CN115330619A - Local geometric fine correction method suitable for high-resolution remote sensing image - Google Patents
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Abstract
The invention relates to a local geometric fine correction method suitable for a high-resolution remote sensing image, which relates to the field of geometric fine correction of the remote sensing image and solves the problems that the time and the calculation resources are wasted when the whole image is calculated by the conventional method, and the geometric fine correction fails because the conventional matching method is easy to generate wrong matching; according to the longitude and latitude of the local area which is interested by the user, resampling by using an indirect method to generate an image of the local area with rough geographic coordinates; and estimating the rough coordinate offset between the local area image and the reference base map by adopting a perceptual hash algorithm, intercepting a part of image corresponding to the local area image from the reference base map, and registering the local area image to be processed and the local reference base map by utilizing Fourier transform blocking. The method provided by the invention has the advantages of remarkably improving the speed, the precision and the stability, and is suitable for various meter-level and sub-meter-level high-resolution remote sensing images.
Description
Technical Field
The invention relates to the field of geometric fine correction of remote sensing images, in particular to a design of a local geometric fine correction method suitable for high-resolution remote sensing images.
Background
The traditional geometric correction method for the remote sensing image generally utilizes information such as ground control points, satellite orbit parameters, satellite attitude parameters, sensor optical parameters and the like to establish a geometric correction model, so that geometric deformation of the remote sensing image caused by factors such as a projection mode, sensor attitude errors, terrain relief, atmospheric refraction, earth rotation and the like is corrected, coordinate errors of image pixels in a geographic coordinate system are eliminated, and the image pixels are corrected to correct positions. The method generally needs to select the ground control points manually, has low efficiency and large workload, and is difficult to meet the requirements of automatic, quick and accurate processing under the condition of the current massive remote sensing images.
At present, the mainstream geometric precise correction method based on image matching can meet the requirement of automation. The method takes a processed remote sensing image with accurate geographic coordinates as a reference base map, and performs homonymy point matching on the image to be processed and the reference base map, wherein the more common algorithms comprise a Scale-Invariant Feature Transform (SIFT) algorithm, an accelerated Up Robust Feature algorithm (SURF) and the like; constraining the matching result by utilizing homography transformation or affine transformation, eliminating the matching result with larger error, and taking the obtained dotted pair as a ground control point; the transformation relation between the image to be processed and the image coordinate system after geometric fine correction can be described by a polynomial model, the coefficients of the polynomial can be solved through ground control points, the original image is resampled by an indirect method, so that the geometric fine correction of the whole image is realized, and then the image result in the local range which is interested by a user is obtained by cutting.
Because the RPC coefficients describe the transformation relationship between the pixel coordinates and the geographic coordinates of the entire image, the conventional geometric refinement method based on image matching needs to calculate the entire image and then cut the calculation result to obtain the image after the local geometric refinement which is interested by the user. In actual work, users often only interest in local areas in the images, and calculating images outside the areas of interest of the users wastes time and calculation resources. Therefore, the invention aims to perform geometric fine correction calculation on only the local area image which is interested by the user, and avoids the calculation of the whole image in the conventional method so as to reduce the calculation amount and improve the efficiency.
Because the acquisition time of the image to be processed and the reference base map is usually longer, the ground object can be changed greatly; the difference between the sensor and the solar radiation can cause the difference between the images in brightness and color; in addition, repeated textures or rare feature points may exist in a shooting region, and a common homonymy point matching algorithm such as SIFT and SURF is influenced by the above factors to cause more wrong matching, so that geometric fine correction fails.
Disclosure of Invention
The invention provides a local geometric precise correction method suitable for high-resolution remote sensing images, and aims to solve the problems that time and computing resources are wasted when the existing method is used for computing the whole image, and geometric precise correction fails due to the fact that the existing matching method is adopted and error matching is prone to occurring.
