Image vegetation feature adaptive enhancement method and system and readable storage medium

Abstract

The invention discloses an image vegetation feature adaptive enhancement method and system and a readable storage medium. The method comprises the following steps: inputting a multispectral remote sensing image; calculating a characteristic ratio index of the remote sensing image; determining a feature threshold value and a feature transformation function of the feature ratio index; constructing a smooth constraint function space; constructing a characteristic ratio exponential transformation function symmetric curve based on three-point constraint; constructing a vegetation feature enhancement coefficient function and a fusion expression of vegetation feature enhancement; and synthesizing a true color image after vegetation enhancement. According to the method, a three-point constrained characteristic curve is constructed as a vegetation characteristic enhancement increment function of the remote sensing image, and automatic selection of enhancement parameters such as enhancement power and symmetric curve slope is realized based on data characteristics of the image, so that the automation, standardization and standardization degrees of a large-range mass remote sensing image enhancement processing process are improved; and a technical basis is provided for standardized enhancement of massive remote sensing images.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image information enhancement, and more specifically, to an image vegetation feature adaptive enhancement method, system and readable storage medium. Background Art

[0002] With the development of multi-platform, multi-sensor, multi-weather, multi-temporal, and multi-resolution remote sensing technology, remote sensing images with different spatial resolutions, temporal resolutions, and spectral resolutions are becoming increasingly abundant. In the past two decades, remote sensing image spatiotemporal fusion has become a new direction in remote sensing image processing, and various fusion technologies have developed rapidly, achieving a series of new results. However, there are few studies on the enhancement of multispectral image inter-spectral fusion, which are mainly concentrated in true color image simulation or vegetation feature enhancement processing.

[0003] With the popularization of remote sensing applications in all walks of life, multispectral satellite remote sensing true color images have the excellent characteristic of what you see is what you get, and have become one of the most widely used remote sensing image types. However, the original true color images have inherent defects such as unnatural and unreal vegetation colors, which restricts their application effectiveness. How to effectively enhance the vegetation characteristics of satellite remote sensing true color images is the focus and difficulty of satellite remote sensing true color image processing. Relevant researchers have made fruitful explorations for this purpose, laying a theoretical and technical foundation for further solving related problems.

[0004] Based on the primary products of satellite remote sensing, Chen Chun et al. corrected the remote sensing data for Rayleigh scattering, making the color signal image close to the ground true color image (Chen Chun et al., Extraction and Reproduction of Color Signals of Remote Sensing Sources, Surveying and Mapping Science, January 2006, Vol. 31, No. 1; Han Xiuzhen et al., Research and Application of Fengyun-3D True Color Image Synthesis Method, Journal of Marine Meteorology, May 2019, Vol. 39, No. 2). You Jing et al. used the white balance method and color correction based on colorimetry to improve the vegetation characteristics of true color images and obtained more realistic true color images (You Jing et al., A White Balance Method for Processing Color Multispectral Images, Journal of Atmospheric and Environmental Optics, July 2012, Vol. 7, No. 4; Huang Honglian et al., True Color Synthesis of Multispectral Remote Sensing Images Based on Artificial Targets, November 2016, Vol. 45, No. 11).

[0005] Based on the products of secondary processing of remote sensing images, Fan Xuyan and others enhanced the green band of remote sensing images to obtain relatively good true color images. In the early days, the overall weighted combination operation scheme of green band and near infrared band was mainly used to obtain new green bands (Fan Xuyan et al., Remote sensing image simulation true color fusion method based on principal component analysis, Journal of Surveying and Mapping Science and Technology, August 2006, Vol. 23, No. 4; Wang Haiyan et al., Discussion on ALOS natural color image transformation and fusion method, Surveying and Mapping Technology and Equipment, Vol. 14, No. 1, 2012; Shi Yuanli et al., Analysis on the applicability of high-resolution satellite remote sensing image mapping, Bulletin of Surveying and Mapping, No. 12, 2017); later it gradually developed into a method based on the real color fusion of remote sensing images ... The normalized vegetation index is used as a classification function to classify and weight the vegetation pixels in the image to obtain a new green band (Zhang Wei et al., True color synthesis method of multispectral images based on vegetation index, Surveying and Spatial Geographic Information, Vol. 33, No. 6, December 2010); the recent development is to use the normalized vegetation index segmentation to perform Contourlet fusion on the green band and the near-infrared band to obtain a new green band image (Ding Huimei, Research on improving the color naturalness of multispectral remote sensing images using near-infrared, Master's thesis, 2016).

