A method for correcting the establishment year of a rubber plantation based on neighborhood analysis

Abstract

The application discloses a rubber plantation establishment year correction method based on neighborhood analysis, which is based on the spatial continuity characteristics of the rubber plantation and optimizes the estimation of the establishment year through a two-stage correction strategy. In the first stage, the abnormal pixels are corrected through difference and standard deviation. In the second stage, the boundary and abnormal pixels are further corrected through neighborhood mode frequency and hole filling strategy. The method effectively solves the deviation problem of the rubber plantation establishment year estimation under the mixed pixel and complex landscape conditions, and improves the accuracy and reliability of the dynamic monitoring of the rubber plantation.

Description

Technical Field

[0001] This invention relates to the field of remote sensing image processing technology, and in particular to a method for correcting the establishment year of rubber plantations based on neighborhood analysis. Background Technology

[0002] Existing technologies for estimating the establishment year of rubber plantations using remote sensing face several key challenges. The primary issue is the accurate identification of plantation boundary areas. Due to the spatial resolution limitations of remote sensing imagery, mixed-pixel phenomena often occur at plantation edges, meaning a single pixel may simultaneously contain spectral information from both the plantation and other land cover types. This mixed-pixel effect not only affects the accurate delineation of boundaries but also leads to inaccurate estimations of the establishment year. Furthermore, different land use types in adjacent areas can interfere with the spectral characteristics of boundary pixels, while the shading effect caused by topographic relief further complicates boundary area identification.

[0003] Maintaining temporal continuity is another significant challenge facing existing technologies. Theoretically, the establishment year within the same rubber plantation should exhibit strong spatial continuity, but in practice, discontinuities in year estimation often occur. This discontinuity stems partly from the uneven quality of historical remote sensing imagery; images from certain historical periods may suffer from cloud cover, banding noise, and other quality issues, increasing the uncertainty of time-series analysis results. Simultaneously, the seasonal variations in rubber production also interfere with the determination of the establishment year, particularly during the transition between the growing and non-growing seasons, where this effect is more pronounced.

[0004] Existing correction methods have significant limitations in addressing the aforementioned problems. While commonly used simple statistical methods (such as mean filtering and median filtering) can reduce the impact of outliers to some extent, they often fail to effectively handle complex boundary conditions and anomalous regions. These methods typically consider only the statistical characteristics of pixel values, neglecting the importance of spatial continuity, which can lead to "patchwork" in the correction results. More importantly, these methods may over-smooth the process during correction, disrupting the true temporal variation characteristics of rubber plantation establishment years. Especially in areas where there are genuine year-to-year differences, erroneous correction can actually reduce the reliability of the results.

[0005] Therefore, this invention provides a method that can solve the problem of bias in estimating the establishment year of rubber plantations under mixed pixel and complex landscape conditions, and improve the accuracy and reliability of dynamic monitoring of rubber plantations. Summary of the Invention

[0006] In view of this, the present invention provides a method for correcting the establishment year of rubber plantations based on neighborhood analysis.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0008] A method for correcting the establishment year of rubber plantations based on neighborhood analysis, the method comprising a first-stage correction and a second-stage correction.

[0009] The first stage of correction includes the following steps:

[0010] Step 1: Determine the neighborhood:

[0011] Select a target cell within the rubber plantation, with its cell value index being EY. (i,j) ;

[0012] Obtain the 8-neighborhood of the target cell, which are 8 adjacent cells; the index coordinates are: (i-1,j-1), (i,j-1), (i+1,j-1); (i-1,j), (i+1,j); (i-1,j+1), (i,j+1), (i+1,j+1);

[0013] Step 2: Collect neighborhood values:

[0014] For each neighborhood index, check if it is within the matrix boundary. If the index value exceeds the row and column range of the matrix, skip the index.

[0015] Extract the neighboring cell values ​​that meet the criteria and put them into a list;

[0016] Step 3: Calculate the mode and standard deviation of the non-masked neighborhood pixels;

[0017] Step 4: Calculate the absolute difference between the center pixel and the mode;

[0018] Step 5: Set the difference threshold T1 and the standard deviation threshold T2;

[0019] Step 6: Calibration test, check if |EY is satisfied simultaneously. (i,j) -EY mode_8NB |≤T1 and EY STDDEV_8NB ≤T2;

[0020] Step 7: Simultaneously satisfy |EY (i,j) -EY mode_8NB |≤T1 and EY STDDEV_8NB The establishment year of the center cell ≤T2 is updated to the neighborhood modulo value;

[0021] Step 8: This process is performed twice to ensure adequate correction.