The local geometric precise correction method suitable for the high-resolution remote sensing image is realized by the following steps:
step one, selecting a whole image I to be processed In According to the whole image I In Calculating RPC coefficient of the whole image I In The latitude and longitude range of (c); then setting a latitude and longitude range of the local area;
secondly, according to the latitude and longitude range of the local area in the first step, an indirect method is adopted to resample and generate a local area image I Loc Go through the local area image I Loc All pixels of (2) to obtain I Loc Pixel values of all pixels;
step three, calculating a reference base map I of the local area according to the latitude and longitude range of the local area and the RPC coefficient in the step one Ref In said reference base I Ref Taking a sliding window upwards to obtain a reference base map in the sliding window
Step four, respectively calculating local area images I by adopting a perceptual hash algorithm Loc With reference to the base in the sliding window
The pHash fingerprint of (1), namely: the local area image I Loc With reference to the base in the sliding window
Carrying out DCT transformation after downsampling to 100 multiplied by 100 pixels, and intercepting the pHash fingerprint of a 10 multiplied by 10 area at the upper left corner of an image after DCT transformation;
step five, counting the local area image I Loc With reference to the base in all sliding windows
And from the calculated minimum reference base map
Obtaining a truncated reference base map
Sixthly, utilizing Fourier Mellin transform pair I in a blocking mode Loc And
match is made, then pair I Loc And (5) resampling, and realizing geometric fine correction of the image.
The invention has the beneficial effects that: according to the method, from the area range in which a user is interested, the image with rough geographic coordinates in a local area is generated by utilizing indirect resampling according to the RPC coefficient and the latitude and longitude range of the original whole image, and then the local reference base map corresponding to the local area is obtained through the perceptual hash algorithm, so that the subsequent Fourier mellin matching calculation is only carried out on the local area, the redundant calculation is greatly reduced, and the local geometric fine correction speed of the high-resolution remote sensing image is effectively improved.
According to the method, through a large number of experiments, a series of parameters and threshold values suitable for geometric fine correction of the high-resolution remote sensing image in the local region are provided for the Fourier Mellin matching algorithm, and a result which is more stable and accurate than a classical SIFT and SURF matching algorithm can be obtained under the condition that ground object change, radiation and color difference exist between the local image to be processed and a reference base map.
According to the RPC coefficient of the original whole image and the latitude and longitude range of the region of interest of the user, the image with rough geographic coordinates in the local region is generated by indirect resampling, and only the image in the local region is calculated, so that the efficiency is improved, and the consumption of computing resources is reduced.
In the method, the rough coordinate offset between the local image to be processed and the reference base map is estimated by using a pHash algorithm in a sliding window mode, so that the redundant calculation in the process of matching the homonymy points is greatly reduced.
The invention obtains the threshold value suitable for local geometric fine correction of the high-resolution remote sensing image, such as the size (W) of a sliding window, according to a large amount of experiments Loc 、H Loc ) Distance of each sliding (0.1W) Loc 、0.1H Loc ) The local image is first down-sampled to 100 x 100 pixels and then DCT transformed, and the size of the upper left corner area (10 x 10 pixels) of the DCT transformed result is cut.
The method of the invention performs homonymy point matching on the local image to be processed and the reference base map in a blocking mode by utilizing Fourier Mellin transform, and can obtain stable and accurate matching effect under the conditions of ground feature difference, illumination and color difference and the like between the images.
Estimated from perceptual hashing algorithmsFinding out the rough coordinate offset between the image to be processed and the reference base map In Each block of (2) in a reference picture
And calculating Fourier Mellin transform of the corresponding region. According to a large number of experiments, the threshold suitable for the high-resolution remote sensing image is obtained, for example, in Fourier Mellin transform, the threshold of a scale coefficient of a first phase correlation operation is set to be not more than 1.7, the response threshold of a second phase correlation operation is set to be not less than 0.05, for the same-name points obtained through matching, the first 16 matching point pairs are taken as a final matching result according to the sequence of response values from large to small, and if the number of the same-name points is less than 8, matching is considered to be failed, and geometric fine correction is finished.
Drawings
FIG. 1 is a flow chart of a local geometric refinement method for high resolution remote sensing images according to the present invention;
FIG. 2 is a sliding window type base image
A schematic diagram;
FIG. 3 is the original whole remote sensing image I In And the image I of the local area Loc Comparing the images;
FIG. 4 is I Loc Performing DCT transformation on the result image effect graph;
FIG. 5 is a schematic representation of a pHash fingerprint;
FIG. 6 is a schematic diagram of Fourier Mellin transform and matching homonyms for block division;
FIGS. 7 (7 a) and (7 b) are graphs showing the comparison effect between the original whole image and the reference base image;
fig. 8 (8 a) and (8 b) are graphs showing the effect of comparing the local area image after the geometric fine correction with the reference base map.