[0006] Although the above methods can achieve true color image vegetation enhancement to a certain extent, the selection of feature enhancement parameters in the color image enhancement process mainly relies on manual selection, which leads to insufficient automation, normalization and standardization of color image enhancement. Summary of the invention

[0007] In view of the above problems, an object of the present invention is to provide a method, system and readable storage medium for adaptively enhancing vegetation features in images.

[0008] A first aspect of the present invention provides a method for adaptively enhancing vegetation features in an image, the method comprising:

[0009] Input multispectral remote sensing images with near infrared band, red band, green band and blue band;

[0010] Calculate the characteristic ratio index of remote sensing images;

[0011] According to the characteristic ratio index of the remote sensing image, the characteristic threshold and characteristic transformation function of the characteristic ratio index are determined;

[0012] According to the characteristic ratio index and its characteristic threshold, a smooth constraint function space is constructed;

[0013] According to the smooth constraint function, a symmetric curve of the characteristic ratio exponential transformation function based on three-point constraints is constructed;

[0014] According to the symmetric curve of the characteristic ratio exponential transformation function based on three-point constraints, the vegetation feature enhancement coefficient function and the fusion expression of vegetation feature enhancement are constructed;

[0015] The true color image after vegetation enhancement is synthesized according to the vegetation feature enhancement coefficient function and the fusion expression of vegetation feature enhancement.

[0016] Preferably, the calculating of the characteristic ratio index of the remote sensing image specifically includes:

[0017] Assume b i is a band of remote sensing image, the remote sensing image bands involved in fusion are near infrared band and m true color bands; the number of bands of the image result after fusion is m, and the fusion result is recorded as b' i ;

[0018] The characteristic ratio index is:

[0019]

[0020] Where n=1,…,m.

[0021] Preferably, the determining of the characteristic threshold of the characteristic ratio index comprises:

[0022] Calculate the minimum value x of the characteristic ratio index x min , maximum value x max and the mean value x m ;

[0023] Determine the threshold x of pure water through human-computer interaction w , the threshold value x of pure bare ground b , the threshold value of pure vegetation x v ; then the feature threshold c∈[x w , x v ]; By default, c = x m .

[0024] Preferably, the feature transformation function expression is:

[0025]

[0026] Where n>0, sign is the sign operator, and abs is the absolute value operator;

[0027] s and t are transformation parameters; when s = 1, t = 0, h(x) is the characteristic exponential power transformation; when s = 1, t = 1-c, h(x) is the characteristic exponential translation power transformation; when s = c, t = 0, h(x) is the characteristic exponential scaling power transformation; if c = 1, the three transformations are equivalent, all of which are h(x) = x n .

[0028] Preferably, the smooth constraint function space is constructed according to the feature threshold, specifically:

[0029] Suppose the smooth constraint function u(x) of the characteristic ratio index x is at the maximum value x max The value of is 1, that is:

[0030]

[0031] The smooth constraint function v(x) of the normalized exponential corresponding to the characteristic ratio index is at the maximum value x max The value of is also 1, that is:

[0032]

[0033] g(x)=C s v(x)

[0034] l(x)=C s u(x)

[0035] Among them, g(x) is the point passing through (c,0), (x max ,C s ) is the incremental function of vegetation feature enhancement, l(x) is the point passing through (c,0), (x max ,C s ) of the straight line, C s >0 is a constant, which is the maximum value of the characteristic ratio index x max The value of the normalized exponential smoothing constraint function v(x) at .

