CN116468598A - High-resolution aerial image and low-resolution satellite image matching method, device and storage device - Google Patents
High-resolution aerial image and low-resolution satellite image matching method, device and storage device Download PDFInfo
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- 230000000007 visual effect Effects 0.000 description 2
- 101150053844 APP1 gene Proteins 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
- G01C11/04—Interpretation of pictures
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- G06T3/14—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention discloses a method, equipment and storage equipment for matching high-resolution aerial images with low-resolution satellite images, wherein the method comprises the following steps: acquiring high-score aerial images and low-score satellite images; reading high-resolution aerial image file parameters; calculating the actual longitude and latitude distance between any pixel point of the high-resolution aerial image and the image center point by using the obtained file parameters; obtaining the longitude and latitude of four vertexes of the high-resolution aerial image according to the actual longitude and latitude distance; and (3) using four vertexes and a central point of the high-resolution aerial image as reference points, and automatically registering with the low-resolution satellite image based on longitude and latitude coordinates and pixel coordinates of the five reference points to obtain a final matching result. The invention has the beneficial effects that: the algorithm complexity is extremely low, complex characteristic point matching is not needed for the high-resolution aerial image and the low-resolution satellite image, and the high-resolution aerial image shot under any course angle can be automatically registered under a longitude and latitude coordinate system, so that the rapid automatic processing of the high-resolution aerial image is realized.
Description
Technical Field
The invention relates to the field of image matching, in particular to a method, equipment and storage equipment for matching high-resolution aerial images with low-resolution satellite images.
Background
The high-resolution aerial image records the longitude and latitude of the position of the camera, namely the longitude and latitude of the center point of the image, but does not record the longitude and latitude of the four corner points of the image. Because the course angle of the aircraft is dynamically changed during flight, the shot high-resolution aerial image is not north-south-oriented, and the shot image has no space reference system information and cannot be directly overlapped with the low-resolution satellite image in the longitude and latitude coordinate system. The existing technical means is that the obtained high-resolution aerial images are spliced and then processed to obtain the orthographic images, and then the orthographic images and the low-resolution satellite images can be displayed in a superimposed mode. However, the process involves image feature point matching, the algorithm complexity is very high, the whole processing process is very time-consuming, manual participation is generally required, and the efficiency is low. In the application fields of target real-time tracking, real-time monitoring and early warning and the like with strong requirements on image real-time performance, a method for quickly and automatically matching high-resolution aerial images to low-resolution satellite images is urgently needed, and quick superposition display of the high-resolution aerial images and the low-resolution satellite images is realized.
Disclosure of Invention
In order to solve the problem of weak real-time performance of target tracking, monitoring and early warning in the prior art, the invention provides a method, equipment and storage equipment for matching high-resolution aerial images and low-resolution satellite images, wherein the method comprises the following steps:
s1: acquiring high-score aerial images and low-score satellite images;
s2: reading high-resolution aerial image file parameters;
s3: calculating the actual longitude and latitude distance between any pixel point of the high-resolution aerial image and the image center point by using the file parameters obtained in the step S2;
s4: obtaining the longitude and latitude of four vertexes of the high-resolution aerial image according to the actual longitude and latitude distance;
s5: and (3) using four vertexes and a central point of the high-resolution aerial image as reference points, and automatically registering with the low-resolution satellite image to obtain a final matching result.
The storage device stores instructions and data for realizing a high-resolution aerial image and low-resolution satellite image matching method.
A high score aerial image and low score satellite image quick match device comprising: a processor and the storage device; the processor loads and executes the instructions and the data in the storage device to realize a high-resolution aerial image and low-resolution satellite image matching method.
