Signal photon point cloud data filtering method and system based on linear feature analysis, and electronic equipment

Abstract

The invention provides a signal photon point cloud data filtering method and system based on linear feature analysis and electronic equipment, and can be applied to the technical field of laser radar data processing. The method comprises the steps that original photon point cloud data are acquired, and the original photon point cloud data comprise a plurality of original photon points; the original photon points are screened in the vertical direction, first photon point data are obtained, the first photon point data comprise a plurality of first photon points, and the number of the first photon points is smaller than that of the original photon points; adjusting the distance between adjacent first photon points in the first photon point data, so that the distances between the plurality of first photon points are kept consistent; calculating the optimal density and the optimal distance corresponding to each photon point based on linear fitting; and comparing the optimal density and the optimal distance with a preset threshold value, and determining a signal photon point and a noise photon point in the first photon point data based on a comparison result.

Description

Technical Field

[0001] The present invention relates to the technical field of laser radar data processing, and more specifically, to a signal photon point cloud data filtering method, system and electronic equipment based on linear feature analysis. Background Art

[0002] Photon point cloud data provides important data support for the study of polar ice cap and sea ice changes, vegetation height, sea level changes and other fields. Due to its high spatial resolution and wide coverage, photon point cloud data has been widely used in surface change monitoring. However, due to the detection principle of photon counting lidar, the collected data contains a large number of noise photons, which have low density and random distribution, causing significant interference to the extraction and subsequent analysis of signal photons. Therefore, in order to improve the utilization efficiency and accuracy of photon point cloud data, denoising and classification of photon point cloud data has become an important topic of current research.

[0003] At present, the denoising and classification methods for terrestrial photon point cloud data have poor adaptability to complex terrain environments and sparse data scenarios, and cannot meet the accurate denoising and classification of sub-point cloud data in different application scenarios. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] The present invention provides a signal photon point cloud data filtering method, system and electronic equipment based on linear feature analysis, which are used to at least partially solve one of the above-mentioned technical problems.

[0006] (II) Technical solution

[0007] According to a first aspect of the present invention, there is provided a signal photon point cloud data filtering method based on linear feature analysis, comprising: acquiring original photon point cloud data, wherein the original photon point cloud data includes a plurality of original photon points; screening the original photon points in a vertical direction to obtain first photon point data, wherein the first photon point data includes a plurality of first photon points, and the number of first photon points is less than the number of original photon points; adjusting the distance between adjacent first photon points in the first photon point data so that the distance between the plurality of first photon points remains consistent; calculating the optimal density and optimal distance corresponding to each photon point based on linear fitting; comparing the optimal density and optimal distance with a preset threshold, and determining the signal photon points and noise photon points in the first photon point data based on the comparison result.

[0008] According to an embodiment of the present invention, the original photon points are screened in the vertical direction to obtain first photon point data, including: calculating the along-track distance of the original photon points based on the timestamp information of the original photon points, and normalizing the along-track distance of the original photon points; grouping multiple original photon points based on the timestamp information to obtain at least one group of light spots; performing Euclidean clustering on the photon points in each group of light spots to determine the photon points that need to be retained in the group of light spots, wherein the photon points that need to be retained are the first photon points.

[0009] According to an embodiment of the present invention, Euclidean clustering is performed on the photon points in each group of light spots to determine the photon points that need to be retained in the group of light spots, including: performing the following operations on each group of light spots: performing Euclidean clustering on the photon points in the group of light spots based on a distance threshold to obtain a clustered point set; calculating the median point in the point set; determining the photon point closest to the median point as the first photon point, wherein the first photon point data consists of the first photon point in each group of light spots.

[0010] According to an embodiment of the present invention, the distance between adjacent first photon points in the first photon point data is adjusted so that the distance between multiple first photon points remains consistent, including: determining the spot spacing information based on environmental information; adjusting the along-track distance of the first photon points according to the spot spacing information so that the distance between multiple first photon points remains consistent.