[0036] Preferably, constructing a symmetric curve of a characteristic ratio exponential transformation function based on three-point constraints according to a smooth constraint function comprises:

[0037] According to the smooth constraint function, the three points of the three-point constraint are determined as (x min ,0), (c,0) and (x max ,C s );

[0038] The normalized exponential smoothing constraint function and symmetry axis of the characteristic ratio exponential transformation function are:

[0039] g(h(x))=C s v(h(x))

[0040] l(h(x))=kA(h(x))

[0041] in,

[0042] A(h(x))=h(x)-h(c)

[0043]

[0044] Let f(h(x)) be the symmetric curve of the normalized exponential smoothing constraint function g(h(x)) about the straight line l(h(x)), and its expression is:

[0045]

[0046] in,

[0047]

[0048] Let f'(h(x)) = 0, we can get f(h(x)) has a minimum value at x0, and x0∈(x min ,c), at this time:

[0049]

[0050] Let f(h(x0)) / C s =ε, the power n of the transformation function can be automatically determined according to the image data characteristics, thereby automatically determining the slope k of the symmetric curve. At this time, affected by (x min ,0), (c,0) and (x max ,C s ) and the three-point constrained symmetric curve f(h(x)) is the desired vegetation feature enhancement increment function.

[0051] Preferably, the construction of the vegetation feature enhancement coefficient function and the fusion expression of vegetation feature enhancement includes:

[0052] The increment function f(h(x)) is normalized, and the vegetation feature enhancement coefficient function F(x) is constructed according to the superposition principle:

[0053] F(x)=m·f(h(x)) / C s +1

[0054] m is the greenness adjustment coefficient, which has the function of adjusting the greenness of the image. m>0 is a constant, and the default value is m=1;

[0055] Assuming that the red, green, and blue bands of the remote sensing image are R, G, and B respectively, the red and green bands are enhanced using the above enhancement coefficients, and the image fusion expression for vegetation feature enhancement is as follows:

[0056] R'=F(x)×R

[0057] G'=F(x)×G

[0058] Among them, R' is the red band after the vegetation characteristics are enhanced, and G' is the green band after the vegetation characteristics are enhanced.

[0059] Preferably, the synthesis of the vegetation enhanced true color image according to the vegetation feature enhancement coefficient function and the fusion expression of vegetation feature enhancement is specifically:

[0060] The red band R' after vegetation feature enhancement, the green band G' after vegetation feature enhancement and the blue band B are placed correspondingly in the red, green and blue channels to synthesize the color image, which is the true color image vegetation feature enhanced image.

[0061] Preferably, the method further comprises the following steps: storing the enhanced true color image.

[0062] The second aspect of the present invention provides an image vegetation feature adaptive enhancement system, including a memory and a processor, wherein the memory includes an image vegetation feature adaptive enhancement method program, and when the image vegetation feature adaptive enhancement method program is executed by the processor, the steps of an image vegetation feature adaptive enhancement method are implemented.

[0063] The third aspect of the present invention provides a computer-readable storage medium, which includes an image vegetation feature adaptive enhancement method program. When the image vegetation feature adaptive enhancement method program is executed by a processor, the steps of an image vegetation feature adaptive enhancement method are implemented.

[0064] Compared with the prior art, the beneficial effects of the technical solution of the present invention are as follows: the present invention provides a method, system and readable storage medium for adaptively enhancing vegetation features in an image. min ,0), (c,0) and (x max ,C s ) The characteristic curve with three-point constraints is used as the incremental function of vegetation feature enhancement of remote sensing true color images. The automatic selection of enhancement parameters such as enhancement power n and symmetry curve slope k is realized based on the image's own data characteristics. This reduces the human dependence on the selection of feature enhancement parameters in the previous true color image enhancement, improves the automation, normalization and standardization of the large-scale massive remote sensing image enhancement processing process, and provides a technical basis for the standardized enhancement of massive remote sensing images. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a flow chart of a method for adaptively enhancing vegetation features in an image as described in Example 1.

[0066] Figure 2 This is a GF6 true color composite map of a certain area on January 18, 2021.

[0067] Figure 3 This is the GF6 characteristic ratio index chart of a certain region on January 18, 2021.

[0068] Figure 4This is a true color composite image of a certain area after GF6 enhancement on January 18, 2021. DETAILED DESCRIPTION

[0069] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0070] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0071] Example 1

[0072] like Figure 1 As shown, this embodiment discloses a method for adaptively enhancing vegetation features in an image, the method comprising:

[0073] S1: Input remote sensing images with near infrared band, red band, green band and blue band;

[0074] S2: Calculate the characteristic ratio index of remote sensing images;

[0075] More specifically, the calculation of the characteristic ratio index of the remote sensing image includes:

[0076] Let x be the characteristic ratio index of vegetation feature enhancement. The index value has the characteristic of increasing from water to exposed objects such as rock and soil to vegetation objects, and can perform binary classification on objects.

