A building height calculation method, device and equipment based on remote sensing images

Abstract

The application discloses a building height calculation method based on remote sensing images, comprising the following steps: acquiring multi-time sequence remote sensing image data containing a building to be measured, and dividing the multi-time sequence remote sensing image data to obtain a plurality of sub-time sequence remote sensing images; performing image stabilization processing and shadow feature enhancement processing on each sub-time sequence remote sensing image; extracting a shadow change footprint of the building to be measured under sunlight projection according to each processed sub-time sequence remote sensing image; and calculating the height of the building to be measured according to the solar elevation angle corresponding to the time point of each sub-time sequence remote sensing image and the shadow change footprint. The application also discloses corresponding calculation devices and equipment. The application is based on the shadow footprint change of a plurality of sub-time sequence remote sensing images to realize the measurement of the building height, improves the accuracy and convenience of the measurement of the building height, and is beneficial to providing a reference for the power resource allocation of a building area.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to a method, apparatus, and device for calculating building height based on remote sensing images. Background Technology

[0002] Buildings carry considerable flexible resource potential in the power grid, such as the power resource allocation for air conditioning and other electrical equipment. Therefore, calculating the volumetric capacity of urban buildings is of great significance. In addition to the specific ground building area, it is also necessary to extract the accurate building elevation.

[0003] Currently, multi-scale building height monitoring relies on regional research methods, including photogrammetric stereo positioning and 3D building modeling based on mobile lidar scanning point cloud data. However, the inventors have found that existing technologies suffer from at least the following problems: Existing measurement methods are limited by large regional data volumes, weak practicality over large areas, and interference from non-buildings such as vegetation and water bodies; and common machine learning methods applied to geospatial data face challenges in building shadow detection, in addition to interference from non-buildings in satellite images, due to spectral heterogeneity and the similarity of shadow and non-shadow area features; since the natural environment in a single remote sensing image is fixed, the attributes of solar altitude, latitude of the detection area, and building shadow projection shape are fixed in each hyperspectral image, resulting in a deviation between the estimated building height and the actual height only within a single time frame. Therefore, a precise and convenient method for measuring building height is urgently needed. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, and device for calculating building height based on remote sensing images. By measuring the shadow footprint changes of multiple sub-time series remote sensing images, the accuracy and convenience of building height measurement are improved, which is beneficial for providing a reference for the allocation of power resources in building areas.

[0005] To achieve the above objectives, embodiments of the present invention provide a method for calculating building height based on remote sensing images, comprising:

[0006] Acquire multi-temporal remote sensing image data containing the building to be measured, and divide the multi-temporal remote sensing image data into several sub-temporal remote sensing images;

[0007] Image stabilization and shadow feature enhancement are performed on each of the aforementioned sub-time series remote sensing images;

[0008] Based on each processed sub-time series remote sensing image, the footprint of shadow change of the building under the projection of sunlight is extracted;

[0009] The height of the building under test is calculated based on the solar altitude angle corresponding to the time point of each sub-time series remote sensing image and the shadow change footprint.

[0010] As an improvement to the above scheme, the step of dividing the multi-time-series remote sensing image data into several sub-time-series remote sensing images specifically includes:

[0011] Calculate time-series data of solar altitude related to latitude variations;

[0012] Based on the solar altitude time series data, the multi-time series remote sensing image data is divided into several sub-time series remote sensing images, so that the degree of shadow change of the building under test under the projection of sunlight in each sub-time series remote sensing image is the same or similar.

[0013] As an improvement to the above scheme, the image stabilization processing for each of the sub-time series remote sensing images includes:

[0014] A corresponding point matching algorithm is used to match feature points for each of the aforementioned sub-temporal remote sensing images;

[0015] The sub-time series remote sensing images are optimized and compensated using a preset rational function model;

[0016] The sub-time series remote sensing images are optimized using a regional network adjustment algorithm and corrected using a rotation and shearing algorithm to obtain stable sub-time series remote sensing images.

[0017] As an improvement to the above scheme, shadow feature enhancement processing is performed on each of the sub-time series remote sensing images, including:

[0018] The shadows displayed in the sub-time series remote sensing image are enhanced by using the local phase and amplitude response of a preset filter until the preset parameter difference remains essentially unchanged; wherein, the preset filter is designed based on the spectral and spatial characteristics of the satellite image;

[0019] The sub-time series remote sensing image is processed using a preset active optimization contour segmentation model to depict shadow edges and enhance shadow effects.

[0020] The initial curve of the depicted shadow edge is initialized with a level set to obtain the enhanced shadow edge;

[0021] A preset discrimination factor is used to filter out the water body region in the sub-time series remote sensing image to obtain a sub-time series remote sensing image after shadow feature enhancement processing; wherein, the discrimination factor refers to the factor used to compare the shadow region and the water body region in the sub-time series remote sensing image, and the discrimination factor is determined by the variance threshold of the spectral features of the remote sensing image.

[0022] As an improvement to the above scheme, the step of calculating the height of the building under test based on the solar altitude angle corresponding to the time point of each sub-time series remote sensing image and the shadow change footprint specifically includes:

[0023] Based on the solar elevation angle and the shadow change footprint corresponding to the time point of each sub-time series remote sensing image, the height data of the building to be measured corresponding to each sub-time series remote sensing image is calculated.

[0024] Based on the height data of the building to be measured corresponding to all the sub-time series remote sensing images, the optimal solution for the height of the building to be measured is obtained by using a probabilistic data statistics algorithm.