[0022] The second stage of correction includes the following steps:

[0023] Step a: Reacquire the 8-neighborhood pixel values ​​for each plantation pixel;

[0024] Step b: Calculate the frequency of the mode in the 8 neighboring pixels; when the frequency of the mode exceeds 50%, update the establishment year of the center pixel to the neighborhood mode value.

[0025] Step c: For isolated non-rubber plantation pixels within the rubber plantation area, if more than N pixels in its 8-neighborhood are classified as rubber plantations, fill the pixel as a rubber plantation and assign it the corresponding establishment year value.

[0026] Preferably, in step 3, the method for calculating the mode is as follows: count the frequency of each value in the list, find the value with the highest frequency, which is the mode of that neighborhood; if multiple values ​​have the same frequency, select any one of them as the mode; the formula for calculating the standard deviation is:

[0027]

[0028] Where: N is the number of unmasked pixels in the neighborhood, with a maximum value of 8; X K is the value of each cell; μ is the mean of the neighborhood values.

[0029] Preferably, in step 4, the method for calculating the absolute difference between the center pixel and the mode is as follows: subtract the neighborhood mode from the center pixel value and take the absolute value. The formula is as follows:

[0030] Absolute Difference = |EY (i,j) -EY mode_8NB │

[0031] Among them, EY (i,j) It is the center cell at row and column position i,j, EY Mode_8NB It is the mode of the neighboring pixels of that pixel.

[0032] Preferably, in step 6, correction is performed when the absolute difference between the center pixel and the mode is less than the threshold T1 and the neighborhood standard deviation is less than the threshold T2.

[0033] Preferably, in step b, the method for calculating the frequency of the mode is as follows: Where N is the number of unmasked pixels, with a maximum value of 8.

[0034] Preferably, the window size used for the first stage correction and the second stage correction is 3×3 pixels.

[0035] Preferably, the difference threshold T1 and standard deviation threshold T2 for the first stage correction are both set to 2.

[0036] Preferably, the mode frequency threshold for the second stage correction is set to 50%.

[0037] The present invention achieves the following technical effects compared to the prior art:

[0038] The method of this invention effectively solves the problem of deviation in estimating the establishment year of rubber plantations under mixed pixel and complex landscape conditions, and improves the accuracy and reliability of dynamic monitoring of rubber plantations. Attached Figure Description

[0039] Figure 1 This is a rubber plantation establishment year correction map based on neighborhood analysis, which is a method for correcting the establishment year of a rubber plantation according to the present invention.

[0040] Figure 2 This is a schematic diagram of the estimation of the plantation establishment year based on neighborhood information in a method for correcting the establishment year of a rubber plantation based on neighborhood analysis according to the present invention; a) the original output of the rubber plantation establishment year, b) the result after correction based on numerical difference and standard deviation, c) the final result after correction using the mode percentage (>50%).

[0041] Figure 3 This is a scatter plot showing the accuracy evaluation of the observed planting years of random sampling points and the estimated planting years of the present invention for a rubber plantation year correction method based on neighborhood analysis. Detailed Implementation

[0042] 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. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0043] This invention discloses a method for correcting the establishment year of rubber plantations based on neighborhood analysis. The method includes a first-stage correction and a second-stage correction, wherein the window size used for the first-stage correction and the second-stage correction is 3×3 pixels.

[0044] The first stage of correction includes the following steps:

[0045] Step 1: Determine the neighborhood:

[0046] Select a target cell and represent it by its row and column indices, denoted as (i,j);

[0047] Obtain the 8-neighborhood of the target pixel, which consists of 8 adjacent pixels;

[0048] The neighborhood indices are: (i-1,j-1), (i,j-1), (i+1,j-1); (i-1,j), (i+1,j); (i-1,j+1), (i,j+1), (i+1,j+1);

[0049] Step 2: Collect neighborhood values:

[0050] For each neighborhood index, check if it is within the matrix boundary; if it is outside the matrix's row and column range, skip that index.

[0051] Extract the neighboring cell values ​​that meet the criteria and put them into a list;

[0052] Step 3: Calculate the mode and standard deviation of the neighboring pixels;

[0053] The method for calculating the mode is as follows: count the frequency of each value in the list, find the value with the highest frequency, which is the mode of that neighborhood; if multiple values ​​have the same frequency, select any one of them as the mode.