[0077] Assume b i is a band of remote sensing image, the remote sensing image bands involved in fusion are near infrared band and m true color bands; the number of bands of the image result after fusion is m, and the fusion result is recorded as b' i ;

[0078] The characteristic ratio index is:

[0079]

[0080] Where n = 1, ..., m, m is the number of true color bands involved in the fusion, m = 1, 2, 3. b' i It is the result of spectral fusion between the near-infrared band NIR and one or more bands in the true color band. Spectral fusion can adopt various image band fusion methods such as IHS, Brovey, Gram-Schmidt, PCA, CN, Wavelet, etc.

[0081] S3: Determine the characteristic threshold and characteristic transformation function of the characteristic ratio index according to the characteristic ratio index of the remote sensing image;

[0082] More specifically, the determining of the characteristic threshold of the characteristic ratio index includes:

[0083] Calculate the minimum value x of the characteristic ratio index x min , maximum value x max and the mean value x m ;

[0084] Determine the threshold x of pure water through human-computer interaction w , the threshold value x of pure bare ground b , the threshold value of pure vegetation x v ; then the feature threshold c∈[x w , x v ]; By default, c=x m , where c is the feature threshold for true color satellite image feature enhancement

[0085] More specifically, the feature transformation function is a function obtained by performing power transformation, translation power transformation, and scaling power transformation on the feature ratio index x at the feature threshold c, and its expression is:

[0086]

[0087] Where n>0, sign is the sign operator, and abs is the absolute value operator;

[0088] s and t are transformation parameters; when s = 1, t = 0, h(x) is the characteristic exponential power transformation; when s = 1, t = 1-c, h(x) is the characteristic exponential translation power transformation; when s = c, t = 0, h(x) is the characteristic exponential scaling power transformation; if c = 1, the three transformations are equivalent, all of which are h(x) = x n .

[0089] It should be noted that the characteristic transformation function h(x) has the same monotonic characteristics as the characteristic ratio index x, that is, it increases along the axis from water to exposed objects such as rock and soil to vegetation objects, and has an equivalent classification function, that is, the left and right sides of the threshold c divide the objects into two categories with the same characteristics. Using h(x) as the basic parameter for vegetation feature enhancement can effectively expand the vegetation enhancement capability of the characteristic ratio index.

[0090] S4: Construct a smooth constraint function space based on the characteristic ratio index and its characteristic threshold;

[0091] More specifically, a smooth constraint function space is constructed, specifically:

[0092] Suppose the smooth constraint function u(x) of the characteristic ratio index x is at the maximum value xmax The value of is 1, that is:

[0093]

[0094] The smooth constraint function v(x) of the normalized exponential corresponding to the characteristic ratio index is at the maximum value x max The value of is also 1, that is:

[0095]

[0096] g(x)=C s v(x)

[0097] l(x)=C s u(x)

[0098] Among them, g(x) is the point passing through (c,0), (x max ,C s ) is the incremental function of vegetation feature enhancement, l(x) is the point passing through (c,0), (x max ,C s ) of the straight line, C s >0 is a constant, which is the maximum value of the characteristic ratio index x max The value of the normalized exponential smoothing constraint function v(x) at .

[0099] S5: constructing a symmetric curve of the characteristic ratio exponential transformation function based on three-point constraints according to the smooth constraint function;

[0100] More specifically, constructing a symmetric curve of a characteristic ratio exponential transformation function based on three-point constraints according to a smooth constraint function includes:

[0101] According to the smooth constraint function, the three points of the three-point constraint are determined as (x min ,0), (c,0) and (x max ,C s );

[0102] The normalized exponential smoothing constraint function and symmetry axis of the characteristic ratio exponential transformation function are:

[0103] g(h(x))=C s v(h(x))

[0104] l(h(x))=kA(h(x))

[0105] in,

[0106] A(h(x))=h(x)-h(c)

[0107]

[0108] Let f(h(x)) be the symmetric curve of the normalized exponential smoothing constraint function g(h(x)) about the straight line l(h(x)), and its expression is:

[0109]

[0110] in,

[0111]

[0112] Since the smooth constraint function g(h(x)) and the line l(h(x)) both pass through the points (c,0) and (x max ,C s ), then f(h(x)) must pass through (c,0) and (x max ,C s )Two points.

