Remote sensing image fusion method, system, equipment and medium based on product function spatial characteristic curve

Abstract

The present invention discloses a remote sensing image fusion method, system, device and medium based on a product function space characteristic curve. The method comprises: calculating a characteristic ratio index according to a satellite remote sensing image with a near infrared band, a red band, a green band and a blue band; constructing a smooth constraint function f(x) and an incremental coefficient function Δ(x) in a product function space according to the characteristic ratio index and its characteristic threshold; and obtaining a remote sensing image fusion method, system, device and medium based on a product function space characteristic curve. The remote sensing image fusion method comprises: calculating a characteristic ratio index according to a satellite remote sensing image with a near infrared band, a red band, a green band and a blue band; constructing a smooth constraint function f(x) and an incremental coefficient function Δ(x) in a product function space according to the characteristic ratio index and its characteristic threshold; and obtaining a remote sensing image fusion method, system, device and medium based on a product function space characteristic curve. min )=0 and Δ(c)=ε, determine the relaxation constraint value and characteristic power to be determined in the product function space; determine the incremental coefficient characteristic curve in the product function space according to the determined relaxation constraint value and characteristic power, and then obtain the enhancement coefficient characteristic curve and enhance the red band and the green band respectively; synthesize the enhanced red band, the enhanced green band and the original blue band. The present invention improves the standardization degree of the enhancement processing process and the consistency of the processing results.

Description

Technical Field

[0001] The present invention belongs to the technical field of remote sensing image fusion, and in particular relates to a remote sensing image fusion method, system, computer equipment and computer-readable storage medium based on a product function space characteristic curve. Background Art

[0002] With the development of multi-platform, multi-sensor, multi-weather, multi-temporal, and multi-resolution remote sensing technology, images with different spatial resolutions, temporal resolutions, and spectral resolutions are becoming increasingly abundant. In the past two decades, spatiotemporal fusion of remote sensing images 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 fusion of multispectral images, which are mainly concentrated in true color image simulation or vegetation enhancement processing.

[0003] With the popularization of remote sensing applications in all walks of life, visible light 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, there are inherent defects such as unnatural and unreal vegetation colors, which restrict its application effectiveness. How to effectively enhance the vegetation characteristics of visible light satellite remote sensing true color images is the key and difficulty of visible light satellite remote sensing true color image processing. Relevant researchers have made fruitful explorations for this, laying a theoretical and technical foundation for further solving related problems. For example: Chen Chun et al., based on satellite remote sensing primary products, corrected the remote sensing data for Rayleigh scattering to make 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, Vol. 31, No. 1, January 2006; Han Xiuzhen et al., Research and Application of Fengyun-3D True Color Image Synthesis Method, Journal of Marine Meteorology, Vol. 39, No. 2, May 2019); You Jing et al. The vegetation features of true color images were improved by using white balance method and color correction based on colorimetry, and more realistic true color images were obtained (You Jing et al., A white balance method for processing color multispectral images, Journal of Atmospheric and Environmental Optics, Vol. 7, No. 4, July 2012; Huang Honglian et al., True color synthesis of multispectral remote sensing images based on artificial targets, Infrared and Laser Engineering, Vol. 45, No. 11, November 2016); Fan Xuyan et al. obtained relatively good true color images by enhancing the green band based on the products after secondary processing of remote sensing images. In the early days, the green band and the near-infrared band were mainly used to obtain the new green band by the overall weighted combination operation scheme (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 GF-2 satellite remote sensing image mapping, Bulletin of Surveying and Mapping, No. 12, 2017); later, it was gradually developed. The development is to use the normalized vegetation index as the 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 most 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).

[0004] In view of the shortcomings of satellite remote sensing true color images, such as vegetation and other objects being dim and unnatural in color, a multi-index, multi-transformation, multi-mode, and multi-parameter natural color image vegetation feature enhancement method has been developed, which effectively improves the quality of true color images and improves the visual resolution and computer resolution of true color images. The rich enhancement methods provide rich choices for different image processing technicians to carry out personalized processing of specific images. However, in large-scale true color remote sensing image enhancement applications, the processing results are often required to have high consistency to facilitate the mapping and classification applications of image processing results. Obviously, there is a huge contradiction between personalized processing and standardization and consistency requirements. Summary of the invention

[0005] In order to resolve the contradiction between the personalized processing of a single image and the standardization and consistency processing requirements of large-scale images in true color image enhancement, the present invention provides a remote sensing image fusion method, system, computer equipment and computer-readable storage medium based on the characteristic curve of the product function space. By satisfying two given conditions, the feature enhancement parameters (relaxed constraint value and feature power) are automatically solved, and a characteristic curve of the product function space of true color image enhancement is determined, which reduces the human dependence on the selection of feature enhancement parameters and improves the standardization degree of the enhancement processing process and the consistency of the processing results.

[0006] The first object of the present invention is to provide a remote sensing image fusion method based on a product function space characteristic curve.

[0007] The second object of the present invention is to provide a remote sensing image fusion system based on the product function space characteristic curve.

[0008] A third object of the present invention is to provide a computer device.