The beneficial effects provided by the invention are as follows: the algorithm complexity is extremely low, complex characteristic point matching is not needed for the high-resolution aerial image and the low-resolution satellite image, the high-resolution aerial image shot under any course angle can be automatically registered under a longitude and latitude coordinate system, the high-resolution aerial image can be rapidly and automatically processed, the high-resolution aerial image and the low-resolution satellite image can be rapidly overlapped and displayed, and technical support is provided for real-time target tracking, real-time monitoring and early warning and other applications based on the high-resolution aerial image.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic view of a coordinate system of a high-resolution aerial image and a longitude and latitude coordinate system of geographic coordinates;
FIG. 3 is a graph showing the result of matching four vertices of a high-resolution aerial image in a low-resolution image;
FIG. 4 is a post registration result of high score aerial images;
FIG. 5 is a superimposed display of registered high-resolution aerial images and low-resolution satellite images;
FIG. 6 is a schematic diagram of the operation of a hardware device in an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be further described with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flow chart of the method of the present invention. The invention provides a high-resolution aerial image and low-resolution satellite image matching method, which comprises the following steps:
s1: acquiring high-score aerial images and low-score satellite images;
s2: reading high-resolution aerial image file parameters;
the high-score aerial image file parameters in step S2 include: the camera focal length f, the height H and the width w of the high-resolution aerial image, the course angle ψ, the longitude and latitude cen_lon and cen_lat of the shooting position of the camera and the height H of the camera relative to the ground.
The exif information stored in the high-resolution aerial image file is analyzed by using an OpenCV image processing library, and the focal length f of the camera and the width w and the height h of the high-resolution aerial image are read. And calculating the row number and the column number of the center point pixels of the high-resolution aerial image, namely cen_h and cen_w.
cen_h＝h/2
cen_w＝w/2
And analyzing an APP1 part added in the high-resolution aerial image file by using the OpenCV image processing library, reading a course angle psi of the camera during shooting, the height H of the camera relative to the ground, and the longitude and latitude (cen_lon, cen_lat) of the shooting position of the camera, wherein the longitude and latitude of the shooting position of the camera are the longitude and latitude of the center point of the high-resolution aerial image.
S3: calculating the actual longitude and latitude distance between any pixel point of the high-resolution aerial image and the image center point by using the file parameters obtained in the step S2;
the step S3 is specifically as follows:
s31, obtaining pixel distances dh and dw between any pixel point (no_h and no_w) and the center point of the image;
when the camera mounted on the aircraft shoots, if the heading of the aircraft does not fly in the north direction, the coordinate system of the high-resolution aerial image obtained by shooting cannot completely coincide with the longitude and latitude coordinate system of the geographic coordinates. As shown in fig. 2, fig. 2 is a schematic diagram of a longitude and latitude coordinate system of a coordinate system and geographic coordinates of a high-resolution aerial image; wherein the solid line coordinate axis is longitude and latitude coordinate system, and the dotted line coordinate axis is high-resolution aerial image coordinate system.
Let the row number and column number of the pixel point to be matched in the high-resolution aerial image be no_h and no_w respectively, as shown by the solid dots in fig. 2. First, the pixel distance between the pixel point (no_h, no_w) and the center point (cen_h, cen_w) of the high-resolution aerial image needs to be calculated.
S32, calculating an included angle c formed by a connecting line of the pixel points (no_h, no_w) and the image center point and the fact that the pixel points (no_h, no_w) are perpendicular to the flight direction of the aircraft;
it should be noted that, the calculation formula of the included angle c is: c=arctan (dh/dw).
S33, calculating actual ground distances of pixel points (no_h, no_w) corresponding to the vertical axis and the horizontal axis of the high-resolution aerial image coordinate system respectively to be Lh and Lw;
let the camera sensor pixel size be pixel_size in μm. And calculating the ground actual distances corresponding to the pixel points (no_h, no_w) in the two axial directions of the coordinate system of the high-resolution aerial image, wherein the unit is m, and the units are shown as line segments Lh and Lw in fig. 2.
Lh＝(dh*H*pixel_size)/(1000*f)
Lw＝(dw*H*pixel_size)/(1000*f)
Calculating the ground actual distance L between the pixel points (no_h, no_w) and the center points (cen_h, cen_w) of the high-resolution aerial images according to Lh and Lw,the unit is m.
S4: according to the actual longitude and latitude distance, calculating the longitude and latitude of four vertexes of the high-resolution aerial image;
s41, judging the quadrant of the pixel point (no_h, no_w) according to the relative positions of the pixel point (no_h, no_w) and the image center point;
s42, calculating an angle e from the forward east to the anticlockwise direction to the pixel point (no_h, no_w) in the longitude and latitude coordinate system according to the quadrant to which the pixel point (no_h, no_w) belongs;
let e be the angle from the forward east to the anticlockwise to the pixel point (no_h, no_w) in the longitude and latitude coordinate system, the range is 0-360 degrees, and the quadrant of the pixel point (no_h, no_w) in the coordinate system of the high-resolution aerial image is judged according to the pixel coordinate values of the pixel point (no_h, no_w) and the pixel point (cen_h, cen_w) of the high-resolution aerial image.