[0113] Let f(h(x min ))=0, then the symmetric curve f(h(x)) passes through (x min ,0) point, then:

[0114] A(h(x min ))=a3

[0115]

[0116] make:

[0117] T(k n )=a3-[h(x min )-h(c)]

[0118] T'(k n )=a3'

[0119] Then the slope k of the symmetric curve f(h(x)) is a function related to the power n, and its expression is:

[0120]

[0121] Let f'(h(x)) = 0, we can get f(h(x)) has a minimum value at x0, and x0∈(x min ,c), at this time:

[0122] (a1 2 +1)A 2 (h(x))+(2a1a2-a3)A(h(x))-a1a2a3+a3 2 / 4=0

[0123] a=a1 2 +1

[0124] b=2a1a2-a3

[0125] c=a3 2 / 4-a1a2a3

[0126]

[0127] A(h(x))=h(x)-h(c)

[0128] h(x)=A(h(x))+h(c)

[0129]

[0130] Let f(h(x0)) / C s =ε, the power n of the transformation function can be automatically determined according to the image data characteristics, so as to scientifically determine the slope k of the symmetric curve, and automatically select the incremental function parameters according to the remote sensing image data characteristics, which improves the deficiency of image enhancement parameters relying on manual selection and improves the automation, normalization and standardization of image enhancement. At this time, affected by (x min ,0), (c,0) and (x max ,C s ) and the three-point constrained symmetric curve f(h(x)) is the desired vegetation feature enhancement increment function.

[0131] S6: According to the symmetric curve of the characteristic ratio exponential transformation function based on the three-point constraint, the vegetation feature enhancement coefficient function and the fusion expression of vegetation feature enhancement are constructed;

[0132] More specifically, the construction of the vegetation feature enhancement coefficient function and the fusion expression of vegetation feature enhancement specifically includes:

[0133] The increment function f(h(x)) is normalized, and the vegetation feature enhancement coefficient function F(x) is constructed according to the superposition principle:

[0134] F(x)=m·f(h(x)) / C s +1

[0135] m is the greenness adjustment coefficient, which has the function of adjusting the greenness of the image. m>0 is a constant, and the default value is m=1;

[0136] Assuming that the red, green, and blue bands of the remote sensing image are R, G, and B respectively, the red and green bands are enhanced using the above enhancement coefficients, and the image fusion expression for vegetation feature enhancement is as follows:

[0137] R'=F(x)×R

[0138] G'=F(x)×G

[0139] Among them, R' is the red band after the vegetation characteristics are enhanced, and G' is the green band after the vegetation characteristics are enhanced.

[0140] S7: synthesize the vegetation enhanced true color image according to the vegetation feature enhancement coefficient function and the fusion expression of vegetation feature enhancement.

[0141] More specifically, the synthesis of the vegetation enhanced true color image according to the vegetation feature enhancement coefficient function and the fusion expression of vegetation feature enhancement is specifically:

[0142] The red band R' after vegetation feature enhancement, the green band G' after vegetation feature enhancement and the blue band B are placed correspondingly in the red, green and blue channels to synthesize the color image, which is the true color image vegetation feature enhanced image.

[0143] S8: Store the enhanced true color image.

[0144] The present embodiment provides a method, system and readable storage medium for adaptive enhancement of vegetation features in an image. The incremental coefficient function constructed in the present embodiment is a continuous function, which is 0 at the minimum value and threshold of the feature ratio index, a very small negative value between the minimum value and the threshold, and a sufficiently large value at a value greater than the threshold, thereby ensuring that the enhanced image is smooth at the threshold and maintaining the stability of non-vegetation features while enhancing the vegetation features of the remote sensing image.

[0145] This embodiment automatically determines the parameters of the increment coefficient function according to the data characteristics of the remote sensing image itself, thereby improving the degree of automation, normalization and standardization of the processing of massive remote sensing images.