[0025] As an improvement to the above scheme, the step of calculating the height data of the building to be measured corresponding to each sub-time series remote sensing image based on the solar altitude angle and the shadow change footprint corresponding to the time point of each sub-time series remote sensing image specifically includes:

[0026] Based on the solar elevation angle and the sub-time series remote sensing image, determine the relationship between the sun, the satellite sensor that captured the time series remote sensing image, and the building under test;

[0027] When the sun and the satellite sensor are located on the same side of the building under test, the height data of the building under test corresponding to the sub-time series remote sensing image is:

[0028]

[0029]

[0030] When the azimuth angle between the sun and the satellite sensor is greater than 180°, the height data of the building to be measured corresponding to the sub-time series remote sensing image is:

[0031] AB = BD × tanα;

[0032] When the azimuth angle between the sun and the satellite sensor is between 0° and 180°, the height data of the building to be measured corresponding to the sub-time series remote sensing image is:

[0033]

[0034] Wherein, α is the solar altitude angle, β is the satellite sensor elevation angle, γ is the solar azimuth angle, δ is the satellite sensor azimuth angle, ε is the angle formed by the direction of the building under test and the clockwise projection of the shadow of the building under test; AB is the height data of the building under test, BC is the length of the shadow projected by the building under sunlight, BD is the total length of the shadow projected by the building under test, CD is the observable shadow length on the sub-time series remote sensing image; DE is the observable projected shadow length in the sub-time series remote sensing image.

[0035] As an improvement to the above scheme, after extracting the shadow change footprint of the building under sunlight projection, and before calculating the height of the building under test based on the solar altitude angle corresponding to the time point of each sub-time series remote sensing image and the shadow change footprint, the method further includes:

[0036] Determine whether the building under test is located in complex terrain or dense area; wherein, complex terrain refers to the fact that the elevation of the shadow projection surface of the building under test in the sub-time series remote sensing image is not equal to the elevation of the horizontal projection surface; and dense area refers to the fact that the shadow of the building under test in the sub-time series remote sensing image overlaps with the area occupied by adjacent buildings.

[0037] When the building under test is located in complex terrain, the two ends of the shaded line are used as buffer zones to obtain the elevation of the contour lines within the buffer zones. The elevation is then assigned to the end of the fishing net line to correct the shaded length.

[0038] When the building under test is located in a dense area, shadow regularization extraction is used to process the sub-time series remote sensing image to correct the shadow length.

[0039] As an improvement to the above solution, after calculating the height of the building to be measured, the method further includes:

[0040] The volume of the building under test is calculated based on its height and the area it occupies in the time-series remote sensing image.

[0041] Based on the volume of each building to be tested within the target area, calculate the required energy supply for the target area; wherein, the energy supply includes heating and cooling capacity;

[0042] Based on the energy supply required by the target area, assess the rationality of the scale of air conditioning configuration in the building space of the target area.

[0043] This invention also provides a building height calculation device based on remote sensing images, comprising:

[0044] The remote sensing image acquisition module is used to acquire multi-temporal remote sensing image data containing the building to be measured, and to divide the multi-temporal remote sensing image data into several sub-temporal remote sensing images.

[0045] The image processing module is used to perform image stabilization and shadow feature enhancement processing on each of the sub-time series remote sensing images;

[0046] The shadow change footprint extraction module is used to extract the shadow change footprint of the building under test under sunlight projection based on each of the processed sub-time series remote sensing images.

[0047] The building height calculation module is used to calculate the height of the building under test based on the solar altitude angle corresponding to the time point of each sub-time series remote sensing image and the shadow change footprint.

[0048] This invention also provides a building height calculation device based on remote sensing images, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the building height calculation method based on remote sensing images as described in any of the above embodiments.

[0049] Compared with existing technologies, the building height calculation method, apparatus, and device based on remote sensing images disclosed in this invention acquire multi-temporal remote sensing image data containing the building to be measured, and divide the multi-temporal remote sensing image data into several sub-temporal remote sensing images; perform image stabilization processing and shadow feature enhancement processing on each sub-temporal remote sensing image; extract the shadow change footprint of the building under sunlight projection based on each processed sub-temporal remote sensing image; and calculate the height of the building based on the solar altitude angle corresponding to the time point of each sub-temporal remote sensing image and the shadow change footprint. By employing the technical means of this invention, a scheme for tracking the shadow footprint of buildings in hyperspectral images over multiple time series is designed, thereby estimating the building height in multiple consecutive sub-temporal remote sensing images, and finally obtaining the height value of the building to be measured. This effectively avoids the situation where the estimated building height deviates from the actual height within a single time range, improves the accuracy and simplicity of building height measurement, and is beneficial for providing a data foundation for calculating the energy consumption and energy efficiency of power resources in building areas, thus playing an important role in the allocation of power resources. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a method for calculating building height based on remote sensing images, provided in an embodiment of the present invention.

[0051] Figure 2 This is the footprint of the shadow change of the building projected onto the horizontal ground every hour in the embodiments of the present invention;

[0052] Figure 3 is a diagram showing the positional relationship between the sun and satellite sensors in an embodiment of the present invention;

[0053] Figure 4 is a diagram showing the elevation relationship between the shadow projection plane and the horizontal projection plane in an embodiment of the present invention.

[0054] Figure 5 This is a flowchart illustrating a preferred method for calculating building height based on remote sensing images, provided by an embodiment of the present invention.