If cen_h > no_h and cen_w < no_w, then the pixel (no_h, no_w) is in the first quadrant, e=c- ψ.
If cen_h > no_h and cen_w > no_w, then the pixel point (no_h, no_w) is in the second quadrant, e=180- ψ -c.
If cen_h < no_h and cen_w > no_w, then the pixel point (no_h, no_w) is in the third quadrant, e=180- ψ+c.
If cen_h < no_h and cen_w < no_w, then the pixel (no_h, no_w) is in the fourth quadrant, e=360- ψ -c.
S43, calculating the actual longitude and latitude lon and lat of the pixel point according to the angle e and the longitude and latitude cen_lon and cen_lat of the shooting position of the camera;
lat＝cen_lat+L*sin(e)/110946.2521327066
lon＝cen_lon+L*cos(e)/(111319.4907932455*cos(lat))
by using the steps, the actual longitude and latitude coordinates of the upper left corner A (1, 1), the lower left corner B (h, 1), the lower right corner C (h, w) and the upper right corner D (1, w) of the high-resolution aerial image are calculated in sequence.
Referring to fig. 3, fig. 3 is a result of matching calculation of four corners of the high-resolution aerial image in the low-resolution remote sensing image.
Taking the high-resolution aerial image shown in fig. 3 (b) as an example, the height is 5460, the width is 8192, and the heading angle ψ=184.7. The height h= 80.028m of the camera relative to the ground, the focal length of the camera is f=35 mm, the pixel size of the camera sensor is pixel_size=4.4 μm, and the longitude and latitude calculation result of four points of A, B, C, D is shown in fig. 3 (a). The longitude and latitude coordinates corresponding to the center point E (2730, 4096) in fig. 3 (b) are the longitude and latitude (cen_lon, cen_lat) of the camera shooting position read from the high-resolution aerial image file.
S5: and (3) using four vertexes and a central point of the high-resolution aerial image as reference points, and automatically registering with the low-resolution satellite image based on longitude and latitude coordinates and pixel coordinates of the five reference points to obtain a final matching result.
And using the A, B, C, D four points and the center point E as control points, and correcting the high-resolution aerial image by using a ArcGIS Engine SDK geographic registration function interface to realize automatic matching of the high-resolution aerial image in a pixel coordinate system and the low-resolution satellite image in a longitude and latitude coordinate system. The result obtained after registration is shown in fig. 4, and the registered high-resolution aerial image and low-resolution satellite image are displayed in an ArcGIS Desktop software in a superimposed manner, so that the effect shown in fig. 5 can be obtained.
Through the steps, the rapid automatic matching of the high-resolution aerial image and the low-resolution satellite image can be completed, and as can be seen from fig. 5, the position deviation of the high-resolution aerial image and the low-resolution satellite image in the superposition display is small, and the requirements of visual interpretation and visual analysis of the images can be completely met.
Referring to fig. 6, fig. 6 is a schematic working diagram of a hardware device according to an embodiment of the present invention, where the hardware device specifically includes: a high-score aerial image and low-score satellite image matching device 401, a processor 402 and a storage device 403.
A high-score aerial image and low-score satellite image matching device 401: the high-resolution aerial image and low-resolution satellite image matching device 401 implements the high-resolution aerial image and low-resolution satellite image matching method.
Processor 402: the processor 402 loads and executes the instructions and data in the storage device 403 to implement the high-resolution aerial image and low-resolution satellite image matching method.
Storage device 403: the storage device 403 stores instructions and data; the storage device 403 is configured to implement the method for matching high-resolution aerial images with low-resolution satellite images.