Detailed Description
The embodiment is described with reference to fig. 1 to 8, and is applied to a local geometric fine correction method for a high-resolution remote sensing image, and the method specifically includes the following steps:
(1) Let the whole image to be processed be I In According to I In RPC coefficient (radial polymeric coeffients) of (1), calculate I In The maximum value and the minimum value range of the longitude and latitude coordinates, the maximum value of the abscissa is
Minimum value of
Maximum value of ordinate of
Minimum value of
(2) Setting the maximum value of longitude, latitude and abscissa of a local area in which a user is interested as
Minimum value of
Maximum value of ordinate
Minimum value of
If the resolution of the whole image to be processed is r, the image I of the local area in which the user is interested Loc Width W of Loc High H Loc Respectively as follows:
where the ceil function represents rounding up, W Loc 、H Loc The unit is a pixel;
(3) Indirect resampling to generate a bureau with coarse geographical coordinatesAn image of the partial region. Traverse I Loc The pixel coordinates of the current pixel are (i, j) (counting from 1), and the latitude and longitude X of the center of the pixel can be calculated by combining the resolution r and the latitude and longitude range of the local area in the step (2) Loc (i,j)、Y Loc (i, j) as shown in formula (2):
then according to the longitude and latitude, utilizing RPC coefficient to back-calculate it in I In The coordinates of the corresponding image points can be calculated to obtain I through conventional bilinear interpolation Loc The pixel value of the current pixel. To I Loc All pixels are subjected to the above operation, thereby obtaining I Loc Pixel values of all pixels;
(4) Calculating a reference base map I required by the local area according to the latitude and longitude range of the local area and the RPC positioning error Ref The maximum value of the abscissa of (1) is set to
Minimum value of
Maximum value of ordinate of
Minimum value of
The reference base image pixel values within this range are read. To I Ref Taking a sliding window, and sliding in the east-west direction each time for a distance of 0.1W Loc The distance of the north-south sliding is 0.1H Loc Size of sliding window and I Loc The same (as shown in fig. 1), so that the maximum value of the abscissa of the sliding window is
Minimum value of
(wherein i represents the number of sliding from west to east), and the maximum value of the ordinate is
Minimum value of
(where j represents the number of slides from south to north). Obtaining a reference base map in the sliding window, and recording the reference base map as
(5) Computing I by adopting Perceptual Hash (pHash) algorithm Loc And
the specific implementation of the respective pHash fingerprints (fingerprints) is as follows. Will I Loc And
down-sampling to 100 × 100 pixels, and if the image includes multiple bands, converting the image into a Gray image (wherein Gray represents a Gray image, and R, G, and B represent red, green, and blue bands in sequence) according to formula (3) in order to reduce the amount of calculation;
Gray＝0.299R+0.587G+0.114B (3)
for down-sampled and grayed I Loc And
the image is transformed into the frequency domain by Discrete Cosine Transform (DCT). Original whole remote sensing image I In And the image I of the intercepted local area Loc As shown in FIG. 2, I Loc The result after DCT transformation is shown in fig. 3;
since most of the energy of the image is concentrated in the low frequency part, the upper left 10 × 10 pixel region of the image after DCT transformation is truncated, which contains the low frequency part of the imageAnd (4) information. The average value of the pixel values is calculated for the 10 × 10 pixel area at the upper left corner, the area is binarized by using the average value as a threshold, if the average value is larger than the average value and is 1, and if the average value is smaller than the average value and is 0, the pHash fingerprint with 100 bits can be obtained, as shown in fig. 4 (white represents 1, and black represents 0). I.C. A Loc And
the Hash fingerprints describe respective image low-frequency information, and binaryzation is carried out by taking the mean value as a threshold value, so that the influence caused by illumination and color difference can be eliminated;
(6) Statistics I Loc And all of
The Hamming Distance (Hamming Distance) between the pHash fingerprints is selected to increase speed through bit operations. Taking the minimum Hamming distance
East and west are respectively expanded outwards by 0.1W Loc The north and south are respectively enlarged by 0.1H Loc In this range, the reference base diagram is taken out and marked
The purpose of the truncated portion with reference to the base map is to reduce memory overhead.