[0054] The formula for calculating the standard deviation is:

[0055]

[0056] Where: N is the number of unmasked pixels in the neighborhood, with a maximum value of 8; X K is the value of each cell; μ is the mean of the neighborhood values.

[0057] Step 4: Calculate the absolute difference between the center pixel and the mode;

[0058] The method for calculating the absolute difference between the center pixel value and the mode is as follows: subtract the neighborhood mode from the center pixel value and take the absolute value. The formula is as follows:

[0059] AbsoluteDifference = |EY (i,j) -EY mode_8NB │

[0060] Among them: EY (i,j) It is the center cell at row and column position i,j, EY Mode_8NB It is the mode of the neighboring pixels of that pixel;

[0061] Step 5: Set the difference threshold T1 and the standard deviation threshold T2;

[0062] Step 6: Perform calibration;

[0063] Correction is performed when the absolute difference between the center pixel and the mode is less than the threshold T1 and the neighborhood standard deviation is less than the threshold T2; the difference threshold T1 and the standard deviation threshold T2 for the first stage of correction are both set to 2.

[0064] Step 7: Update the establishment year of the central cell that meets the conditions to the neighborhood modulo value;

[0065] Step 8: This process is performed twice to ensure adequate correction.

[0066] The second stage of correction includes the following steps:

[0067] Step a: Reacquire the 8-neighborhood pixel values ​​for each plantation pixel;

[0068] Step b: Calculate the frequency of the mode in the 8-neighborhood pixels; when the frequency of the mode exceeds 50%, (e.g.) Figure 1 Using neighborhood information to correct the establishment year of the rubber plantation (EY), as shown in the figure, there are 18 cases (M / N represents the mode percentage, N / A represents the non-rubber pixels that have been masked), and the establishment year of the center pixel is updated to the neighborhood mode value; the mode frequency threshold for the second stage correction is set to 50%.

[0069] The method for calculating the frequency of the mode is as follows: Where N is the number of unmasked pixels, with a maximum value of 8.

[0070] Step c: For isolated non-rubber plantation pixels within the rubber plantation area, if more than N pixels in its 8-neighborhood are classified as rubber plantations, fill the pixel as a rubber plantation and assign it the corresponding establishment year value.

[0071] Among them, considering the presence of masks in surrounding pixels, there are a total of 18 scenarios, such as... Figure 1 As shown.

[0072] Example 1:

[0073] This embodiment uses a rubber plantation in Vietnam as an example. Figure 2 As shown, the estimation results of the rubber plantation establishment year are gradually improved through the neighborhood correction method proposed in this invention. Figure 2 Figure a shows the initial output of the remote sensing monitoring algorithm for rubber plantation establishment time, where different colors represent different establishment years. It can be seen that there is significant variability in year estimation within individual plantation blocks, which is inconsistent with the reality that plantations are typically planted simultaneously.

[0074] After applying the first-stage correction, such as Figure 2 As shown in b, the correction method based on numerical differences and standard deviations significantly reduces annual variability within the plantation, resulting in a more uniform color distribution within the blocks. However, some anomalies still exist at the plantation edges where different establishment years intersect.

[0075] After the second phase of correction, as follows Figure 2As shown in c, the estimation results were further optimized after applying the mode percentage correction method. The plantation blocks exhibited more coherent and clear characteristics, and the boundaries between areas established in different years were more defined. This stepwise optimization process effectively reduced the "salt and pepper" effect that occurred in the initial estimation, making the results more consistent with the actual plantation establishment pattern.

[0076] As can be seen, this embodiment fully demonstrates the effectiveness of the present invention in practical applications. It can not only correct the year estimation bias within the plantation, but also effectively handle the mixed pixel problem in the boundary area, ultimately obtaining more accurate and reliable year estimation results.