[0009] A fourth object of the present invention is to provide a computer-readable storage medium.

[0010] The first object of the present invention can be achieved by adopting the following technical solutions:

[0011] A remote sensing image fusion method based on a product function spatial characteristic curve, the method comprising:

[0012] Obtain satellite remote sensing images with near-infrared band, red band, green band and blue band;

[0013] Calculate the characteristic ratio index according to the near infrared band, red band, green band and blue band;

[0014] According to the characteristic ratio index and its characteristic threshold, a smooth constraint function f(x) and an incremental coefficient function Δ(x) in a product function space are constructed;

[0015] By f′(xmin )=0 and Δ(c)=ε, determine the relaxation constraint value and characteristic power to be determined in the product function space; where x min is the minimum value of the characteristic ratio index x, c is the characteristic threshold of the characteristic ratio index x, ε>0 is much smaller than 1;

[0016] Determine the incremental coefficient characteristic curve in the product function space according to the determined relaxation constraint value and characteristic power;

[0017] According to the incremental coefficient characteristic curve, an enhancement coefficient characteristic curve is obtained;

[0018] The red band and the green band are enhanced respectively by using the enhancement coefficient characteristic curve; the enhanced red band, the enhanced green band and the original blue band are synthesized to obtain a true color image vegetation feature enhanced image;

[0019] The smooth constraint function f(x) is:

[0020]

[0021] in:

[0022]

[0023] In the formula, c x and n are the relaxation constraint value and characteristic power to be determined in the product function space, respectively, n>0; h(x) is the transformation function, sign is the sign operator, abs is the absolute value operator; s and t are the transformation parameters;

[0024] The incremental coefficient function Δ(x) is:

[0025]

[0026] Furthermore, when s = 1, t = 0, h(x) is the characteristic ratio exponential power transformation;

[0027] When s = 1, t = 1-c, h(x) is the characteristic ratio exponential shift power transformation;

[0028] When s=v, t=0, h(x) is the characteristic ratio exponential scaling power transformation.

[0029] Furthermore, if c=1, the three transformations of the characteristic ratio exponential power transformation, the characteristic ratio exponential translation power transformation and the characteristic ratio exponential scaling power transformation are all equivalent to h(x)=x n .

[0030] Furthermore, the process of determining the relaxation constraint value and characteristic power to be determined in the product function space is as follows:

[0031] Depend on:

[0032]

[0033] We can get:

[0034]

[0035] In general,

[0036] By f′(x min )=0, we get:

[0037]

[0038] From Δ(c)=ε, we can get:

[0039]

[0040] Right now:

[0041]

[0042] is a function of characteristic power n, let:

[0043]

[0044]

[0045] Use an iterative method to find a numerical solution to the characteristic power:

[0046]

[0047] n f =n k+1

[0048] n f That is, the characteristic power is determined, n k+1 is the characteristic power of k+1 iterations, n k is the characteristic power of k iterations.

[0049] Furthermore, the enhancement coefficient characteristic curve is:

[0050] F f (x) = kΔ f (x)+1

[0051] Where k>0 is a constant, called the greenness adjustment coefficient; Δ f (x) is the incremental coefficient characteristic curve.

[0052] Furthermore, the characteristic threshold c∈[x min , xmax ], x max is the maximum value of the characteristic ratio index x; or the threshold value x of pure water is determined through human-computer interaction w and the threshold x for pure vegetation v , then the characteristic threshold c∈[x w , x v ]; by default, c=x m , x m is the average value of x.

[0053] Furthermore, the characteristic ratio index is:

[0054]

[0055] Where b i is any band in the true color band, b i ′ is the result of spectral fusion between the near-infrared band and one or more true color bands; n=1,...,m, m is the number of true color bands involved in the fusion, m=1,2,3.

[0056] Furthermore, for quasi-Brovey fusion, the fusion result b i 'for:

[0057]

[0058] Where b j is the true color band involved in fusion;

[0059] For Gram-Schmidt, PCA or Wavelet fusion, the fusion result b′ i for:

[0060] b′ i =Fusion(NIR,b1,…,b m )

[0061] Where b1,...,b m It is the true color band involved in fusion. Fusion is Gram-Schmidt, PCA or Wavelet fusion method.

[0062] The second object of the present invention can be achieved by adopting the following technical solutions:

[0063] A remote sensing image fusion system based on a product function spatial characteristic curve, the system comprising:

[0064] An acquisition module is used to acquire satellite remote sensing images with near infrared band, red band, green band and blue band;

[0065] A calculation module, used for calculating a characteristic ratio index according to a near infrared band, a red band, a green band and a blue band;

[0066] A construction module, used to construct a smoothing constraint function f(x) and an incremental coefficient function Δ(x) in a product function space according to the characteristic ratio index and its characteristic threshold;

[0067] The first determination module is used to determine f′(x min )=0 and Δ(c)=ε, determine the relaxation constraint value and characteristic power to be determined in the product function space; where x min is the minimum value of the characteristic ratio index x, c is the characteristic threshold of the characteristic ratio index x, ε>0 is much smaller than 1;

[0068] A second determination module is used to determine an incremental coefficient characteristic curve in the product function space according to the determined relaxation constraint value and characteristic power;