Is noted as the center point geographic coordinate
I Loc Is noted as the center point geographic coordinate
(7) In a blocking mode, fourier Mellin transform pair I is utilized Loc And with
The method can obtain stable and accurate matching effect under the conditions of ground feature difference, illumination and color difference and the like between images, and the aim of partitioning is to reduce memory overhead, enable programs to be parallel and improve the running speed. The method comprises the following concrete steps:
will I Loc Down-sampling to
The same resolution is used for improving the accuracy of the matching result. The resampled I Loc Dividing the block into 6 x 6 blocks, and recording the geographic coordinates of the center point of each block as
Wherein m =1,2, \8230, 6,k =1,2, \82306;
will I Loc 、
The difference between the geographical coordinates of the central point is regarded as the approximate coordinate offset between the two images, and the calculation can be carried out
Correspond to in
Geographical coordinates of the above possible homologous points
The formula is as follows:
to be provided with
Centered on the same size as the single block in step five one
An image is captured as shown in fig. 5.
To pair
And with
And performing Fourier Mellin transform on the two corresponding images. In the method, when the phase correlation operation is performed for the first time and the rotation angle and the scale coefficient are calculated, the threshold value of the scale coefficient is set to be 1.7, and if the threshold value is larger than the threshold value, the matching is considered to be failed; in the second phase correlation operation, when the coordinate translation amount is calculated, the response threshold value is set to be 0.05, and if the response threshold value is smaller than the threshold value, the matching is considered to be failed;
obtaining the homonym obtained by matching, i.e.
Is correspondingly at
Point of same name on
If the number of the same-name point pairs is less than 8, the matching is considered to be failed, and the geometric fine correction is finished; if the number of the same-name point pairs exceeds 16, sorting the same-name point pairs from large to small according to response values, and taking the first 16 matching point pairs as final matching results;
(8) And solving parameters of a second-order polynomial according to the homonymy point pairs, wherein the formula is as follows:
then utilizing conventional indirect method and bilinear interpolation to pair I Loc Resampling to realize geometric fine correction of the image, wherein the original image and the result of geometric fine correction are shown in fig. 6 and 7, and the color image represents the reference baseIn the figure, the grayscale image represents the image before and after the geometric correction. According to the processing result, the method can effectively carry out local geometric fine correction on the high-resolution remote sensing image.
All possible combinations of the technical features of the above embodiments may not be described for the sake of brevity, but should be considered as within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (7)
1. The local geometric precise correction method suitable for the high-resolution remote sensing image is characterized by comprising the following steps of: the correction method is realized by the following steps:
step one, selecting a whole image I to be processed In According to the whole image I In Calculating the RPC coefficient of the whole image I In The latitude and longitude range of (c); then setting a latitude and longitude range of a local area;
secondly, resampling by adopting an indirect method to generate a local area image L according to the latitude and longitude range of the local area in the step one Loc Go through the local area image I Loc All pixels of (2) to obtain I Loc Pixel values of all pixels;
step three, calculating a reference base map I of the local area according to the latitude and longitude range of the local area and the RPC coefficient in the step one Ref In said reference base I Ref Taking a sliding window upwards to obtain a reference base map in the sliding window
Step four, respectively calculating local area images I by adopting a perceptual hash algorithm Loc With reference to the base in the sliding window
The pHash fingerprint of (1), namely: the local area image I Loc With reference to the base in the sliding window
Carrying out DCT transformation after downsampling to 100 multiplied by 100 pixels, and intercepting the pHash fingerprint of a 10 multiplied by 10 area at the upper left corner of an image after DCT transformation;
step five, counting the local area image I Loc With reference to the base in all sliding windows
And from the calculated minimum reference base map
Obtaining a truncated reference base map
Sixthly, utilizing Fourier Mellin transform pair I in a blocking mode Loc And with
Match is made, then pair I Loc And (5) resampling, and realizing geometric fine correction of the image.
2. The method for local geometric fine correction of high-resolution remote-sensing images according to claim 1, characterized in that: in the first step, the maximum value of the latitude, longitude and abscissa of the local area which is interested by the user is set as
Minimum value of
Maximum value of ordinate
Minimum value of
Setting the whole image I to be processed In With a resolution r, the image I of the local area of interest to the user Loc Width W of Loc And height H Loc Respectively as follows:
in the formula, the ceil function represents rounding up.
