection panchromatic simulated image using the method of this invention, and the Gram-Schmidit fused image. The comparison of their image band statistical characteristic parameters is shown in Table 5. As can be seen from the table, compared to the original multispectral image, the spatial resolution of the simulated image using the method of this invention is improved from 2 meters to 0.5 meters, significantly enhancing the spatial accuracy of the image. The simulated image simultaneously incorporates the characteristics of both the original multispectral and panchromatic bands. Compared to the GS fused image, the panchromatic injection multispectral simulated image (Scheme 1) shows better information consistency, with a slight decrease in information entropy, but enhanced gradient information in the bands, resulting in richer spatial information of ground features in the fused multispectral image. Compared to GS fusion images, the multispectral injected panchromatic fusion images (Scheme 2) produced by this method have better consistency between the multispectral bands and the original images. While the information entropy is slightly improved, the image gradient information is basically consistent with the GS fusion images. The spectral information of ground features simulated by this method is more realistic, which is conducive to the development of various image applications such as ground feature identification, interpretation, and analysis.

[0215] This invention discloses a high-resolution multispectral image nonlinear simulation method, system, and readable storage medium, primarily targeting remote sensing images with panchromatic and near-infrared, red, green, and blue multispectral bands. First, based on the assumption of image scale invariance, this method utilizes spatial and spectral detail differences between images at different spatial scales to construct a nonlinear simulation method for high-resolution multispectral images. Second, addressing the difficulty of predicting fusion results in traditional nonlinear image simulation methods, this invention constructs a pair of image simulation operators to achieve predictability of the simulation results. Based on a suitable balance between multispectral and panchromatic correlation coefficients, this invention provides an optimized feature extraction coefficient k. I The design feature fusion coefficient is the reciprocal of the feature extraction coefficient, i.e. Finally, two nonlinear simulation modes are provided: panchromatic injection multispectral and multispectral injection panchromatic.

[0216] This method improves the spectral preservation capability of nonlinear image simulation while maintaining the spatial details of panchromatic images. The fused image is vivid in color, rich in information, stable in spectral information, and easy to visually inspect and automatically classify. Especially in the context of the rapid development of high-resolution satellite remote sensing, it plays a significant role in promoting the application of domestically produced high-resolution imagery in various industries both domestically and internationally.

[0217] Table 5 Comparison of Band Statistical Feature Parameters of Original Multispectral Image, Simulated Image Using This Method, and GS-fused Image

[0218]

[0219] Example 3

[0220] This embodiment provides a nonlinear simulation system for high-resolution multispectral remote sensing images, including a memory and a processor. The memory includes a nonlinear simulation method program for high-resolution multispectral remote sensing images. When the processor executes the nonlinear simulation method program for high-resolution multispectral remote sensing images, it implements the steps of the nonlinear simulation method for high-resolution multispectral remote sensing images of this embodiment.

[0221] Example 4

[0222] This embodiment provides a computer-readable storage medium that includes a nonlinear simulation method program for high-resolution multispectral remote sensing images. When the nonlinear simulation method program for high-resolution multispectral remote sensing images is executed by a processor, it implements the steps of the nonlinear simulation method for high-resolution multispectral remote sensing images described in Embodiment 1.