[0069] An enhancement and synthesis module is used to obtain an enhancement coefficient characteristic curve according to the incremental coefficient characteristic curve; enhance the red band and the green band respectively by using the enhancement coefficient characteristic curve; synthesize the enhanced red band, the enhanced green band and the original blue band to obtain a true color image vegetation feature enhanced image;

[0070] The smooth constraint function f(x) is:

[0071]

[0072] in:

[0073]

[0074] In the formula, c x and n are the relaxation constraint value and characteristic power to be determined in the product function space, respectively, n>0; h(x) is the transformation function, sign is the sign operator, abs is the absolute value operator; s and t are the transformation parameters;

[0075] The incremental coefficient function Δ(x) is:

[0076]

[0077] The third object of the present invention can be achieved by adopting the following technical solutions:

[0078] A computer device comprises a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the remote sensing image fusion method based on the product function space characteristic curve is implemented.

[0079] The fourth object of the present invention can be achieved by adopting the following technical solutions:

[0080] A computer-readable storage medium stores a program, and when the program is executed by a processor, the remote sensing image fusion method based on the product function space characteristic curve is realized.

[0081] The present invention has the following beneficial effects compared with the prior art:

[0082] 1. The present invention effectively enhances the green vegetation features of true color images and improves the visual separability and computer parsing capabilities of vegetation. By enhancing the vegetation pixels of true color combined images point by point, the color, texture and layering of vegetation are comprehensively improved, effectively improving the visual resolution and computer parsing capabilities of true color image vegetation information, and improving the vegetation analysis capabilities and effects of true color image mode;

[0083] 2. The present invention greatly improves the overall characteristics of true color images and expands their application scope and potential. While enhancing the vegetation characteristics on true color images, the present invention maintains the characteristics of exposed objects such as water bodies, soil, rocks, and buildings on true color images, greatly improving the overall visual characteristics and effects of true color images. At the same time, the correlation between the bands of true color images is reduced, and the image color, texture, and layers are richer. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0085] Figure 1 A simplified flow chart of a remote sensing image fusion method based on a product function spatial characteristic curve according to Embodiment 1 of the present invention;

[0086] Figure 2 Detailed flow chart of the remote sensing image fusion method based on the product function spatial characteristic curve of embodiment 1 of the present invention;

[0087] Figure 3 The true color combined color image before vegetation feature enhancement according to Example 2 of the present invention;

[0088] Figure 4 This is a characteristic ratio index diagram of Example 2 of the present invention;

[0089] Figure 5 This is a true color image after synthesis enhancement according to Example 2 of the present invention;

[0090] Figure 6It is a structural block diagram of a remote sensing image fusion system based on a product function space characteristic curve according to Embodiment 3 of the present invention;

[0091] Figure 7 This is a structural block diagram of a computer device according to Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0092] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should be understood that the specific embodiments described are only used to explain the present application and are not used to limit the present application.

[0093] Embodiment 1:

[0094] The evaluation technology of vegetation enhancement effect of true color image includes visual evaluation and quantitative evaluation:

[0095] (1) Visual evaluation. As is known to all, although the original remote sensing true color images are consistent with the ground objects in the water bodies, exposed land and other landforms, the colors are dim and the layers are unclear in the vegetation areas. Generally, true color images need to be enhanced to obtain true color images that are consistent with the ground colors in the above-mentioned landforms. That is, the vegetation is based on green, and vegetation of different types and coverage presents various shades of green; the water body is based on blue, except for the green, yellow, black, etc. due to different components such as vegetation coverage on the water surface, high concentration of sand, and high pollution, the main body is various shades of blue; other exposed land such as rocks, exposed soil, roads, and residential areas are consistent with the rich colors of the ground, presenting gray, black, white, red, orange, yellow, green, cyan, blue, purple and other colors. By visually selecting typical land features such as water areas, exposed land, and vegetation, and qualitatively comparing the color changes between the original true color image and the enhanced true color image, the effect of vegetation enhancement in the true color image can be visually evaluated.

[0096] (2) Quantitative evaluation. Quantitative evaluation is, to a certain extent, the quantification of visual evaluation indicators. For the enhancement of vegetation features in true color images, it is necessary to improve the color of vegetation in true color images and ensure the richness and clarity of the layers, details, etc. of the enhanced true color images. In general, the reconstructed images can be quantitatively evaluated from two aspects: the first is the quantitative description and comparison of the vegetation color enhancement effect. Among the models that describe color space, such as RGB, CMYK, IHS, and CIE Lab, it is generally believed that the RGB three-primary color model is suitable for screen display such as computers, and the CMYK and other printing models are suitable for color image printouts, while the color description of color space models such as IHS and CIE Lab conforms to the human visual perception mode. Based on this understanding, the method generally used in the quantitative evaluation of the effect of true color images is: converting the remote sensing image described in the RGB three-primary color space into an image described in the IHS or CIE Lab color space, reading the hue, saturation, intensity, etc. of vegetation objects in these color spaces before and after enhancement, and analyzing their changing trends and characteristics. Second, statistics and comparison of the quality indicators of the enhanced true color images. Generally speaking, the quality of image processing can be evaluated from three aspects: first, the information richness of the overall image and vegetation area after enhancement, which can be measured by entropy and joint entropy; second, the color richness and brightness of the overall image and vegetation area after enhancement, which can be measured by band statistical characteristics - maximum value, minimum value, mean, variance and correlation indicators between bands - correlation coefficient, covariance, etc.; third, the level (edge), details (texture) and clarity of the overall image and vegetation area after enhancement, which can be measured by gradient, average gradient, etc. By comparing the differences in various indicators of the overall image and vegetation area before and after enhancement, the change direction of spectral (grayscale, hue) information, edge (level, difference) information, and texture (detail) information can be analyzed.