[0146] This embodiment uses remote sensing image data to construct a characteristic curve constrained by three points as an incremental function, and realizes the normalized automatic selection of parameters such as the power n of the incremental function and the slope k of the symmetric curve, which can standardize a large range of massive remote sensing images and improve the consistency of the enhancement effect of multi-period and multi-scene remote sensing images. At the same time, this embodiment can significantly enhance the amount of vegetation information in remote sensing images, comprehensively improve the vegetation characteristics of remote sensing images, and effectively improve the visual resolution of true color images and the computer analysis ability.

[0147] The method described in this embodiment significantly improves the overall performance of the true color image by directionally enhancing the vegetation feature information of the true color image and maintaining the stability of the image features of exposed objects such as water bodies, soil, rocks, and buildings in the image, reduces the correlation of images in each band, reduces information redundancy, optimizes the data structure, and improves the image information entropy and gradient, so that the vegetation information of the true color image is richer, the color, texture, layer and feature differences of different objects are richer and more significant, and the overall performance is better.

[0148] As a specific embodiment, the following is described in conjunction with a specific example:

[0149] In order to achieve the purpose of enhancing the vegetation features of satellite remote sensing true color images, this embodiment mainly uses ENVI remote sensing image processing software to achieve it, and is further described by a remote sensing image with a near infrared band (NIR), a red light band (R), a green light band (G), and a blue light band (B).

[0150] 1. Input remote sensing image.

[0151] Open a multispectral remote sensing image with near infrared band (NIR), red band (R), green band (G), and blue band (B). Figure 2 This is a true color composite color image before vegetation feature enhancement (the effect of 0.1% stretching according to the default setting of envi).

[0152] 2. Calculate the characteristic ratio index

[0153] Take the feature ratio index obtained by GS fusion as an example.

[0154]

[0155] GSR, GSG and GSB are the results of fusion of NIR with R, G and B according to the GS method.

[0156] According to the above formula, the band operation expression (1.0*b1+b2+b3) / (1.0*b4+b5+b6) is established to calculate the characteristic ratio index x. Among them, b1 is the red band obtained by GS method fusion, b2 is the green band obtained by GS method fusion, b3 is the blue band obtained by GS method fusion, b4 is the original red band, b5 is the original green band, and b6 is the original blue band. The calculation results are as follows Figure 3 . (The effect of 0.1% stretching according to the default setting of envi).

[0157] 3. Determine the feature threshold and the translation transformation feature power and feature curve

[0158] Apply ENVI statistical tools to find the mean value x m , and use it as the feature threshold c, that is, c = x m =1.066517. The maximum value of x max =2.921482, the minimum value of x min =0.258185.

[0159] In this embodiment, the parameters of the power transformation are s=1 and t=0. Let ε=-0.01, then f(h(x)) / C s =ε=-0.01, automatically calculate and solve n=1.390986, then the power transformation function h(x)=x 1.390986 , we calculated k = 0.620061, Cs =2.076579, the symmetric curve f(h(x)) has a minimum value at x=0.724515, then the symmetric curve function f(h(x)) of the characteristic ratio index x transformation space based on the three-point constraint is:

[0160] f(h(x))=(1*sqrt(((-0.759198)*(x^1.390986-1.093712)+(4.964122))^2+(x^1.390986-1.093712 )^2-(-0.941655)*(x^1.390986-1.093712))-((-0.759198)*(x^1.390986-1.093712)+(4.964122)))

[0161] 4. Enhanced calculation of vegetation features in red and green bands and true color synthesis.

[0162] Taking the greenness adjustment coefficient m = 1, the image vegetation feature enhancement coefficient is:

[0163] F(h(x))=f(h(x)) / C s +1

[0164] R'=F(x)×R

[0165] G'=F(x)×G

[0166] According to the above formula, enter the calculation expression: uint((((1*sqrt(((-0.759198)*(b1^1.390986-1.093712)+(4.964122))^2+(b1^1.390986-1.093712)^2-(-0.941655)*(b1^1.390986-1.093712))-((-0.759198)*(b1^1.390986-1.093712)+(4.964122))) / 2.07657941)+1)*b2), where b1 is the characteristic ratio index x, and b2 is the red band or green band of the true color image. The enhanced red band, green band and original blue band are combined into a color image synthesized according to the red, green and blue channels to obtain an enhanced true color image. Figure 4 (The result is a 0.1% stretch according to the default setting of envi.) The reconstructed image file is the result.