[0055] Figure 6 This is a schematic diagram of a building height calculation device based on remote sensing images provided in an embodiment of the present invention;

[0056] Figure 7 This is a schematic diagram of a building height calculation device based on remote sensing images provided in an embodiment of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] See Figure 1 This is a flowchart illustrating a method for calculating building height based on remote sensing images, provided by an embodiment of the present invention. This embodiment of the present invention provides a method for calculating building height based on remote sensing images, applicable to multi-scale, multi-time-series geographic data, specifically executed through steps S11 to S14:

[0059] S11. Acquire multi-temporal remote sensing image data containing the building to be measured, and divide the multi-temporal remote sensing image data into several sub-temporal remote sensing images.

[0060] S12. Perform image stabilization and shadow feature enhancement processing on each of the sub-time series remote sensing images;

[0061] S13. Based on each of the processed sub-time series remote sensing images, extract the shadow change footprint of the building under test under sunlight projection;

[0062] S14. Calculate the height of the building to be measured based on the solar altitude angle corresponding to the time point of each sub-time series remote sensing image and the shadow change footprint.

[0063] In this embodiment of the invention, remote sensing image data containing the building to be measured, captured by a satellite sensor, is first acquired. The remote sensing image data is then divided into multiple sub-time series. Each sub-time series remote sensing image is preprocessed, including image stabilization and shadow feature enhancement. A semantic segmentation model is used to identify buildings and shadow objects in the high-resolution image, and a multi-time series building shadow change footprint is created. Subsequently, the building height is estimated using the solar altitude angle, the length and shape of the building shadow, and the geometric relationship between the building and the building object.

[0064] Using the technical means of this invention, multiple schemes for tracking building shadow footprints in hyperspectral images over time series are designed, thereby estimating building heights in multiple consecutive sub-time series remote sensing images, and finally obtaining the height value of the building to be measured. This effectively avoids the situation where the estimated building height deviates from the actual height within a time range, improves the accuracy and convenience of building height measurement, and is conducive to providing a data foundation for calculating the energy consumption and energy efficiency of power resources in building areas, thus playing an important role in the allocation of power resources.

[0065] As a preferred embodiment, the present invention is further implemented based on the above embodiments, wherein step S11, that is, dividing the multi-time-series remote sensing image data into several sub-time-series remote sensing images, specifically includes the following steps:

[0066] Calculate time-series data of solar altitude related to latitude variations;

[0067] Based on the solar altitude time series data, the multi-time series remote sensing image data is divided into several sub-time series remote sensing images, so that the degree of shadow change of the building under test under the projection of sunlight in each sub-time series remote sensing image is the same or similar.

[0068] Specifically, see Figure 2 The shadow footprint is the hourly change of the shadow cast by a building on a horizontal ground in this embodiment of the invention. Moving the outline of an obstacle a distance in the direction opposite to the solar azimuth angle α produces the shadow footprint outline. The shadow footprint is useful for calculating the exact area of ​​the ground shadowed at a specific time. Let the sun be located at the origin of the spatial rectangular coordinate system, emitting sunlight in the xOy plane (ecliptic plane); the Earth revolves around the sun in a circular motion in the xOy plane, and the winter solstice is located at (R, 0, 0). At this time, the direction from the Earth's center to the North Pole is... The direction (this direction always remains unchanged) That is, the size of the obliquity of the ecliptic, currently approximately 23°26'. After time t, the Earth arrives at... The position (T is the orbital period), the direction of the sunlight pointing towards the Earth's center is Let the North Pole be point P and the point of direct sunlight be point Q.

[0069]

[0070] In other words, the latitude of the point where the sun is directly overhead at noon changes according to the following pattern over time:

[0071]

[0072] Here, α>0 refers to North latitude, and α<0 refers to South latitude. This yields time-series data on solar altitude related to latitude variations.

[0073] Furthermore, since the solar altitude velocity affects the rate of change of the shadow footprint, the degree of shadow change varies in remote sensing images divided into equal time series, thus affecting the accuracy of shadow detection and height measurement in the case of equal time series. Therefore, in order to make the degree of building shadow change similar in each sub-time series of remote sensing images and improve the accuracy of building height measurement, this embodiment of the invention provides a corresponding time series allocation algorithm. Here, solar declination is represented by δ, geographical latitude of the observation location is represented by φ, local time (hour angle) is represented by t, and the formula for calculating the solar altitude angle η is:

[0074] sinη=sinφ·sinδ+sinφ·cosδ·cost;

[0075] Formula for calculating the obliquity of the ecliptic:

[0076]

[0077] Here, N is the number of days since the beginning of the year. N = 0 represents midnight on January 1st, UTC (meaning the date numbering starts from 1). The relationship between the solar altitude angle and time t in a day is then calculated:

[0078]

[0079] Based on this, after obtaining the solar altitude time series data related to latitude changes, and combining it with the image time series allocation algorithm, the multi-time series remote sensing image data is divided into several sub-time series remote sensing images, so that the degree of shadow change of the building under test under sunlight projection in each sub-time series remote sensing image is the same or similar.

[0080] As a preferred embodiment, the present invention further implements the above embodiments. In step S12, the image stabilization processing for each of the sub-time series remote sensing images includes:

[0081] Feature points are matched for each of the aforementioned sub-temporal remote sensing images using a corresponding point matching algorithm.

[0082] The sub-time series remote sensing images are optimized and compensated using a preset rational function model;

[0083] The sub-time series remote sensing images are optimized using a regional network adjustment algorithm and corrected using a rotation and shearing algorithm to obtain stable sub-time series remote sensing images.

[0084] Specifically, the corresponding point matching algorithm is either the SIFT matching algorithm or the RANSAC algorithm.