[0097] The remote sensing image fusion method based on the characteristic curve of the product function space provided in this embodiment mainly includes: in the vegetation feature enhancement of the true color image, the product of the smooth constrained basis function of the characteristic ratio index and its normalized index constitutes the product function space of the vegetation feature enhancement. Assume that the first-order derivative of a curve in the product function space at the minimum value of the characteristic ratio index is zero, so that it is an increasing function within the statistical value range of the characteristic ratio index; at the same time, the value of the curve at the characteristic threshold is greater than zero and much less than 1, so that the value less than the threshold is small enough; the characteristic parameters of the curve are determined accordingly. According to the principle of superposition enhancement, the red and green bands of the true color image are enhanced based on the characteristic curve, and the enhanced true color image is synthesized together with the original blue band.

[0098] like Figure 1 As shown, the remote sensing image fusion method based on the product function space characteristic curve provided in this embodiment includes the following steps:

[0099] S101. Input satellite remote sensing image data.

[0100] Input satellite remote sensing images with near infrared NIR, red R, green G, and blue B bands.

[0101] S102. Calculate the characteristic ratio index.

[0102] Assume b i is one of the true color bands, and the bands involved in the fusion are NIR and m true color bands. The number of band results after fusion is m, and the fusion result is recorded as b′ i .

[0103] (1) For quasi-Brovey fusion, the fusion result is recorded as:

[0104]

[0105] Where m = 1, 2, 3, i = 1, ..., n, b i 、b j It is the true color band involved in fusion.

[0106] (2) For Schmidt-Gram, PCA fusion or Wavelet fusion, the fusion result is recorded as:

[0107] b′ i =Fusion(NIR,b1,…,b m )

[0108] where m = 1, 2, 3, i = 1, ..., m, b1, ..., b m It is the true color band involved in fusion. Fusion is GS (Gram-Schmidt), PCA fusion or WL (Wavelet) fusion method.

[0109] The characteristic ratio index is:

[0110]

[0111] Where n=1,…,m.

[0112] S103, determining a characteristic threshold of a characteristic ratio index and constructing a product function space for vegetation feature enhancement.

[0113] (1) Determine the characteristic threshold of the characteristic ratio index.

[0114] Calculate the minimum, maximum and average values ​​of the characteristic ratio index x, denoted by x min 、x max 、x m , feature threshold c∈[x min , xmax ]; or human-computer interaction to determine the threshold x of pure water 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 .

[0115] (2) Construct the transformation function of the characteristic ratio index.

[0116] At the feature threshold c, the feature ratio index x is subjected to power transformation, translation power transformation, and scaling power transformation to obtain the transformation function h(x), which is uniformly recorded as:

[0117]

[0118] Where n>0, sign is the sign operator, abs is the absolute value operator; s and t are transformation parameters. When s=1, t=0, h(x) is the characteristic ratio exponential power transformation; when s=1, t=1-c, h(x) is the characteristic ratio exponential translation power transformation; when s=c, t=0, h(x) is the characteristic ratio exponential scaling power transformation. If c=1, the three transformations are equivalent, all of which are h(x)=x n .

[0119] h(x) has the same monotonic characteristics as the original feature ratio index x - increasing along the axis from water to exposed objects such as rock and soil to vegetation objects, and has an equivalent classification function - the left and right sides of the threshold c divide the objects into two categories with the same number of pixels and the same features. Using h(x) as the basic parameter for vegetation feature enhancement can effectively expand the vegetation enhancement capability of the feature ratio index.

[0120] (3) Based on the transformation function, a smooth constrained product function space for vegetation feature enhancement is constructed.

[0121] The basic requirement for vegetation feature enhancement is to enhance the vegetation feature pixels with the function of h(x), while keeping the non-vegetation features unchanged, and the enhanced image remains smooth on both sides of the threshold - the increment is zero at the threshold c. The two basis functions that meet the smoothness constraint - the ratio exponential constraint function and the normalized exponential constraint function are as follows:

[0122] u(x)=h(x)-h(c)

[0123]

[0124] Let c x ∈[0,c], the following function also satisfies the smoothness constraint:

[0125]

[0126] f(x) is the smooth constraint function of the product of the transformation exponent and its normalized exponent, and is the relaxation constraint value c x , the space of smooth constrained product functions with ratio exponent power n as parameter.