[0167] The statistical characteristics of the color image after vegetation feature enhancement in this case and the original true color image in RGB color mode are compared and analyzed in Tables 1 and 2. The statistical characteristics of the whole image and enhanced area in HLS color mode are compared and analyzed in Table 3.

[0168] Table 1 Comparative analysis of RGB mode statistical features of true color images after vegetation feature enhancement and original true color images

[0169]

[0170]

[0171] Table 2 Comparative analysis of vegetation regional statistical characteristics of true color images after vegetation feature enhancement and original true color images in RGB mode

[0172]

[0173] Table 3 Comparative analysis of statistical characteristics of HLS mode between true color images after vegetation feature enhancement and original true color images

[0174]

[0175]

[0176] The satellite remote sensing image vegetation feature adaptive enhancement technology described in this embodiment is mainly aimed at the inherent defects of the vegetation features of satellite remote sensing images with near-infrared, red, green and blue bands. According to the intrinsic relationship of the remote sensing band data, an enhancement formula based on a three-point constrained symmetric curve is constructed, and the image enhancement incremental function parameters are automatically determined according to the image's own data characteristics, realizing the automation, normalization and standardization of image enhancement. The technical method has a clear physical meaning and a wide range of applications. The parameter selection depends on the image's own data characteristics, providing a technical basis for the standardized enhancement of massive images. This technical method effectively improves the vegetation chromaticity and layer characteristics on the original true color synthetic image, and greatly improves the overall effect of the true color image. The enhanced image has bright colors, rich information, and is easy to visually and automatically classify. Especially in the current context of the rapid development of high-resolution satellite remote sensing, it has a huge role in promoting the promotion and application of domestic high-resolution images in various industries at home and abroad.

[0177] Example 2

[0178] The present embodiment discloses an image vegetation feature adaptive enhancement system, including a memory and a processor, wherein the memory includes an image vegetation feature adaptive enhancement method program, and when the image vegetation feature adaptive enhancement method program is executed by the processor, the steps of an image vegetation feature adaptive enhancement method as described in Example 1 are implemented.

[0179] Example 3

[0180] This embodiment discloses a computer-readable storage medium, which includes an image vegetation feature adaptive enhancement method program. When the image vegetation feature adaptive enhancement method program is executed by a processor, the steps of an image vegetation feature adaptive enhancement method as described in Example 1 are implemented.

[0181] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0182] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0183] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0184] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0185] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A method for adaptively enhancing vegetation features in an image, characterized in that: The method comprises: Input multispectral remote sensing images with near infrared band, red band, green band and blue band; Calculate the characteristic ratio index of remote sensing images; According to the characteristic ratio index of the remote sensing image, the characteristic threshold and characteristic transformation function of the characteristic ratio index are determined; According to the characteristic ratio index and its characteristic threshold, a smooth constraint function space is constructed; According to the smooth constraint function, a symmetric curve of the characteristic ratio exponential transformation function based on three-point constraints is constructed; According to the symmetric curve of the characteristic ratio exponential transformation function based on three-point constraints, the vegetation feature enhancement coefficient function and the fusion expression of vegetation feature enhancement are constructed; The true color image after vegetation enhancement is synthesized according to the vegetation feature enhancement coefficient function and the fusion expression of vegetation feature enhancement.

2. The method for adaptively enhancing vegetation features in an image according to claim 1, characterized in that: The calculating of the characteristic ratio index of the remote sensing image specifically includes: Assume b i is a band of remote sensing image, the remote sensing image bands involved in fusion are near infrared band and m true color bands; the number of bands of the image result after fusion is m, and the fusion result is recorded as b' i ; The characteristic ratio index is: Where n=1,…,m.

3. The method for adaptively enhancing vegetation features in an image according to claim 1 or 2, characterized in that: The characteristic threshold value of the characteristic ratio index is determined by: Calculate the minimum value x of the characteristic ratio index x min , maximum value x max and the mean value x m ; Determine the threshold x of pure water through human-computer interaction w , the threshold value x of pure bare ground b , the threshold value of pure vegetation x v ; then the feature threshold c∈[x w , x v ]; By default, c = x m .