[0085] In one implementation, to obtain stable and accurate corresponding points, this embodiment of the invention uses the SIFT algorithm (Scale Invariant Feature Transform) to extract feature points, and uses a priority kd-tree to find the nearest and second nearest neighbor feature points of each feature point to complete the matching work.

[0086] In another implementation, the matching points are filtered using the RANSAC (Random Sample Consensus) algorithm to obtain stable and high-precision pairs of identical points.

[0087] Furthermore, the rational function model includes an adjacent time-series mapping model, an RFM model, or a rational function compensation model, wherein the adjacent time-series mapping model:

[0088]

[0089] Where n represents the image of the nth sub-time series, and n+i (i≠0) represents the next image of the adjacent time series.

[0090] RFM Model: In constructing the inter-series mapping model, this embodiment of the invention utilizes the image's RFM (Rational Function Model) and its image-space compensation model, employing two proportional polynomials to complete the mutual transformation from image-space coordinates to object-space coordinates. As shown in the following equation:

[0091]

[0092] The above two equations contain a total of four polynomials, which are basically the same in form, as shown in the following equation:

[0093] P i (B,V,H)=a1+a2V+a3B+a4H+a5BV+a6VH

[0094] +a7BH+a8V 2 +a9B 2 +a 10 H 2 +a 11 BVH

[0095] +a12 V 3 +a 13 VB 2 +a 14 VH 2 +a 15 V 2 B

[0096] +a 16 B 3 +a 17 BH 2 +a 18 V 2 H+a 19 B 2 H+a 20 H 5 :

[0097] Where P = NUM, DEN; i = S, L. The parameters of the four polynomials are combined to form the RPC parameters (Rational Polynomial Coefficients, Geometric Positioning Model Parameters). The normalized parameters of the above formulas are (S, L, B, V, H), and their normalization formulas are S = (s - s0) / Ss, L = (l - l0) / ls, B = (b - b0) / bs, V = (v - v0) / vs, H = (h - h0) / hs: where (s, l) are the image plane coordinates, (b, v, h) are the WGS-84 geodetic latitude and longitude coordinates of the ground point, (s0, l0, b0, v0, h0) are the normalized offset factors, and (ss, ls, bs, vs, hs) are the normalized scaling factors.

[0098] From the RFM model scaling polynomial, we can obtain the formula for the forward transformation of the RFM model from image coordinates to ground point coordinates, and the formula for the inverse transformation of the RFM model from ground point coordinates to image coordinates, as shown below:

[0099] (b rfm v rfm )=RFM′(s,l,h),(s rfm , l rfm =RFM(b, v, h)

[0100] In the above formula, RFM'(s,l,h) and RFM(b,v,h) are the forward and inverse calculation functions of the RFM model, respectively.

[0101] Rational Function Compensation Model: The RFM model includes two compensation models: object compensation, which can be used to reduce remote sensing satellite sensor errors and offset flight trajectory errors during aviation, thereby reducing the systematic errors caused by geometric positioning model parameters. Therefore, rational functions are compensated and optimized.

[0102] The object compensation model directly compensates for the coordinates of ground points:

[0103]

[0104] Among them, (b) true ,v true ,h true (b) represents the latitude and longitude coordinates of the actual ground point, λ and R are the similarity transformation scale constants and the similarity transformation rotation matrix, respectively. rfm ,v rfm ,h rfm The ground point's latitude and longitude coordinates after forward transformation using the RFM model, where (b0, v0, h0) are the translation parameters.

[0105] Furthermore, the regional network adjustment process includes: calculating the exterior orientation parameters and densification point coordinates for each image. A small number of control points are used to solve for the rational function image-side compensation parameters of the RFM model within the same measurement area, thereby unifying the positioning coordinates of different images within the same survey area. The linearization equation for the densification point error function is constructed as follows:

[0106]

[0107]

[0108] Among them, F x0 and F y0 These are the coordinate deviations obtained given initial values, and xv and yv are the equation residuals. The matrix equation for Equ9 is: V = At ​​+ Bx - l.

[0109] Similarly, after constructing the linearized equation of the control point error function, the corresponding matrix equation is obtained: V = At ​​- l

[0110] The coordinate error function of the regional network adjustment is calculated using least squares adjustment for the above formula, and the increment of the affine transformation parameter is iteratively calculated until the increment reaches a certain range.

[0111] Rotation and shear correction strategies include: around an arbitrary specified anchor point (x r ,y r Perform a rotation, using the rotation transformation matrix:

[0112]

[0113] Shear transformation matrix:

[0114]

[0115] As a preferred embodiment, the present invention further implements the above embodiments, wherein step S12, that is, performing shadow feature enhancement processing on each of the sub-time series remote sensing images, includes:

[0116] The shadows displayed in the sub-time series remote sensing image are enhanced by using the local phase and amplitude response of a preset filter until the preset parameter difference remains essentially unchanged; wherein, the preset filter is designed based on the spectral and spatial characteristics of the satellite image;

[0117] The sub-time series remote sensing image is processed using a preset active optimization contour segmentation model to depict shadow edges and enhance shadow effects.

[0118] The initial curve of the depicted shadow edge is initialized with a level set to obtain the enhanced shadow edge;

[0119] A preset discrimination factor is used to filter out the water body region in the sub-time series remote sensing image to obtain a sub-time series remote sensing image after shadow feature enhancement processing; wherein, the discrimination factor refers to the factor used to compare the shadow region and the water body region in the sub-time series remote sensing image, and the discrimination factor is determined by the variance threshold of the spectral features of the remote sensing image.