[0127] Let Δ(x) be the incremental coefficient function of vegetation feature enhancement constructed by f(x), satisfying that the incremental coefficient is 0 at the statistical minimum value of x and the incremental coefficient is 1 at the statistical maximum value of x. Then a concise expression of the incremental coefficient function is:

[0128]

[0129] According to the superposition principle, the enhancement coefficient function is obtained from the increment function Δ(x):

[0130] F(x)=k·Δ(x)+1

[0131] k>0 is a constant, which has the function of adjusting the greenness of the image and can be called the greenness adjustment coefficient.

[0132] The smoothing constraint function f(x), the incremental coefficient function Δ(x), and the enhancement coefficient function F(x) are all product function spaces constructed by the basis functions u(x) and v(x). Each curve in the function space is composed of the relaxed constraint value c x And the ratio exponential transformation power n is determined.

[0133] S104, determine a smooth constraint product function space characteristic curve (function).

[0134] Assume that the statistical maximum and minimum values ​​of the characteristic ratio index are x max 、x min , let f′(x min )=0 so that f(x) is min ,x max ] is an increasing function on the interval. Let ε>0 be much smaller than 1, Δ(c)=ε, so that x∈[x min ,c], the incremental coefficient Δ(x) function value is small enough. According to the above two conditions, a characteristic curve on the Δ(x) function space can be determined, that is, the relaxation constraint value c corresponding to the curve can be determined. x And the ratio exponential transformation power n. The details are as follows:

[0135] (1) The spatial relaxation constraint value c of the product function x or h(c x ) determination.

[0136] Depend on

[0137]

[0138] We can get:

[0139]

[0140] In general:

[0141]

[0142] It is known that f′(x min )=0, we get:

[0143]

[0144] It can be seen that h(c x ) is only a function of n.

[0145] (2) Determination of the characteristic power n of the ratio exponential of the product function space.

[0146] From Δ(c)=ε, we can get:

[0147]

[0148] Right now:

[0149]

[0150] is the function of the power n of the characteristic ratio exponential transformation function, let:

[0151]

[0152] The numerical solution of the characteristic power can be obtained by iterative method:

[0153]

[0154] n f =n k+1

[0155] (3) Determine the characteristic curve (function) of the product function space.

[0156] From n = n f , it can be obtained that the ratio exponential characteristic transformation curve (function) of the product function space is:

[0157]

[0158] Accordingly, the curves (functions) in the product function space for vegetation enhancement are as follows:

[0159] The smooth constraint characteristic curve (function) is:

[0160]

[0161] The incremental coefficient characteristic curve (function) is:

[0162]

[0163] The enhancement coefficient characteristic curve (function) is:

[0164] F f (x) = k·Δ f (x)+1

[0165] k>0 is a constant, which is the greenness adjustment coefficient.

[0166] in:

[0167]

[0168] S105. Enhance the vegetation features in the red and green bands of true color images.

[0169] The enhancement plan is as follows:

[0170] R′=F f (x) × R = [kΔ f (x)+1]×R

[0171] G′=F f (x)×G=[kΔ f (x)+1]×G

[0172] The default value of k is 1.

[0173] S106: synthesize and store the vegetation-enhanced true color image.

[0174] The color image is synthesized using the three bands R′, G′, and B corresponding to the red, green, and blue channels of the color image.

[0175] The remote sensing image fusion method based on the characteristic curve of the product function space provided in this embodiment generally provides an enhancement algorithm based on the intrinsic characteristics of satellite image data, and has strong data adaptability; the characteristic ratio index, characteristic threshold, characteristic index feature transformation, characteristic curve of the smooth constraint product function space, incremental coefficient function, greenness adjustment coefficient, and enhancement coefficient function have clear physical meanings, and the parameter determination of feature enhancement reduces human dependence, the processing target is clear, the quality is reliable, and the application is simple; it has the following outstanding advantages:

[0176] (1) The given increment coefficient function is a continuous function, which is small enough when it is less than the threshold and large enough when it is greater than the threshold, and has the function of enhancing vegetation features and maintaining non-vegetation features.

[0177] The incremental coefficient function is a continuous function containing a classification threshold, and has excellent characteristics: it is 0 at the minimum value of the feature ratio index, the incremental coefficient is a small value within a neighborhood of 0 at the threshold, and the incremental coefficient is 1 at the maximum value, which ensures that the enhanced image is smooth at the threshold and maintains non-vegetation features while enhancing vegetation features.

[0178] (2) The characteristic power of the transformation index effectively improves the chromaticity and level differences of vegetation, and the greenness adjustment coefficient makes the enhancement effect adjustable and controllable.

[0179] The characteristic power of the transformation index is generally greater than 1, which has the function of enhancing the hierarchical differences of different vegetation and effectively highlights the differences between different vegetation. When the greenness adjustment coefficient changes from small to large, the tones of the vegetation in the enhanced image change from dark green to yellow-green, and the adjustment direction and enhancement effect are predictable and controllable.

[0180] Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be completed by instructing related hardware through a program, and the corresponding program may be stored in a computer-readable storage medium.

[0181] It should be noted that although the method operations of the above embodiments are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted can be performed in a different order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0182] Embodiment 2:

[0183] 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).

