An automatic and rapid fire point position correction method based on image frequency domain information

Abstract

The present invention discloses an automatic and rapid fire point position correction method based on image frequency domain information, which belongs to the field of image processing. The present invention uses the GSHHG coastline and inland water body data provided by NOAA as geometric reference data, and uses the frequency domain information of a specific band of the Himawari image data to perform geometric correction on the position offset of the fire point. First, after completing the screening of available control points, the POC algorithm is used to calculate the offset of the window around the control point. Then, based on the distance, three control points are selected, and the average value of their offsets is used as the offset of the pixel to be corrected to correct the position of the fire point pixel. The present invention corrects the geometric position offset problem existing in the Himawari‑8 / 9 image, and effectively alleviates the problem of fire point position offset. The present invention effectively improves the accuracy of fire point detection in southwest China.

Description

Technical Field

[0001] To address the issue of positional offset in Himawari fire point detection results, this paper proposes an automatic and rapid fire point position correction algorithm based on image frequency domain information. First, the positional offset of each control point is calculated using the Point of Interest (POC) algorithm. For each fire point within the study area, the positional offset of the fire point is calculated by averaging the offsets of the three nearest control points, thereby correcting the fire point positional offset. Background Art

[0002] Existing geometric correction methods primarily include traditional correction algorithms, correction algorithms based on spatial information, and correction algorithms based on frequency domain information. Traditional manual geometric correction methods employ methods such as constructing line-of-sight equations and building lookup tables followed by polynomial fitting. These algorithms not only leave significant room for improvement in correction accuracy but also require manual selection of control points for matching, making automation difficult. Furthermore, due to the sensor's imaging principle, the Himawari-8 / 9 image data exhibit varying degrees of geometric distortion, resulting in a certain offset in the identified fire locations. Because southwestern my country lies at the edge of the Himawari full-disk imagery, geometric distortion is even more pronounced. The study area is a high-incidence fire zone. Due to complex and variable terrain and other natural factors, the offset in the location of fire pixels can pose significant challenges to subsequent firefighting efforts. Therefore, developing a fast, automatic correction algorithm for fire points in Himawari-8 / 9 data is crucial for fire fighting and prevention. Summary of the Invention

[0003] The present invention solves the problem of positional offset in Himawari fire point detection results.

[0004] The technical solution is an automatic rapid fire point position correction method based on image frequency domain information, which includes:

[0005] Step 1: Preprocess the coastline and inland water data. The two-dimensional Fourier transform formulas corresponding to the preprocessed data are as follows:

[0006]

[0007] Where F(u,v) and G(u,v) are the frequency domain images corresponding to the GSHHG reference water data in the Himawari image 6-band data domain obtained after periodic extension and discrete Fourier transform; f(x,y) represents the Himawari image 6-band data, N represents the image as an N×N square matrix, g(x,y) represents the GSHHG reference water data, (u,v) represents the index in the frequency domain, corresponding to the frequency components in the horizontal and vertical directions, (x,y) represents the position index in the spatial domain, ranging from 0 to N-1, and i represents the imaginary unit;

[0008] Step 2: To eliminate the noise in the Fourier transformed image, use the Hamming window function to filter it. The Hamming window is defined as follows:

[0009] w(x,y)=w(x)*w(y) (3)

[0010]

[0011] Where a0 is the proportional coefficient, and w(x,y) is the two-dimensional Hamming window function;

[0012] Step 3: After obtaining the filtered frequency domain image data, calculate its normalized cross-power spectrum function R(u,v) as shown in formula (6);

[0013]

[0014] in is the complex conjugate function of G(u,v). To obtain the offset information of the image, perform inverse Fourier transform on it, as shown in formula (7);

[0015]

[0016] Where r(x,y) is an impulse function that represents the offset between the Himawari image to be corrected and the GSHHG data in the selected window; window correction is performed based on the offset;

[0017] Step 4: Correct the fire point position.