[0223] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0224] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0225] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0226] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0227] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A nonlinear simulation method for high-resolution multispectral remote sensing images, characterized in that, The method includes: S1: Acquire satellite remote sensing images, which include a high spatial resolution panchromatic image P and a low spatial resolution multispectral image. The multispectral image includes images of n bands, denoted as M1, M2, ..., Mn. n ; S2: Spatial registration of all images to make the geometric spatial position of the same ground object in the panchromatic band image P and the multispectral band image consistent, and resample the multispectral band image participating in the simulation as a high spatial resolution image. The multispectral band image participating in the simulation is denoted as M. S3: Constructing intermediate band image I using low-resolution multispectral imagery: Where l is the preset number of bands in the multispectral band image that participates in the construction of intermediate band image I, and l≤n; S4: Select either panchromatic band injection into multispectral band image simulation or multispectral band injection into panchromatic band image simulation, and construct the corresponding image simulation operator, specifically: When panchromatic bands are injected into multispectral band images for simulation, a pair of product simulation operators P are constructed. M and I M for: Where: μ P Let μ be the mean value of P in the panchromatic image. I Mean value of intermediate band image I; When multispectral bands are injected into panchromatic image simulation, a pair of product simulation operators M are constructed. P and I P for: Where, μ M Let μ be the mean value of the multispectral band image M. I Mean value of intermediate band image I; S5: Select an image simulation method and determine the feature extraction coefficient k based on a moderate balance between multispectral correlation coefficient and panchromatic correlation coefficient. I and feature fusion coefficient k E ,make High-resolution multispectral band image simulation calculations are performed based on remote sensing images, image simulation methods, and characteristic parameters, specifically as follows: When panchromatic bands are injected into multispectral band image simulation: Where Mf is the high-resolution multispectral band corresponding to the simulated low spatial resolution multispectral band M, and σ M , σ Mf M and P respectively M I M The mean square error of Mf, μ M , μ Mf M and P respectively M I M The mean of Mf; When multispectral bands are injected into panchromatic image simulation: Where Mf is the high-resolution multispectral band corresponding to the simulated low spatial resolution multispectral band M, and σ M σ P , σ Mf M, P, M respectively P I P The mean square error of Mf, μ M μ P , μ Mf M, P, M respectively P I P The mean of Mf.

2. The nonlinear simulation method for high-resolution multispectral remote sensing images according to claim 1, characterized in that, In step S2, spatial registration is performed on all images, specifically including: Preprocessing: Correcting the original image, including radiometric and atmospheric correction, to eliminate the influence of sensor characteristics and atmospheric conditions on the image; Select control points: Determine a set of control points from the high spatial resolution panchromatic image P; Feature point extraction: Extract feature points from each image of low spatial resolution multispectral imagery and match them with control points; Image Transformation: Determine the transformation model and transform all images using the transformation model to achieve spatial alignment.

3. The nonlinear simulation method for high-resolution multispectral remote sensing images according to claim 1, characterized in that, In step S2, the multispectral band images participating in the simulation are resampled as high spatial resolution images, specifically as follows: S201: Let the resolution of the high spatial resolution image be p meters × p meters, and the resolution of the low resolution multispectral band image be m meters × m meters. Determine the upsampling factor s: S202: Insert pixels into multispectral band images to increase both the horizontal and vertical pixels of the multispectral band images by s times.

4. The nonlinear simulation method for high-resolution multispectral remote sensing images according to claim 3, characterized in that, In step S202, the nearest neighbor interpolation method is used to insert pixels into the multispectral band image, that is, to assign the value of the nearest neighbor pixel to the new pixel.

5. The nonlinear simulation method for high-resolution multispectral remote sensing images according to claim 3, characterized in that, In step S202, a bicubic convolution interpolation method is used to insert pixels into the multispectral band image, that is, the 16 pixel values ​​around the interpolation point are used to perform interpolation using a cubic convolution function.

6. The nonlinear simulation method for high-resolution multispectral remote sensing images according to claim 1, characterized in that, In step S3, the number of bands in the multispectral band image participating in the construction of intermediate band image I is l≤n, where n is the total number of bands in the multispectral image.

7. A nonlinear simulation system for high-resolution multispectral remote sensing images, characterized in that, The system includes a memory and a processor. The memory includes a nonlinear simulation method program for high-resolution multispectral remote sensing images. When the processor executes the nonlinear simulation method program for high-resolution multispectral remote sensing images, it implements the steps of the nonlinear simulation method for high-resolution multispectral remote sensing images as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a nonlinear simulation method program for high-resolution multispectral remote sensing images. When the nonlinear simulation method program for high-resolution multispectral remote sensing images is executed by a processor, it implements the steps of the nonlinear simulation method for high-resolution multispectral remote sensing images as described in any one of claims 1 to 6.

Images