[0184] (1) Input remote sensing image.

[0185] Open a multispectral remote sensing image with near infrared band (NIR), red band (R), green band (G), and blue band (B). Figure 3 This is a true color composite color image before vegetation feature enhancement (the effect of 0.5% stretching according to the default setting of envi).

[0186] (2) Calculate the characteristic ratio index and determine its threshold.

[0187] Take the characteristic ratio index obtained by fusion of Wavelet transform method as an example.

[0188]

[0189] WLR, WLG and WLB are the results of fusion of NIR with R, G and B according to Wavelet transform method.

[0190] 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 Wavelet method fusion, b2 is the green band obtained by Wavelet method fusion, b3 is the blue band obtained by Wavelet 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 4 . (The effect of 0.5% stretching according to the default setting of envi).

[0191] Apply ENVI statistical tools to find the mean value x m , and use it as the feature threshold c, that is, c = x m =0.43266. The maximum value of x max =1.323029, the minimum value of x min =0.252563.

[0192] (3) Calculate characteristic curves and fusion results.

[0193] Taking the characteristic ratio exponential scaling transformation as an example, the characteristic power and characteristic curve are calculated as follows:

[0194] at this time:

[0195] h(x)=(x / 0.43266) n

[0196]

[0197] Let ε = 0.01 and use Newton's iteration method to solve the equation:

[0198]

[0199] have to:

[0200] n f =3.177812038

[0201] Then the feature transformation function is:

[0202] h f (x) = (x / 0.43266) 3.177812038

[0203] h f (c) = 1

[0204] h f (x max )=34.88043067

[0205] h f (x min )=0.180759

[0206] h f (c x )=-0.302903774

[0207] The characteristic curve is:

[0208]

[0209] f f (x min )=-0.335578

[0210] f f (x max )=33.222191

[0211] f f (x max )-f(x min )=33.557769

[0212] The increment factor is:

[0213]

[0214] Take k = 1, the enhancement coefficient is:

[0215] F f (x) = kΔ f (x)+1=({[(x / 0.43266)^3.177812038+0.302903774]·((x / 0.43266)^3.1 77812038-1) / ((x / 0.43266)^3.177812038+1)}+0.335578) / 33.557769+1

[0216] R′=[F f (x)+1]×R

[0217] G′=[F f (x)+1]×G

[0218] According to the above formula, input the calculation expression: uint(((((b1 / 0.43266)^3.177812038+0.302903774)*((b1 / 0.43266)^3.177812038-1) / ((b1 / 0.43266)^3.177812038+1)+0.335578) / 33.557769+1)*b2+0.5), 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 according to the red, green and blue channels to form an enhanced true color image. Figure 5 (The result is a 0.5% stretch according to the default setting of envi.) The reconstructed image file is the result.

[0219] The statistical characteristics comparison and analysis table of the color image after vegetation feature enhancement in this case and the original true color image in RGB color mode are shown in Tables 1 and 2. The statistical characteristics comparison and analysis table of the whole image and enhanced area in HLS color mode is shown in Table 3.

[0220] Table 1 Comparative analysis of RGB mode statistical features of enhanced true color images and original true color images

[0221]

[0222] Table 2 Comparative analysis of vegetation regional statistical characteristics between enhanced true color images and original true color images in RGB mode

[0223]

[0224] Table 3 Comparative analysis of statistical characteristics of enhanced true color images and original true color images in HLS mode

[0225]

[0226] Embodiment 3:

[0227] like Figure 6 As shown, this embodiment provides a remote sensing image fusion system based on a product function space characteristic curve, the system includes an acquisition module 601, a calculation module 602, a construction module 603, a first determination module 604, a second determination module 605 and an enhancement and synthesis module 606, wherein:

[0228] The acquisition module 601 is used to acquire satellite remote sensing images with near infrared band, red band, green band and blue band;

[0229] A calculation module 602 is used to calculate a characteristic ratio index according to a near infrared band, a red band, a green band, and a blue band;

[0230] A construction module 603 is used to construct a smoothing constraint function f(x) and an incremental coefficient function Δ(x) in a product function space according to the characteristic ratio index and its characteristic threshold;

[0231] The first determination module 604 is used to determine f′(x min )=0 and Δ(c)=ε, determine the relaxation constraint value and characteristic power to be determined in the product function space; where x min is the minimum value of the characteristic ratio index x, c is the characteristic threshold of the characteristic ratio index x, ε>0 is much smaller than 1;

[0232] A second determination module 605 is used to determine an incremental coefficient characteristic curve in the product function space according to the determined relaxation constraint value and characteristic power;

[0233] The enhancement and synthesis module 606 is used to obtain an enhancement coefficient characteristic curve according to the incremental coefficient characteristic curve; enhance the red band and the green band respectively by using the enhancement coefficient characteristic curve; synthesize the enhanced red band, the enhanced green band and the original blue band to obtain a true color image vegetation feature enhanced image;

[0234] The smooth constraint function f(x) is:

[0235]

[0236] in:

[0237]

[0238] In the formula, c x and n are the relaxation constraint value and characteristic power to be determined in the product function space, respectively, n>0; h(x) is the transformation function, sign is the sign operator, abs is the absolute value operator; s and t are the transformation parameters;