[0018] Furthermore, the specific method of step 4 is:

[0019] Step 4.1: Manually select control points. Select inland lakes that can be identified at a spatial resolution of 2 km in band 6 of the Himawari image data as control points.

[0020] Step 4.2: Using the Himawari-8 / 9 image of the phase to be corrected as the source data, set a window of 81×81 around the control point. After the window is set and selected, perform cloud masking on it.

[0021] Finally, use the following formula to filter available control points:

[0022]

[0023] Where amount_cloud and amount_total are the number of cloud pixels in the window and the total number of pixels in the window respectively, and criteria is a fixed threshold;

[0024] After cloud mask screening, the offset of each control point is calculated using a window registration method based on the POC algorithm; if the row offset or column offset of a control point exceeds 5, the point will be removed from the set; all remaining available control points are recorded as the matrix OFF, which stores the position information and row and column offset information of each point.

[0025] Step 4.3: After obtaining the offset information matrix OFF, calculate the offset of each fire point pixel based on the distance information; first, define the distance between two points based on the longitude and latitude information as shown in formula (9);

[0026]

[0027] where lon fp with lon cp are the longitudes of the fire point pixel and the control point pixel, lat fp with lat cp are the latitudes of the fire point pixel and the control point pixel respectively; by calculating the distance between the fire point to be corrected and all the control points in the set OFF, find the three control points with the smallest distance, which are recorded as p1, p2 and p3 respectively; if the number of available control points is less than three, in order to avoid incorrect results, choose not to correct the fire point in the scene image;

[0028] Step 4.4: After obtaining the three nearest control points, use equations (10) and (11) to calculate the row offset and column offset of the fire point pixel at the correction position;

[0029]

[0030] Among them, loff and coff are the row offset and column offset of the fire point pixel to be corrected. i with coff i are the row offset and column offset of the i-th control point itself;

[0031] Step 4.4: The corrected latitude and longitude coordinates of the fire point pixel can be obtained through equations (12) and (13);

[0032] lat real =lat ori -0.02*loff (12)

[0033] lon real =lon ori +0.02*coff (13)

[0034] Among them, lat ori with lon ori are the original latitude and longitude of the fire point to be corrected, lat real with lon real are the latitude and longitude of the fire point to be corrected after correction.

[0035] Furthermore, the preprocessing method in step 1 is:

[0036] A water mask was applied to the Himawari image data. The water body was extracted by combining the vegetation index information with the strong absorption of water near the 2.3 μm band. The criteria are as follows:

[0037]

[0038] a6<0.05 and a4<0.15 and NDVI<0 (15)

[0039] Where a6 is the reflectance of band 6 of the Himawari-8 / 9 data, a3 and a4 are the reflectance of bands 3 and 4 of the Himawari-8 / 9 data, respectively. NDVI is the normalized difference vegetation index calculated based on the Himawari-8 / 9 image data. Pixels that meet formula (15) are marked as water pixels.

[0040] The water mask result is filled with binary values ​​of 0 and 1, and the binary image obtained is used as the final result after preprocessing and input into the POC algorithm for registration;

[0041] For the GSHHG data, a raster image with a spatial resolution of 2 km is obtained through preprocessing, and its water and non-water parts are filled with binary values ​​of 0 and 1.

[0042] Furthermore, in step 4.2, the criterion is a fixed threshold value, which is set to 0.3.

[0043] Furthermore, in step 2, a0=0.53836.

[0044] Beneficial effects of the present invention: Based on the frequency domain information of band 6 of the Himawari-8 / 9 image data, the present invention proposes an automatic geometric correction algorithm for correcting the position offset of the Himawari fire point detection results. First, the coastline and inland water body data are preprocessed, and the offsets of all selected control points are calculated based on the POC algorithm. For fire points in different locations, the distance between them and the control points is used as the selection criterion, and the position offset of the fire point is obtained by calculating the offset values ​​of the three nearest control points. In most cases, the algorithm can effectively realize the position correction of the Himawari fire point detection results and effectively improve the positioning accuracy of the fire point pixels. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 : Schematic diagram of the location and topography of the Southwest Study Area. The diagram marks the distribution location of the Southwest Study Area, the topography of the study area, and the topography of the study area.