[0120] In this embodiment of the invention, a feature enhancement algorithm is used to enhance the projected shadows of buildings in remotely sensed images. First, the local phase and amplitude responses of a custom filter designed based on the spectral and spatial characteristics of the satellite image are utilized to enhance the shadows and reduce intensity heterogeneity. Its general form is as follows:

[0121]

[0122] x θ =xcosθ + ysinθ, y θ = -xsinθ + ycosθ;

[0123] Where θ is the rotation angle of the filter relative to the x-axis, f is the local frequency, the filter is centered at the origin, and the standard deviations of the Gaussian function along the x and y axes are σ and σ, respectively. x and σ y .

[0124] After convolving the implemented filter with the satellite image, two region images are needed, one representing the shadowed region and the other the unshadowed region. To achieve this, the parameters of the Gabor filter function and f should be chosen to maximize the distance between the two points, and this is achieved using the following equation:

[0125]

[0126] Here, the Euclidean distance between the feature points (xi,y) and (xj,yj) of dij is given. Therefore, the Gabor filter parameters are optimized by maximizing the above objective function. The process terminates when the differences between the generated parameters for each set remain constant.

[0127] Next, the optimized active contour model (OAC model) is used to depict the shadow edges and enhance the shadow effect. The OAC model uses a filter to depict and highlight shadows, and the radiative properties of the shadow region can be represented by a global constant. The building shadow contour C is detected using the I(x,y) image. The OAC model is defined as follows:

[0128]

[0129]

[0130] Where λ1, λ2, μ, ν > 0 are fixed parameters responsible for weighting different terms; c1 and c2 are the average intensity values ​​inside image I and outside contour C, respectively; S(x, y) is the complementary image obtained from the convolution of the optimal filter Gador with the original image, defined as S(x, y) = 1 - (G*I(x, y)); r is an unknown constant responsible for describing the radiation characteristics of the shadow; it is automatically estimated as the optimal threshold after histogram analysis of S(x, y), and the estimation formula is r = 1 - max{σ 2 (t)}, 1≤t<L, where [1,L] is the gray level range of image S(x,y), σ 2 Φ is the inter-class variance; H(Φ) and δ(Φ) are the Heaviside function and the Dirac function, respectively.

[0131] Furthermore, the initial shadow curve is initialized with a level set to obtain the enhanced shadow. The evolution of the level set, specifically the zero level set, is described as a function of time.

[0132]

[0133] Based on the level set function, the initial curve is placed on the image to be evaluated:

[0134] Φ xy =C,Φ(x,y)=1inside C,Φ(x,y)=-1outside C;

[0135] The model uses the following forward finite difference scheme to evolve the initial curve and detect shadows in satellite images:

[0136] Φ x =Φx+1,y -Φ x,y ,Φ y =Φ x,1+y -Φ xy ;

[0137] Φ xy =Φ x+1,y+1 -Φ x,y+1 -(Φ x+1,y -Φ xy );

[0138] Furthermore, since the radiation characteristics of water bodies are very similar to those of shadows, it is necessary to use the variance threshold of the spectral characteristics of remote sensing images as a distinguishing factor between shadow areas and water surfaces. Finally, a global variance threshold is used to filter out water areas.

[0139] As a preferred embodiment, the present invention further implements the above embodiments. In step S14, calculating the height of the building to be measured based on the solar altitude angle corresponding to the time point of each sub-time series remote sensing image and the shadow change footprint specifically includes:

[0140] Based on the solar elevation angle and the shadow change footprint corresponding to the time point of each sub-time series remote sensing image, the height data of the building to be measured corresponding to each sub-time series remote sensing image is calculated.

[0141] Based on the height data of the building to be measured corresponding to all the sub-time series remote sensing images, the optimal solution for the height of the building to be measured is obtained by using a probabilistic data statistics algorithm.

[0142] Specifically, the step of calculating the height data of the building to be measured corresponding to each sub-time series remote sensing image based on the solar altitude angle corresponding to the time point of each sub-time series remote sensing image and the shadow change footprint specifically includes:

[0143] Based on the solar altitude angle and the sub-time series remote sensing image, the relationship between the sun, the satellite sensor that captured the time series remote sensing image, and the building under test is determined; see Figure 3, which is a diagram showing the positional relationship between the sun and the satellite sensor in an embodiment of the present invention, specifically including... Figures 3(a) to 3(c) .

[0144] Referring to Figure 3(a), when the sun and the satellite sensor are located on the same side of the building under test, the height data of the building under test corresponding to the sub-time series remote sensing image is:

[0145]

[0146]

[0147] Referring to Figure 3(b), when the azimuth angle between the sun and the satellite sensor is greater than 180°, the height data of the building to be measured corresponding to the sub-time series remote sensing image is as follows:

[0148] AB = BD × tanα;

[0149] Referring to Figure 3(c), when the azimuth angle between the sun and the satellite sensor is between 0° and 180°, the height data of the building to be measured corresponding to the sub-time series remote sensing image is as follows:

[0150]

[0151] Wherein, α is the solar altitude angle, β is the satellite sensor elevation angle, γ is the solar azimuth angle, δ is the satellite sensor azimuth angle, ε is the angle formed by the direction of the building under test and the clockwise projection of the shadow of the building under test; AB is the height data of the building under test, BC is the length of the shadow projected by the building under sunlight, BD is the total length of the shadow projected by the building under test, CD is the observable shadow length on the sub-time series remote sensing image; DE is the observable projected shadow length in the sub-time series remote sensing image.