[0077] This invention generates 100 random sample points on a classified rubber distribution map using the "ee.Image.sample" method in the GEE platform and obtains estimated values. Observed values ​​are obtained from historical images from Google Earth to visually interpret the establishment years of rubber plantations. A strong correlation exists between the estimated establishment years of Vietnamese rubber plantations and the observed establishment years, such as... Figure 3 As shown, most points cluster tightly along the 1:1 line, indicating good agreement between the estimates and observations. The linear fit (red line) almost overlaps with the 1:1 line (grey dashed line), further demonstrating the high accuracy of the estimates. Excluding the 5 points (5.1%) with significant differences, the linear fit for the remaining points shows an R² of 0.99 and an RMSE of 0.25. The estimates appear particularly accurate for years between 1990 and 2015, with most data points concentrated in these years. The slightly larger divergence for the earliest years (before 1990) and the most recent years (after 2015) is likely due to the smaller sample size in these ranges.

[0078] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the technical scope of the present invention. Therefore, any minor modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for correcting the establishment year of rubber plantations based on neighborhood analysis, characterized in that, The correction method includes a first-stage correction and a second-stage correction. The first stage of correction includes the following steps: Step 1: Select a target pixel within the rubber plantation, with a pixel value of EY. (i, j) ; Step 2: Extract the values ​​of the 8 neighboring pixels of the target pixel, with index coordinates as follows: (i−1, j−1), (i, j-1), (i+1, j-1); (i−1, j), (i+1, j); (i−1, j+1), (i, j+1), (i+1, j+1); Discard the masked pixels and put them into a list; Step 3: Calculate the mode and standard deviation of the non-masked neighborhood pixels; Step 4: Calculate the absolute difference between the target pixel and the mode; Step 5: Set the difference threshold T1 and the standard deviation threshold T2; Step 6: Calibration test, check if |EY is satisfied simultaneously. (i, j) -EY mode_8NB |≤T1 and EY STDDEV_8NB ≤T2; Step 7: Update the target cell values ​​that meet the conditions to the neighborhood modulo values; Step 8: This process is performed twice to ensure adequate correction. The second stage of correction includes the following steps: Step a: Reacquire the values ​​of the 8 neighboring pixels of each plantation pixel and discard the masked pixels; Step b: Calculate the frequency of the mode among the 8 neighboring pixels; when the frequency of the mode exceeds 50%, update the value of the center pixel to the value of the neighborhood mode. Step c: For isolated non-rubber plantation pixels within the rubber plantation area, if more than N pixels in its 8 neighborhoods are classified as rubber plantations, fill the pixel as a rubber plantation and assign it the corresponding establishment year value.

2. The method for correcting the establishment year of rubber plantations based on neighborhood analysis according to claim 1, characterized in that, In step 3, the method for calculating the mode is as follows: count the frequency of each value in the list, find the value with the highest frequency, which is the mode of that neighborhood; if multiple values ​​have the same frequency, select any one of them as the mode; the formula for calculating the standard deviation is: EY STDDEV_8NB = Where: N is the number of unmasked pixels in the neighborhood, with a maximum value of 8; X K is the value of each cell; μ is the mean of the neighborhood values.

3. The method for correcting the establishment year of rubber plantations based on neighborhood analysis according to claim 1, characterized in that, In step 4, the method for calculating the absolute difference between the center pixel and the mode is as follows: subtract the neighborhood mode from the center pixel value and take the absolute value, as shown in the following formula: Absolute Difference = │EY (i, j) -EY Mode_8NB │ Among them, EY (i, j) It is the center cell at row and column position i,j, EY Mode_8NB It is the mode of the neighboring pixels of that pixel.

4. The method for correcting the establishment year of rubber plantations based on neighborhood analysis according to claim 1, characterized in that, In step 6, correction is performed when the absolute difference between the center pixel and the mode is less than threshold T1 and the neighborhood standard deviation is less than threshold T2, i.e., testing whether |EY| is satisfied simultaneously. (i, j) -EY mode_8NB |≤T1 and EY STDDEV_8NB ≤T2.

5. The method for correcting the establishment year of rubber plantations based on neighborhood analysis according to claim 1, characterized in that, In step b, ×100%, where N is the number of unmasked pixels, with a maximum value of 8.

6. The method for correcting the establishment year of rubber plantations based on neighborhood analysis according to claim 1, characterized in that, The window size used for the first stage correction and the second stage correction is 3×3 pixels.

7. The method for correcting the establishment year of rubber plantations based on neighborhood analysis according to claim 1, characterized in that, The difference threshold T1 and standard deviation threshold T2 for the first stage correction are both set to 2.

8. The method for correcting the establishment year of rubber plantations based on neighborhood analysis according to claim 1, characterized in that, The mode frequency threshold for the second stage correction is set to 50%.

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