[0239] The incremental coefficient function Δ(x) is:

[0240]

[0241] The specific implementation of each module in this embodiment can refer to the above-mentioned embodiment 1, which will not be described one by one here; it should be noted that the system provided in this embodiment is only illustrated by the division of the above-mentioned functional modules. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0242] Embodiment 4:

[0243] This embodiment provides a computer device, which may be a computer, such as Figure 7 As shown, a processor 702, a memory, an input device 703, a display 704 and a network interface 705 connected via a system bus 701 are provided. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium 706 and an internal memory 707. The non-volatile storage medium 706 stores an operating system, a computer program and a database. The internal memory 707 provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. When the processor 702 executes the computer program stored in the memory, the remote sensing image fusion method based on the spatial characteristic curve of the product function of the above-mentioned embodiment 1 is implemented as follows:

[0244] Obtain satellite remote sensing images with near-infrared band, red band, green band and blue band;

[0245] Calculate the characteristic ratio index according to the near infrared band, red band, green band and blue band;

[0246] According to the characteristic ratio index and its characteristic threshold, a smooth constraint function f(x) and an incremental coefficient function Δ(x) in a product function space are constructed;

[0247] By f′(x min )=0 and Δ(c)=ε, determine the relaxation constraint value and characteristic power to be determined in the product function space; where x min is the minimum value of the characteristic ratio index x, c is the characteristic threshold of the characteristic ratio index x, ε>0 is much smaller than 1;

[0248] Determine the incremental coefficient characteristic curve in the product function space according to the determined relaxation constraint value and characteristic power;

[0249] According to the incremental coefficient characteristic curve, an enhancement coefficient characteristic curve is obtained;

[0250] The red band and the green band are enhanced respectively by using the enhancement coefficient characteristic curve; the enhanced red band, the enhanced green band and the original blue band are synthesized to obtain a true color image vegetation feature enhanced image;

[0251] The smooth constraint function f(x) is:

[0252]

[0253] in:

[0254]

[0255] In the formula, c xand n are the relaxation constraint value and characteristic power to be determined in the product function space, respectively, n>0; h(x) is the transformation function, sign is the sign operator, abs is the absolute value operator; s and t are the transformation parameters;

[0256] The incremental coefficient function Δ(x) is:

[0257]

[0258] Embodiment 5:

[0259] This embodiment provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the remote sensing image fusion method based on the product function space characteristic curve of the above embodiment 1 is implemented as follows:

[0260] Obtain satellite remote sensing images with near-infrared band, red band, green band and blue band;

[0261] Calculate the characteristic ratio index according to the near infrared band, red band, green band and blue band;

[0262] According to the characteristic ratio index and its characteristic threshold, a smooth constraint function f(x) and an incremental coefficient function Δ(x) in a product function space are constructed;

[0263] By f′(x min )=0 and Δ(c)=ε, determine the relaxation constraint value and characteristic power to be determined in the product function space; where x min is the minimum value of the characteristic ratio index x, c is the characteristic threshold of the characteristic ratio index x, ε>0 is much smaller than 1;

[0264] Determine the incremental coefficient characteristic curve in the product function space according to the determined relaxation constraint value and characteristic power;

[0265] According to the incremental coefficient characteristic curve, an enhancement coefficient characteristic curve is obtained;

[0266] The red band and the green band are enhanced respectively by using the enhancement coefficient characteristic curve; the enhanced red band, the enhanced green band and the original blue band are synthesized to obtain a true color image vegetation feature enhanced image;

[0267] The smooth constraint function f(x) is:

[0268]

[0269] in:

[0270]

[0271] In the formula, c xand n are the relaxation constraint value and characteristic power to be determined in the product function space, respectively, n>0; h(x) is the transformation function, sign is the sign operator, abs is the absolute value operator; s and t are the transformation parameters;

[0272] The incremental coefficient function Δ(x) is:

[0273]

[0274] It should be noted that the computer-readable storage medium of the present embodiment may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0275] In summary, the remote sensing image fusion method, system, computer device and computer-readable storage medium based on the product function space characteristic curve provided by the present invention, and the satellite remote sensing true color image vegetation feature enhancement technology described in the present invention are 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 the characteristic exponential smoothing constraint product function space characteristic curve is constructed, which 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 method has clear physical meaning and a wide range of applications. The enhanced image has bright colors, rich information, and is easy to visually and automatically classify. Especially in the current context of 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.

[0276] The above is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solution and inventive concept of the present invention within the scope disclosed by the present invention, which shall fall within the protection scope of the present invention.