[0152] Preferably, after extracting the shadow change footprint of the building under sunlight projection, to avoid inaccurate height calculation due to distortion of the building's shadow in the remote sensing image, before calculating the height of the building based on the solar altitude angle corresponding to the time point of each sub-time series remote sensing image and the shadow change footprint, the shadow of the building under test also needs to be corrected. The method further includes:

[0153] Determine whether the building under test is located in complex terrain or dense area; wherein, complex terrain refers to the fact that the elevation of the shadow projection surface of the building under test in the sub-time series remote sensing image is not equal to the elevation of the horizontal projection surface; and dense area refers to the fact that the shadow of the building under test in the sub-time series remote sensing image overlaps with the area occupied by adjacent buildings.

[0154] When the building under test is located in complex terrain, the two ends of the shaded line are used as buffer zones to obtain the elevation of the contour lines within the buffer zones. The elevation is then assigned to the end of the fishing net line to correct the shaded length.

[0155] When the building under test is located in a dense area, shadow regularization extraction is used to process the sub-time series remote sensing image to correct the shadow length.

[0156] Specifically, referring to Figure 4, which is a diagram showing the elevation relationship between the shadow projection plane and the horizontal projection plane in an embodiment of the present invention, including Figure 4(a) and Figure 4(b). Shadow length correction under complex terrain includes two cases: when the elevation of the shadow projection plane is higher than the elevation of the horizontal projection plane, and when the elevation of the shadow projection plane is lower than the elevation of the horizontal projection plane.

[0157] Referring to Figure 4(a), when the elevation of the shadow projection plane is higher than the elevation of the horizontal projection plane, the shadow length result is corrected by the following formula:

[0158]

[0159] Where EG is the measured shadow length in the image, HF is the actual shadow length, and h, h1, and h2 are the heights of the building's base and the end of the shadow, respectively.

[0160] Referring to Figure 4(b), when the elevation of the shadow projection plane is less than the elevation of the horizontal projection plane, the shadow length result is corrected by the following formula:

[0161]

[0162] Where FH is the total measured shadow length of the image, and GE is the actual shadow length.

[0163] Due to factors such as building density, solar altitude angle and declination, and remote sensing satellite azimuth angle, building shadows in densely populated areas may obscure adjacent buildings, causing overlap between the actual projected shapes and building footprints. To address this issue, a solution for correcting building shadow lengths in densely populated areas is proposed, including:

[0164] Regularized shadow extraction: First, create an envelope rectangle for the building vector boundary and scale it proportionally; second, obtain the angular coordinates from the envelope rectangle and generate cutting lines based on the solar azimuth and angular coordinates; then, trim the regularized shadows using the cutting lines and overlapping shadows; finally, match the buildings and regularized shadows according to the solar azimuth direction, while reducing the scaling ratio of the building envelope rectangle until all buildings match the shadows, which can then be used to calculate building height.

[0165] Alternatively, the shadow length can be calculated using the fishing net and tower protection criteria. This method comprises two parts: generating the fishing net line and eliminating gross errors. Generating the fishing net line involves numbering the shadows cast by the building and constructing the fishing net line based on the solar azimuth angle. Finally, the fishing net line is superimposed on the shadow to obtain the shadow line. Eliminating gross errors involves calculating the residual length of each cutting line on the shadow plane: V i =|X i -X|; After calculating the standard deviation σ and arithmetic mean of all line lengths in each shaded plane plot, if V i≤3σ, normal test values ​​should be retained; if V i For values ​​≤3σ, outlier detections should be discarded, and iterative calculations should be performed until all gross errors are eliminated.

[0166] For a preferred embodiment, see Figure 5 This is a flowchart illustrating a preferred method for calculating building height based on remote sensing images, provided by an embodiment of the present invention. This embodiment further implements the above embodiments, and in step S14, i.e., after calculating the height of the building to be measured, the method further includes steps S15 to S17:

[0167] S15. Calculate the volume of the building under test based on its height and the area it occupies in the time-series remote sensing image.

[0168] S16. Calculate the required energy supply for the target area based on the volume of each building to be tested within the target area; wherein the energy supply includes heating and cooling capacity;

[0169] S17. Based on the energy supply required by the target area, assess the rationality of the scale of air conditioning configuration in the building space of the target area.

[0170] Based on the optimized building heights described above, subsequent wide-area air conditioning energy consumption calculations and energy result assessments are conducted. First, a quantitative analysis of the energy supplied by air conditioning within this area is performed, calculating the volume of each building:

[0171] V i =E(H i )*Sf i ;

[0172] Among them, E(H i Sf represents the energy supply per unit volume of the i-th building within the range of the sub-time series remote sensing image. i The area of ​​the i-th building within the range of the sub-time series remote sensing image is used to determine the cooling and heating capacity of the air conditioner for the i-th building in that area.

[0173] Q = k x V i ;

[0174] Where k x Thermal conductivity is defined as:

[0175]

[0176] q″ x Let x be the heat flux in the direction of heat flow (W / m2). The gradient (K / m) in that direction;

[0177] Next, the energy conversion efficiency of the air conditioning system is evaluated. The ease with which the energy consumed by the air conditioning system is converted into cooling and heating varies; therefore, based on the building's required cooling or heating capacity, evaluation indicators for the air conditioning energy conversion system are used to measure whether it meets energy conservation assessment requirements. The energy quality coefficients for air conditioning heating and cooling are as follows:

[0178]

[0179] Where T0 is the outdoor air temperature (K) and T is the indoor air temperature (K).

[0180] Finally, the Energy Conversion Coefficient (ECC) is used to examine and verify the rationality of the air conditioning cold and heat source configuration.