[0046] Figure 2 Example images of the L1 and L2 layers of the GSHHG data. The spatial reference data used in this paper is the Global High-Resolution Geographic Database (GSHHG), a hierarchical, high-resolution global coastline dataset provided by the National Oceanic and Atmospheric Administration (NOAA). The L1 and L2 layers represent continental landmasses, oceanic islands (excluding Antarctica), and lakes, respectively.

[0047] Figure 3 POC algorithm flow chart. The POC algorithm is an image registration algorithm based on frequency domain information. Its core is the two-dimensional fast Fourier transform (FFT) of the image. The figure shows the specific process of the POC algorithm.

[0048] Figure 4 :An example of the POC algorithm running results. The results obtained by the POC algorithm are presented from multiple different perspectives.

[0049] Figure 5 : Schematic diagram of the control point distribution, especially the partial display of the inland water control points, with the base map being a high-resolution remote sensing image.

[0050] Figure 6 : Examples of window correction results for the Qinghai Lake region at different time phases. The top figure shows the window correction for the Qinghai Lake region on March 17, 2021, and the bottom figure shows the correction results on January 24, 2023. The figures show the degree of overlap between the Qinghai Lake region on the Himawari-8 / 9 imagery and the Qinghai Lake outline provided by the GSHHG inland water data before and after correction.

[0051] Figure 7: An example of the geometric correction results of the fire point location is shown, with the base map being a high-resolution remote sensing image. DETAILED DESCRIPTION

[0052] The present invention will be further described below with reference to the accompanying drawings.

[0053] 1: Window correction based on POC algorithm:

[0054] Flowchart as Figure 3 shown.

[0055] Before geometrically correcting the fire point position, the offset of each control point needs to be calculated using the POC algorithm. The data required are the 6-band data of the Himawari image and the GSHHG reference water data, denoted as f(x,y) and g(x,y) respectively. Before performing POC registration, both data are preprocessed. The Himawari image data is masked for water bodies. This method uses the strong absorption of water bodies near the 2.3μm band and combines it with vegetation index information to extract water bodies. The standards are as follows:

[0056]

[0057] a6<0.05 and a4<0.15 and NDVI<0 (17)

[0058] Where a6 is the reflectance of band 6 of the Himawari-8 / 9 data, and NDVI is the normalized difference vegetation index calculated based on the Himawari-8 / 9 image data. Pixels that satisfy equation (17) are marked as water pixels.

[0059] The resulting water mask was then filled with binary values ​​of 0 and 1. The resulting binary image served as the final result after preprocessing and was input into the POC algorithm for registration. For the GSHHG data, the preprocessed raster image with a spatial resolution of 2 km was obtained, and its water and non-water areas were also filled with binary values ​​of 0 and 1.

[0060] The two-dimensional Fourier transforms corresponding to the two types of preprocessed data are shown in equations (18) and (19), respectively.

[0061]

[0062] Where F(u,v) and G(u,v) are the frequency domain images obtained after periodic extension and discrete Fourier transform of the two images, respectively. The frequency domain images obtained by the transform contain some high-frequency noise. To eliminate this noise, the frequency domain information of the obtained image is filtered using the Hamming window function. The Hamming window is defined as follows: (20)-(22).

[0063] w(x,y)=w(x)*w(y) (20)

[0064]

[0065] The proportional coefficient a0 = 0.53836. w(x, y) is the two-dimensional Hamming window function. After obtaining the filtered frequency domain image data, its normalized cross-power spectrum function R(u, v) is calculated as shown in Equation (23).

[0066]

[0067] in is the complex conjugate function of G(u,v). To obtain the offset information of the image, perform inverse Fourier transform on it, as shown in Equation (24).

[0068]

[0069] Here, r(x, y) should ideally be an impulse function, which represents the offset between the Himawari image to be corrected and the GSHHG data in the selected window.