In combination, the invention has the beneficial effects that: the algorithm complexity is extremely low, complex characteristic point matching is not needed for the high-resolution aerial image and the low-resolution satellite image, the high-resolution aerial image shot under any course angle can be automatically registered under a longitude and latitude coordinate system, the high-resolution aerial image can be rapidly and automatically processed, the high-resolution aerial image and the low-resolution satellite image can be rapidly overlapped and displayed, and technical support is provided for real-time target tracking, real-time monitoring and early warning and other applications based on the high-resolution aerial image.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (9)
1. A high-resolution aerial image and low-resolution satellite image matching method is characterized in that: the method comprises the following steps:
s1: acquiring high-score aerial images and low-score satellite images;
s2: reading high-resolution aerial image file parameters;
s3: calculating the actual longitude and latitude distance between any pixel point of the high-resolution aerial image and the image center point by using the file parameters obtained in the step S2;
s4: obtaining the longitude and latitude of four vertexes of the high-resolution aerial image according to the actual longitude and latitude distance;
s5: and (3) using four vertexes and a central point of the high-resolution aerial image as reference points, and automatically registering with the low-resolution satellite image based on longitude and latitude coordinates and pixel coordinates of the five reference points to obtain a final matching result.
2. The method for matching high-resolution aerial images with low-resolution satellite images according to claim 1, wherein: the high-score aerial image file parameters in step S2 include: the camera focal length f, the height H and the width w of the high-resolution aerial image, the course angle ψ, the longitude and latitude cen_lon and cen_lat of the shooting position of the camera and the height H of the camera relative to the ground.
3. The method for matching high-resolution aerial images with low-resolution satellite images according to claim 2, wherein: the step S3 is specifically as follows:
s31, obtaining pixel distances dh and dw between any pixel point (no_h and no_w) and the center point of the image;
s32, calculating an included angle c formed by a connecting line of the pixel points (no_h, no_w) and the image center point and the fact that the pixel points (no_h, no_w) are perpendicular to the flight direction of the aircraft; the calculation formula of the included angle c is as follows: c=arctan (dh/dw);
s33, calculating the ground actual distances Lh and Lw of the pixel points (no_h and no_w) corresponding to the two axes of the high-resolution aerial image coordinate system.
4. A method for matching high-resolution aerial images with low-resolution satellite images as defined in claim 3, wherein: the step S4 is specifically as follows:
s41, judging the quadrant of the pixel point (no_h, no_w) according to the relative positions of the pixel point (no_h, no_w) and the image center point;
s42, calculating an angle e from the forward east to the anticlockwise direction to the pixel point (no_h, no_w) in the longitude and latitude coordinate system according to the quadrant to which the pixel point (no_h, no_w) belongs;
s43, calculating the actual longitude and latitude lon and lat of the pixel point and the image center point according to the angle e and the longitude and latitude cen_lon and cen_lat of the shooting position of the camera.
5. The method for matching high-resolution aerial images with low-resolution satellite images according to claim 4, wherein: in step S33, the calculation formula of the actual distances Lh, lw is as follows:
Lh＝(dh*H*pixel_size)/(1000*f)
Lw＝(dw*H*pixel_size)/(1000*f)
calculating the ground actual distance L between the pixel points (no_h, no_w) and the center points (cen_h, cen_w) of the high-resolution aerial images according to Lh and Lw:
6. the method for matching high-resolution aerial images with low-resolution satellite images according to claim 5, wherein: in steps S41 to S42, the specific rule for determining the quadrant to which the pixel belongs and calculating the angle e is as follows: .
If cen_h > no_h and cen_w < no_w, then the pixel (no_h, no_w) is in the first quadrant, e=c- ψ.
If cen_h > no_h and cen_w > no_w, then the pixel point (no_h, no_w) is in the second quadrant, e=180- ψ -c.
If cen_h < no_h and cen_w > no_w, then the pixel point (no_h, no_w) is in the third quadrant, e=180- ψ+c.
If cen_h < no_h and cen_w < no_w, then the pixel (no_h, no_w) is in the fourth quadrant, e=360- ψ -c.
7. The method for matching high-resolution aerial images with low-resolution satellite images according to claim 6, wherein: in step S43, the calculation formulas of the actual longitude and latitude lon and lat are as follows:
lat＝cen_lat+L*sin(e)/110946.2521327066
lon＝cen_lon+L*cos(e)/(111319.4907932455*cos(lat))。
8. a memory device, characterized by: the storage device stores instructions and data for implementing any one of the high-resolution aerial image and low-resolution satellite image matching methods described in claims 1-7.
9. A high-score aerial image and low-score satellite image matching device is characterized in that: comprising the following steps: a processor and a storage device; the processor loads and executes instructions and data in the storage device for implementing any one of the high-resolution aerial image and low-resolution satellite image matching methods of claims 1-7.
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