[0011] According to an embodiment of the present invention, the optimal density and optimal distance corresponding to each photon point are calculated based on linear fitting, including: performing the following operations on each first photon point: searching for a neighborhood point set of the target first photon point within a first radius by rotating the angle; determining a first straight line corresponding to the target first photon point in the neighborhood point set; and determining the optimal density of the target first photon point based on the first straight line.

[0012] According to an embodiment of the present invention, the optimal density and optimal distance corresponding to each photon point are calculated based on linear fitting, and also include: fitting the second straight line corresponding to the target first photon point based on a random sampling consensus algorithm; calculating the distance from the target first photon point to the second straight line, and the distance is the optimal distance of the target first photon point.

[0013] According to an embodiment of the present invention, the preset threshold includes a first threshold and a second threshold, the optimal density and the optimal distance are compared with the preset threshold, and the signal photon points and noise photon points in the first photon point data are determined based on the comparison result, including: performing the following operations on each first photon point: comparing the optimal density of the first photon point with the first threshold to obtain a first relationship; comparing the optimal distance of the first photon point with the second threshold to obtain a second relationship; judging whether the first photon point meets the preset conditions based on the first relationship and the second relationship; if the first photon point meets the preset conditions, determining that the first photon point is a signal photon point; if the first photon point does not meet the preset conditions, determining that the first photon point is a noise photon point.

[0014] According to an embodiment of the present invention, the preset conditions include: the first relationship is that the optimal density of the first photon point is greater than a first threshold and the second relationship is that the optimal distance of the first photon point is less than a second threshold.

[0015] According to a second aspect of the present invention, there is provided a signal photon point cloud data filtering system based on linear feature analysis, comprising: an acquisition module for acquiring original photon point cloud data, wherein the original photon point cloud data includes a plurality of original photon points; a first adjustment module for screening the original photon points in a vertical direction to obtain first photon point data, wherein the first photon point data includes a plurality of first photon points, and the number of first photon points is less than the number of original photon points; a second adjustment module for adjusting the distance between adjacent first photon points in the first photon point data so that the distance between the plurality of first photon points remains consistent; a calculation module for calculating the optimal density and optimal distance corresponding to each photon point based on linear fitting; a determination module for comparing the optimal density and optimal distance with a preset threshold, and determining the signal photon points and noise photon points in the first photon point data based on the comparison result.

[0016] A third aspect of the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0017] (III) Beneficial effects

[0018] The signal photon point cloud data filtering method, system and electronic device based on linear feature analysis provided by the present invention include at least the following beneficial effects:

[0019] The resolution of the photon point cloud data is optimized and adjusted in the vertical and horizontal directions respectively, so as to improve the accuracy and robustness of the signal photon point cloud data filtering method under complex terrain conditions, so as to achieve accurate extraction of signal photons. In addition, by calculating the linear features such as the optimal density and optimal distance of the photon points, the distribution law of the signal photons is accurately captured, the dependence on the fixed threshold is reduced, the adaptive ability of the filtering method is improved, and the accuracy of the signal photon extraction results is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0021] Figure 1 Schematically shows a flow chart of a signal photon point cloud data filtering method based on linear feature analysis according to an embodiment of the present invention;

[0022] Figure 2 A schematic diagram schematically shows a schematic diagram of screening original photon points in a vertical direction to obtain first photon point data according to an embodiment of the present invention;

[0023] Figure 3 A schematic diagram of linear feature calculation in an embodiment of the present invention is schematically shown;

[0024] Figure 4 The location distribution diagram of an example in an embodiment of the present invention is schematically shown;

[0025] Figure 5 The optimal distance graph calculated based on the data of Example 1 according to an embodiment of the present invention is schematically shown;

[0026] Figure 6 The optimal density map calculated based on the data of Example 1 according to an embodiment of the present invention is schematically shown;

[0027] Figure 7 The optimal distance graph calculated based on the data of Example 2 according to an embodiment of the present invention is schematically shown;

[0028] Figure 8 The optimal density map calculated based on the data of Example 2 according to the present invention is schematically shown;

[0029] Fig. 9 A diagram schematically shows the land photon identification result of Example 1 in an embodiment of the present invention;