3. The method for local geometric fine correction of high-resolution remote-sensing images according to claim 1, characterized in that: in step two, I is obtained Loc The specific process of the pixel values of all the pixels is as follows:
setting the pixel coordinates of the current pixel as (i, j), and calculating the longitude and latitude and X of the center of the current pixel according to the resolution r of the whole image and the longitude and latitude horizontal and longitudinal coordinate minimum value of the local area in the step two Loc (i, j) and Y Loc (i, j) are the horizontal and vertical coordinates of the longitude and latitude of the current pixel center, respectively, and are expressed by the following formula:
then according to the longitude and latitude, using RPC coefficient to back-calculate it in I In The coordinates of the corresponding image points are calculated to obtain I Loc Pixel value of the current pixel, pair I Loc All the pixels are calculated to obtain I Loc Pixel values of all pixels.
4. The method for local geometric fine correction of high-resolution remote-sensing images according to claim 1, characterized in that: in step three, the reference base map I of the local area Ref Taking a sliding window, and sliding in the east-west direction each time by a distance of 0.1W Loc The distance of the north-south sliding is 0.1H Loc Size of sliding window and I Loc Same, so that the maximum value of the abscissa of the sliding window is
Minimum value of
Maximum value of ordinate of
Minimum value of
Obtaining a reference base map in a sliding window
Xsteps is the number of sliding from west to east, ysteps is the number of sliding from south to north.
5. The method for local geometric fine correction of high-resolution remote-sensing images according to claim 1, characterized in that: in the fourth step, if the image contains a plurality of wave bands, the image is converted into a gray image according to the formula (3);
Gray＝0.299R+0.587G+0.114B (3)
in the formula, gray represents Gray image, and R, G and B represent red, green and blue wave bands in sequence;
to the I after down sampling and gray level Loc And
performing DCT transformation to transform the image into a frequency domain; and intercepting a 10 × 10 pixel area at the upper left corner of the image after DCT transformation, calculating the mean value of pixel values of the 10 × 10 pixel area at the upper left corner, carrying out binarization on the area by taking the mean value as a threshold value, and if the mean value is larger than the mean value, marking as 1, and if the mean value is smaller than the mean value, marking as 0, obtaining the pHash fingerprint with 100 bits.
6. The method for fine local geometry correction of high resolution remote sensing images according to claim 1, wherein: in step five, the minimum reference base map with the minimum Hamming distance is calculated
And at the minimum reference base
Respectively expand by 0.1W Loc The north and south are respectively enlarged by 0.1H Loc The range is taken as a reference base diagram for cutting
The truncated reference base diagram
Is noted as the center point geographic coordinate
I Loc Is noted as the center point geographic coordinate
7. The method for local geometric fine correction of high-resolution remote-sensing images according to claim 1, characterized in that: the concrete implementation steps of the sixth step are as follows:
step (ii) of61. Will I Loc Down-sampling to
Same resolution, I after resampling Loc Dividing into 6 × 6 blocks, and recording the geographic coordinates of the center point of each block as
Wherein m =1,2, \8230, 6, k =1,2, \8230, 6;
step six and two, mixing I Loc 、
The difference between the geographic coordinates of the central point of (a) is calculated as a rough coordinate offset between the two images
Correspond to in
Geographic coordinates of the same point on
The formula is as follows:
step six and three, the
As a centre, of the same size as the single block in step six
Intercepting an image;
step six and four, the pair
And
fourier Mellin transform is carried out on the two corresponding images to obtain the homonymy point obtained by matching, namely
Is correspondingly at
Point of same name on
If the number of the same-name point pairs is less than 8, the matching is considered to be failed, and the geometric fine correction is finished; if the number of the same-name point pairs exceeds 16, sorting the same-name point pairs from large to small according to response values, and taking the first 16 matching point pairs as final matching results;
sixthly, solving parameters of a second-order polynomial according to the homonymy point pairs, wherein the formula is as follows:
in the formula, a 0 -a 5 ，b 0 -b 5 Are coefficients of a polynomial.
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