[0181]

[0182] Where, ∑Q C and ∑Q h These represent the annual cooling and heating capacity of air conditioning units within the total building volume of the region, E i This is the total annual consumption (kWh or GJ) of the i-th type of energy required for both heating and cooling sources. According to the "Energy Conservation Assessment Standard for Public Buildings," when ECC... HVAC A value of ≥0.2 indicates that the air conditioning energy conversion efficiency in this region meets the basic requirements for energy conservation assessment.

[0183] Evaluate the energy consumption index of air conditioning power transmission and distribution, design the air conditioning power transmission and distribution coefficient (TDC), and determine the amount of cooling and heating that its air system and water system can transmit and distribute under unit power consumption.

[0184]

[0185] Ep represents the total annual power consumption (kWh) of the building's air conditioning chiller pumps and heating pumps; Ef represents the total annual power consumption (kWh) of the building's air conditioning units and fresh air handling units. According to the "Energy Conservation Assessment Standard for Public Buildings," when TDC ≥ 5, it indicates that the energy consumption of the air conditioning power supply in the region during transmission and distribution within the power system basically meets the energy conservation requirements.

[0186] Based on time-series analysis to predict the periodicity of air conditioning scale, quantitative data on air conditioning scale within a defined area for each remote sensing image are analyzed for multi-time-series seasonal, weekly, and daily periodicity. The ARIMA(p,d,q) prediction model (Autoregressive Integrated Moving Average model) is used.

[0187]

[0188] Among them, X t The multiplication coefficient AR represents the autoregressive series, where p is the number of autoregressive terms; the equation result MA represents the moving average, where q is the number of moving average terms; and d is the order of differencing required to make the time series stationary. X t This can be used to predict the energy conversion efficiency and energy consumption indicators of air conditioners in the future time t, thereby predicting the energy efficiency and energy consumption of air conditioners over a period of time in the future, and providing a reference for the reasonable optimization of large-scale air conditioner usage plans.

[0189] This invention provides a precise measurement scheme for building height based on shadow footprint changes in multiple sub-time series remote sensing images. Furthermore, the method is used to infer building volume, providing a reference for the allocation of air conditioning power resources, especially in densely built-up areas common in cities.

[0190] See Figure 6 This is a schematic diagram of a building height calculation device based on remote sensing images provided in an embodiment of the present invention. The embodiment of the present invention provides a building height calculation device 20 based on remote sensing images, comprising:

[0191] The remote sensing image acquisition module 21 is used to: acquire multi-temporal remote sensing image data containing the building to be measured, and divide the multi-temporal remote sensing image data into several sub-temporal remote sensing images;

[0192] Image processing module 22 is used to perform image stabilization processing and shadow feature enhancement processing on each of the sub-time series remote sensing images;

[0193] The shadow change footprint extraction module 23 is used to extract the shadow change footprint of the building under the projection of sunlight based on each of the processed sub-time series remote sensing images.

[0194] The building height calculation module 24 is used to calculate the height of the building under test based on the solar altitude angle corresponding to the time point of each sub-time series remote sensing image and the shadow change footprint.

[0195] Using the technical means of this invention, multiple time-series tracking schemes for building shadow footprints in hyperspectral images are designed to estimate building heights in multiple consecutive sub-time-series remote sensing images, ultimately obtaining the height value of the building to be measured. This effectively avoids deviations between the estimated building height and the actual height within a single time range, improving the accuracy and ease of building height measurement. It also provides a data foundation for calculating energy consumption and energy efficiency of power resources in building areas, thus playing an important role in the allocation of power resources.

[0196] It should be noted that the building height calculation device based on remote sensing images provided in this embodiment of the invention is used to execute all the process steps of the building height calculation method based on remote sensing images in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0197] See Figure 7 This is a schematic diagram of a building height calculation device based on remote sensing images provided in an embodiment of the present invention. The embodiment of the present invention provides a building height calculation device 30 based on remote sensing images, including a processor 31, a memory 32, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the building height calculation method based on remote sensing images as described in any of the above embodiments.

[0198] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0199] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for calculating building height based on remote sensing images, characterized in that, include: Acquire multi-temporal remote sensing image data containing the building to be measured, and divide the multi-temporal remote sensing image data into several sub-temporal remote sensing images; Image stabilization and shadow feature enhancement are performed on each of the aforementioned sub-time series remote sensing images; Based on each processed sub-time series remote sensing image, the footprint of shadow change of the building under the projection of sunlight is extracted; The height of the building under test is calculated based on the solar altitude angle corresponding to the time point of each sub-time series remote sensing image and the shadow change footprint. The process of dividing the multi-time-series remote sensing image data into several sub-time-series remote sensing images specifically includes: Calculate time-series data of solar altitude related to latitude variations; Based on the solar altitude time series data, the multi-time series remote sensing image data is divided into several sub-time series remote sensing images so that the degree of shadow change of the building under test under the projection of sunlight in each sub-time series remote sensing image is the same or similar. The step of calculating the height of the building under test based on the solar altitude angle corresponding to the time point of each sub-time series remote sensing image and the shadow change footprint specifically includes: Based on the solar elevation angle and the shadow change footprint corresponding to the time point of each sub-time series remote sensing image, the height data of the building to be measured corresponding to each sub-time series remote sensing image is calculated. Based on the height data of the building to be measured corresponding to all the sub-time series remote sensing images, the optimal solution for the height of the building to be measured is obtained by using a probabilistic data statistics algorithm.