[0070] 2: Fire point position correction based on control points:

[0071] Based on the selected coastline control points, this paper adds some control points located inside the study area. Some inland lakes that can be identified at a spatial resolution of 2 km in the 6th band of the Himawari image data are selected as control points. Similarly, some coastline points that are close to the study area and less obscured by clouds are selected as supplements. The location information of the control points is shown in Table 1 and Figure 5 shown.

[0072] Table 1

[0073]

[0074]

[0075] Before correcting the offset of the fire point position, the control points in Table 1 need to be screened to obtain available control points.

[0076] First, the Himawari-8 / 9 image of the phase to be corrected is used as the source data, and a window of size 81×81 is set around all the control points in Table 1. The window set here is also the window used for matching when calculating the offset using the POC algorithm.

[0077] After completing the window setting and selection, it needs to be cloud masked. After completing the cloud masking process, use formula (25) to filter the available control points.

[0078]

[0079] where amount_cloud and amount_total are the number of cloud pixels in the window and the total number of pixels in the window, respectively. The left side of inequality (25) is the proportion of cloud pixels in the window. criteria is a fixed threshold that represents the acceptable degree of cloud contamination in the window around the control point. In this paper, this threshold is set to 0.3. All pixels that do not satisfy equation (25) are considered to be obscured by clouds and are removed from the set of available control points.

[0080] After filtering by the cloud mask, the offset of each control point is calculated using the window registration method based on the POC algorithm described in 1. If the row offset or column offset of a control point exceeds 5, the point is removed from the set. All remaining available control points are recorded as the matrix OFF, which stores the position information and row and column offset information of each point.

[0081] After obtaining the offset information matrix OFF, the offset of each fire pixel is calculated based on the distance information. First, the distance between two points is defined based on the longitude and latitude information as shown in formula (26).

[0082]

[0083] where lon fp with lon cp are the longitudes of the fire point pixel and the control point pixel, lat fp with lat cp The latitudes of the fire point pixel and the control point pixel, respectively. By calculating the distance between the fire point to be corrected and all control points in the set OFF, the three control points with the smallest distances are found and recorded as p1, p2, and p3. It is important to note that if there are fewer than three control points, the basic information used to calculate the offset is severely insufficient, making it impossible to effectively simulate the overall distortion trend of the image. To avoid erroneous results, the fire point in this image is not corrected.

[0084] After obtaining the three nearest control points, use equations (27) and (28) to calculate the row offset and column offset of the fire point pixel at the correction position.

[0085]

[0086] Where loff and coff are the row offset and column offset of the fire point pixel to be corrected. i with coff i are the row offset and column offset of the ith control point itself. Then, the corrected longitude and latitude coordinates of the fire point pixel can be obtained through equations (29) and (30).

[0087] lat real =lat ori -0.02*loff (29)

[0088] lon real =lon ori +0.02*coff (30)

[0089] Among them, lat ori with lon ori are the original latitude and longitude of the fire point to be corrected, lat real with lon real are the latitude and longitude of the fire point to be corrected after correction.

Claims

1. A method for automatic rapid fire point position correction based on image frequency domain information, the method comprising: Step 1: Preprocess the coastline and inland water data. The two-dimensional Fourier transform formulas corresponding to the preprocessed data are as follows: Where F(u,v) and G(u,v) are the frequency domain images corresponding to the GSHHG reference water data in the Himawari image 6-band data domain obtained after periodic extension and discrete Fourier transform; f(x,y) represents the Himawari image 6-band data, N represents the image as an N×N square matrix, g(x,y) represents the GSHHG reference water data, (u,v) represents the index in the frequency domain, corresponding to the frequency components in the horizontal and vertical directions, (x,y) represents the position index in the spatial domain, ranging from 0 to N-1, and i represents the imaginary unit; Step 2: To eliminate the noise in the Fourier transformed image, use the Hamming window function to filter it. The Hamming window is defined as follows: w(x,y)=w(x)*w(y) (3) Where a0 is the proportional coefficient, and w(x,y) is the two-dimensional Hamming window function; Step 3: After obtaining the filtered frequency domain image data, calculate its normalized cross-power spectrum function R(u,v) as shown in formula (6); in is the complex conjugate function of G(u,v). To obtain the offset information of the image, perform inverse Fourier transform on it, as shown in formula (7); Where r(x,y) is an impulse function that represents the offset between the Himawari image to be corrected and the GSHHG data in the selected window; window correction is performed based on the offset; Step 4: Correct the fire point position.