4. The method for adaptively enhancing vegetation features in an image according to claim 3, characterized in that: The feature transformation function expression is: Where n>0, sign is the sign operator, and abs is the absolute value operator; s and t are transformation parameters; when s = 1, t = 0, h(x) is the characteristic exponential power transformation; when s = 1, t = 1-c, h(x) is the characteristic exponential translation power transformation; when s = c, t = 0, h(x) is the characteristic exponential scaling power transformation; if c = 1, the three transformations are equivalent, all of which are h(x) = x n .

5. The method for adaptively enhancing vegetation features in an image according to claim 4, characterized in that: The smooth constraint function space is constructed according to the characteristic ratio index and its characteristic threshold, specifically: Suppose the smooth constraint function u(x) of the characteristic ratio index x is at the maximum value x max The value of is 1, that is: The smooth constraint function v(x) of the normalized exponential corresponding to the characteristic ratio index is at the maximum value x max The value of is also 1, that is: g(x)=C s v(x) l(x)=C s u(x) Among them, g(x) is the point passing through (c,0), (x max ,C s ) is the incremental function of vegetation feature enhancement, l(x) is the point passing through (c,0), (x max ,C s ) of the straight line, C s >0 is a constant, which is the maximum value of the characteristic ratio index x max The value of the normalized exponential smoothing constraint function v(x) at .

6. The method for adaptively enhancing vegetation features in an image according to claim 5, characterized in that: The step of constructing a symmetric curve of a characteristic ratio exponential transformation function based on a three-point constraint according to a smooth constraint function specifically includes: According to the smooth constraint function, the three points of the three-point constraint are determined as (x min ,0), (c,0) and (x max ,C s ); The normalized exponential smoothing constraint function and symmetry axis of the characteristic ratio exponential transformation function are: g(h(x))=C s v(h(x)) l(h(x))=kA(h(x)) in, A(h(x))=h(x)-h(c Let f(h(x)) be the symmetric curve of the normalized exponential smoothing constraint function g(h(x)) about the straight line l(h(x)), and its expression is: in, Let f'(h(x)) = 0, we can get f(h(x)) has a minimum value at x0, and x0∈(x min ,c), at this time: Let f(h(x0)) / C s =ε, the power n of the transformation function can be automatically determined according to the image data characteristics, thereby automatically determining the slope k of the symmetric curve. At this time, affected by (x min ,0), (c,0) and (x max ,C s ) and the three-point constrained symmetric curve f(h(x)) is the desired vegetation feature enhancement increment function.

7. The method for adaptively enhancing vegetation features in an image according to claim 6, characterized in that: The construction of the vegetation feature enhancement coefficient function and the fusion expression of vegetation feature enhancement specifically includes: The increment function f(h(x)) is normalized, and the vegetation feature enhancement coefficient function F(x) is constructed according to the superposition principle: F(x)=m·f(h(x)) / C s +1 m is the greenness adjustment coefficient, which has the function of adjusting the greenness of the image. m>0 is a constant, and the default value is m=1; Assuming that the red, green, and blue bands of the remote sensing image are R, G, and B respectively, the red and green bands are enhanced using the above enhancement coefficients, and the image fusion expression for vegetation feature enhancement is as follows: R′=F(x)×R G′=F(x)×G Among them, R' is the red band after the vegetation characteristics are enhanced, and G' is the green band after the vegetation characteristics are enhanced.

8. The method for adaptively enhancing vegetation features in an image according to claim 7, characterized in that: The method of synthesizing the vegetation enhanced true color image according to the vegetation feature enhancement coefficient function and the fusion expression of vegetation feature enhancement is specifically as follows: The red band R' after vegetation feature enhancement, the green band G' after vegetation feature enhancement and the blue band B are placed correspondingly in the red, green and blue channels to synthesize the color image, which is the true color image vegetation feature enhanced image.

9. An image vegetation feature adaptive enhancement system, characterized in that: The method comprises a memory and a processor, wherein the memory comprises a method program for adaptively enhancing image vegetation features, and when the method program for adaptively enhancing image vegetation features is executed by the processor, the steps of a method for adaptively enhancing image vegetation features as claimed in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes an image vegetation feature adaptive enhancement method program, and when the image vegetation feature adaptive enhancement method program is executed by a processor, the steps of an image vegetation feature adaptive enhancement method as described in any one of claims 1 to 8 are implemented.

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