2. The method for calculating building height based on remote sensing images as described in claim 1, characterized in that, The image stabilization process for each of the sub-time series remote sensing images includes: Feature points are matched for each of the aforementioned sub-temporal remote sensing images using a corresponding point matching algorithm. The sub-time series remote sensing images are optimized and compensated using a preset rational function model; The sub-time series remote sensing images are optimized using a regional network adjustment algorithm and corrected using a rotation and shearing algorithm to obtain stable sub-time series remote sensing images.

3. The method for calculating building height based on remote sensing images as described in claim 1, characterized in that, Shadow feature enhancement processing is performed on each of the aforementioned sub-time series remote sensing images, including: The shadows displayed in the sub-time series remote sensing image are enhanced by using the local phase and amplitude response of a preset filter until the preset parameter difference remains essentially unchanged; wherein, the preset filter is designed based on the spectral and spatial characteristics of the satellite image; The sub-time series remote sensing image is processed using a preset active optimization contour segmentation model to depict shadow edges and enhance shadow effects. The initial curve of the depicted shadow edge is initialized with a level set to obtain the enhanced shadow edge; A preset discrimination factor is used to filter out the water body region in the sub-time series remote sensing image to obtain a sub-time series remote sensing image after shadow feature enhancement processing; wherein, the discrimination factor refers to the factor used to compare the shadow region and the water body region in the sub-time series remote sensing image, and the discrimination factor is determined by the variance threshold of the spectral features of the remote sensing image.

4. The method for calculating building height based on remote sensing images as described in claim 1, characterized in that, The step of calculating the height data of the building to be measured corresponding to each sub-time series remote sensing image based on the solar altitude angle corresponding to the time point of each sub-time series remote sensing image and the shadow change footprint specifically includes: Based on the solar elevation angle and the sub-time series remote sensing image, determine the relationship between the sun, the satellite sensor that captured the time series remote sensing image, and the building under test; When the sun and the satellite sensor are located on the same side of the building under test, the height data of the building under test corresponding to the sub-time series remote sensing image is: ； ； When the azimuth angle between the sun and the satellite sensor is greater than 180°, the height data of the building to be measured corresponding to the sub-time series remote sensing image is: ； When the azimuth angle between the sun and the satellite sensor is between 0° and 180°, the height data of the building to be measured corresponding to the sub-time series remote sensing image is: ； in, The solar altitude angle, For the satellite sensor elevation angle, The azimuth of the sun. For the azimuth angle of the satellite sensor, AB is the angle formed by the direction of the building under test and the clockwise projection of the shadow of the building under test; BC is the height data of the building under test; BD is the length of the shadow projected by the building under test under sunlight; CD is the observable shadow length on the sub-time series remote sensing image; DE is the observable projected shadow length in the sub-time series remote sensing image.

5. The method for calculating building height based on remote sensing images as described in claim 4, characterized in that, After extracting the shadow change footprint of the building under sunlight projection, and before calculating the height of the building under test based on the solar altitude angle corresponding to the time point of each sub-time series remote sensing image and the shadow change footprint, the method further includes: Determine whether the building under test is located in complex terrain or dense area; wherein, complex terrain refers to the fact that the elevation of the shadow projection surface of the building under test in the sub-time series remote sensing image is not equal to the elevation of the horizontal projection surface; and dense area refers to the fact that the shadow of the building under test in the sub-time series remote sensing image overlaps with the area occupied by adjacent buildings. When the building under test is located in complex terrain, the two ends of the shaded line are used as buffer zones to obtain the elevation of the contour lines within the buffer zones. The elevation is then assigned to the end of the fishing net line to correct the shaded length. When the building under test is located in a dense area, shadow regularization extraction is used to process the sub-time series remote sensing image to correct the shadow length.

6. The method for calculating building height based on remote sensing images as described in claim 1, characterized in that, After calculating the height of the building to be measured, the method further includes: The volume of the building under test is calculated based on its height and the area it occupies in the time-series remote sensing image. Based on the volume of each building to be tested within the target area, calculate the required energy supply for the target area; wherein, the energy supply includes heating and cooling capacity; Based on the energy supply required by the target area, assess the rationality of the scale of air conditioning configuration in the building space of the target area.

7. A building height calculation device based on remote sensing images, characterized in that, include: The remote sensing image acquisition module is used to acquire multi-temporal remote sensing image data containing the building to be measured, and to divide the multi-temporal remote sensing image data into several sub-temporal remote sensing images. The image processing module is used to perform image stabilization and shadow feature enhancement processing on each of the sub-time series remote sensing images; The shadow change footprint extraction module is used to extract the shadow change footprint of the building under test under sunlight projection based on each of the processed sub-time series remote sensing images. The building height calculation module is used to calculate the height of the building under test based on the solar altitude angle corresponding to the time point of each sub-time series remote sensing image and the shadow change footprint; The remote sensing image acquisition module is specifically used for: Calculate time-series data of solar altitude related to latitude variations; Based on the solar altitude time series data, the multi-time series remote sensing image data is divided into several sub-time series remote sensing images so that the degree of shadow change of the building under test under the projection of sunlight in each sub-time series remote sensing image is the same or similar. The building height calculation module is specifically used for: Based on the solar elevation angle and the shadow change footprint corresponding to the time point of each sub-time series remote sensing image, the height data of the building to be measured corresponding to each sub-time series remote sensing image is calculated. Based on the height data of the building to be measured corresponding to all the sub-time series remote sensing images, the optimal solution for the height of the building to be measured is obtained by using a probabilistic data statistics algorithm.

8. A building height calculation device based on remote sensing images, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the building height calculation method based on remote sensing images as described in any one of claims 1 to 6.

Images