2. The automatic rapid fire point position correction method based on image frequency domain information according to claim 1, characterized in that: The specific method of step 4 is: Step 4.1: Manually select control points. Select inland lakes that can be identified at a spatial resolution of 2 km in band 6 of the Himawari image data as control points. Step 4.2: Using the Himawari-8 / 9 image of the phase to be corrected as the source data, set a window of 81×81 around the control point. After the window is set and selected, perform cloud masking on it. Finally, use the following formula to filter available control points: Where amount_cloud and amount_total are the number of cloud pixels in the window and the total number of pixels in the window respectively, and criteria is a fixed threshold; After cloud mask screening, the offset of each control point is calculated using a window registration method based on the POC algorithm. If the row offset or column offset of a control point exceeds 5, the point will be removed from the set. All remaining available control points are recorded as the matrix OFF, which stores the position information and row and column offset information of each point. Step 4.3: After obtaining the offset information matrix OFF, calculate the offset of each fire point pixel based on the distance information; first, define the distance between two points based on the longitude and latitude information as shown in formula (9); where lon fp with lon cp are the longitudes of the fire point pixel and the control point pixel, lat fp with lat cp are the latitudes of the fire point pixel and the control point pixel respectively; by calculating the distance between the fire point to be corrected and all the control points in the set OFF, find the three control points with the smallest distance, which are recorded as p1, p2 and p3 respectively; if the number of available control points is less than three, in order to avoid incorrect results, choose not to correct the fire point in the image; Step 4.4: After obtaining the three nearest control points, use equations (10) and (11) to calculate the row offset and column offset of the fire point pixel at the correction position; Among them, loff and coff are the row offset and column offset of the fire point pixel to be corrected. i with coff i are the row offset and column offset of the i-th control point itself; Step 4.4: The corrected latitude and longitude coordinates of the fire point pixel can be obtained through equations (12) and (13); years real =years ori -0.02*loff (12) lon real = happy ori +0.02*memory (13) Among them, lat ori with lon ori are the original latitude and longitude of the fire point to be corrected, lat real with lon real are the latitude and longitude of the fire point to be corrected after correction.

3. The automatic rapid fire point position correction method based on image frequency domain information according to claim 1, characterized in that: The preprocessing method in step 1 is: The Himawari image data was masked for water bodies. The water bodies were extracted by combining the vegetation index information with the strong absorption characteristics of water bodies near the 2.3 μm band. The standards are as follows: a6<0.05 and a4<0.15 and NDVI<0 (15) Where a6 is the reflectance of band 6 of the Himawari-8 / 9 data, a3 and a4 are the reflectance of bands 3 and 4 of the Himawari-8 / 9 data, respectively. NDVI is the normalized difference vegetation index calculated based on the Himawari-8 / 9 image data. Pixels that meet formula (15) are marked as water pixels. The water mask result is filled with binary values ​​of 0 and 1, and the binary image obtained is used as the final result after preprocessing and input into the POC algorithm for registration; For the GSHHG data, a raster image with a spatial resolution of 2 km is obtained through preprocessing, and its water and non-water parts are filled with binary values ​​of 0 and 1.

4. The method for automatic rapid fire point position correction based on image frequency domain information according to claim 1, characterized in that: In step 4.2, criterion is a fixed threshold value, which is set to 0.

3.

5. The automatic rapid fire point position correction method based on image frequency domain information according to claim 1, characterized in that: In step 2, a0=0.53836.

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