Claims

1. A remote sensing image fusion method based on a product function spatial characteristic curve, characterized in that: The method comprises: Obtain satellite remote sensing images with near-infrared band, red band, green band and blue band; The near-infrared band is spectrally fused with one or more bands among the red band, the green band and the blue band, and the characteristic ratio index x is calculated by taking the fused band combination as the numerator and the corresponding band combination before fusion as the denominator; According to the characteristic ratio index and its characteristic threshold, a smooth constraint function f(x) and an incremental coefficient function Δ(x) in a product function space are constructed; By f′(x min )=0 and Δ(c)=ε, determine the relaxation constraint value and characteristic power to be determined in the product function space; where x min is the minimum value of the characteristic ratio index x, c is the characteristic threshold of the characteristic ratio index x, ε>0 is much smaller than 1; Determine the incremental coefficient characteristic curve in the product function space according to the determined relaxation constraint value and characteristic power; According to the incremental coefficient characteristic curve, an enhancement coefficient characteristic curve is obtained; The red band and the green band are enhanced respectively by using the enhancement coefficient characteristic curve; the enhanced red band, the enhanced green band and the original blue band are synthesized to obtain a true color image vegetation feature enhanced image; The smooth constraint function f(x) is: in: In the formula, c x and n are the relaxation constraint value and characteristic power to be determined in the product function space, respectively, n>0; sign is the sign operator, abs is the absolute value operator, s and t are transformation parameters; when s=1, t=0, h(x) is the characteristic ratio exponential power transformation; when s=1, t=1-c, h(x) is the characteristic ratio exponential translation power transformation; when s=c, t=0, h(x) is the characteristic ratio exponential scaling power transformation; The incremental coefficient function Δ(x) is: In the formula, x max is the maximum value of the characteristic ratio index x.

2. The remote sensing image fusion method according to claim 1, characterized in that: If c=1, the three transformations of the characteristic ratio exponential power transformation, characteristic ratio exponential translation power transformation and characteristic ratio exponential scaling power transformation are all equivalent to h(x)=x n .

3. The remote sensing image fusion method according to claim 1, characterized in that: The process of determining the relaxation constraint value and characteristic power to be determined in the product function space is as follows: Depend on: We can get: Known By f′(x min )=0, we get: From Δ(c)=ε, we can get: Right now: is a function of characteristic power n, let: Use an iterative method to find a numerical solution to the characteristic power: n f =n k+1 n f That is, the characteristic power is determined, n k+1 is the characteristic power of k+1 iterations, n k is the characteristic power of k iterations.

4. The remote sensing image fusion method according to any one of claims 1 to 3, characterized in that: The enhancement coefficient characteristic curve is: F f (x)=k·Δ f (x)+1 Where k>0 is a constant, called the greenness adjustment coefficient; Δ f (x) is the incremental coefficient characteristic curve.

5. The remote sensing image fusion method according to any one of claims 1 to 3, characterized in that: The characteristic threshold c∈[x min , x max ], or determine the threshold x of pure water through human-computer interaction w and the threshold x for pure vegetation v , then the characteristic threshold c∈[x w , x v ]; by default, c=x m , x m is the average value of x.

6. The remote sensing image fusion method according to any one of claims 1 to 3, characterized in that: For quasi-Brovey fusion, the fusion result b i 'for: Where b j is the true color band involved in fusion; NIR is the near infrared band; For Gram-Schmidt, PCA or Wavelet fusion, the fusion result b i 'for: b′ i =Fusion(NIR,b1,…,b m ) Where b1,...,b m It is the true color band involved in fusion. Fusion is Gram-Schmidt, PCA or Wavelet fusion method.

7. A remote sensing image fusion system based on a product function spatial characteristic curve, characterized in that: The system comprises: An acquisition module is used to acquire satellite remote sensing images with near infrared band, red band, green band and blue band; A calculation module is used to perform spectrum fusion between the near infrared band and one or more bands among the red band, the green band and the blue band, and calculate the characteristic ratio index x by taking the band combination after fusion as the numerator and the corresponding band combination before fusion as the denominator; A construction module, used to construct a smoothing constraint function f(x) and an incremental coefficient function Δ(x) in a product function space according to the characteristic ratio index and its characteristic threshold; The first determination module is used to determine f′(x min )=0 and Δ(c)=ε, determine the relaxation constraint value and characteristic power to be determined in the product function space; where x min is the minimum value of the characteristic ratio index x, c is the characteristic threshold of the characteristic ratio index x, ε>0 is much smaller than 1; A second determination module is used to determine an incremental coefficient characteristic curve in the product function space according to the determined relaxation constraint value and characteristic power; An enhancement and synthesis module is used to obtain an enhancement coefficient characteristic curve according to the incremental coefficient characteristic curve; enhance the red band and the green band respectively by using the enhancement coefficient characteristic curve; synthesize the enhanced red band, the enhanced green band and the original blue band to obtain a true color image vegetation feature enhanced image; The smooth constraint function f(x) is: in: In the formula, c x and n are the relaxation constraint value and characteristic power to be determined in the product function space, respectively, n>0; sign is the sign operator, abs is the absolute value operator, s and t are transformation parameters; when s=1, t=0, h(x) is the characteristic ratio exponential power transformation; when s=1, t=1-c, h(x) is the characteristic ratio exponential translation power transformation; when s=c, t=0, h(x) is the characteristic ratio exponential scaling power transformation; The incremental coefficient function Δ(x) is: In the formula, x max is the maximum value of the characteristic ratio index x.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the remote sensing image fusion method according to any one of claims 1 to 6 is implemented.

Images