[0030] Fig.10 A diagram schematically shows the land photon recognition result of Example 2 in an embodiment of the present invention;

[0031] Fig.11The consistency analysis between the signal photon filtering result of the data of Example 1 in the embodiment of the present invention and the GLO-30 DEM elevation data is schematically shown;

[0032] Fig.12 The consistency analysis between the signal photon filtering result of the data of Example 2 in the embodiment of the present invention and the GLO-30 DEM elevation data is schematically shown;

[0033] Fig.13 A structural block diagram of a signal photon point cloud data filtering system based on linear feature analysis according to an embodiment of the present invention is schematically shown;

[0034] Fig.14 A block diagram of an electronic device suitable for implementing a signal photon point cloud data filtering method based on linear feature analysis according to an embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0037] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or can communicate with each other; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0038] In the description of the present invention, it should be understood that the terms "longitudinal", "length", "circumferential", "front", "rear", "left", "right", "top", "bottom", "inside", "outside", etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the subsystem or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0039] Throughout the drawings, the same elements are represented by the same or similar reference numerals. Conventional structures or configurations will be omitted when they may cause confusion in the understanding of the present invention. The shapes, sizes, and positional relationships of the components in the drawings do not reflect the actual size, proportion, and actual positional relationship. In addition, any reference symbols between brackets should not be construed as limitations.

[0040] Similarly, in order to simplify the present invention and help understand one or more of the various disclosed aspects, in the above description of exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. The description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0041] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0042] An embodiment of the present invention provides a signal photon point cloud data filtering method based on linear feature analysis, including: obtaining original photon point cloud data, wherein the original photon point cloud data includes multiple original photon points; screening the original photon points in the vertical direction to obtain first photon point data, wherein the first photon point data includes multiple first photon points, and the number of first photon points is less than the number of original photon points; adjusting the distance between adjacent first photon points in the first photon point data so that the distance between multiple first photon points remains consistent; calculating the optimal density and optimal distance corresponding to each photon point based on linear fitting; comparing the optimal density and optimal distance with a preset threshold, and determining the signal photon points and noise photon points in the first photon point data based on the comparison result.

[0043] Figure 1 The flowchart of the signal photon point cloud data filtering method based on linear feature analysis according to an embodiment of the present invention is schematically shown.

[0044] like Figure 1 As shown, the signal photon point cloud data filtering method based on linear feature analysis of this embodiment includes operations S110 to S150.

[0045] In operation S110 , original photon point cloud data is acquired, wherein the original photon point cloud data includes a plurality of original photon points.

[0046] In operation S120, the original photon points are screened in a vertical direction to obtain first photon point data, wherein the first photon point data includes a plurality of first photon points, and the number of the first photon points is less than the number of the original photon points.

[0047] In some embodiments, when the satellite is in motion, the ATLAS instrument emits laser pulses with a wavelength of 532nm at a fixed frequency. The same pulse may generate several return photons with close vertical distances, thereby interfering with the calculation of the neighborhood features of the photon point cloud. To eliminate this interference, the present invention adjusts the resolution of the photon point cloud data in the vertical direction.

[0048] In a specific implementation process, operation S120 may further include operation S1201 to operation S1203, for example.

[0049] Figure 2 A schematic diagram of filtering original photon points in a vertical direction to obtain first photon point data according to an embodiment of the present invention is shown schematically. Figure 2 (a) is a schematic diagram of the original photon point cloud data. Figure 2 (b) is a schematic diagram of the first photon point data. Figure 2 Operation S1201 to Operation S1203 are introduced.

[0050] In operation S1201 , the along-track distance of the original photon point is calculated based on the time stamp information of the original photon point, and the along-track distance of the original photon point is normalized.

[0051] In some embodiments, the raw photon point cloud data may be represented as:

[0052]

[0053] Among them, t i is the timestamp of the i-th original photon point, y i is the elevation of the i-th original photon point.

[0054] Calculate the distance x along the track of the i-th original photon point i :

[0055]

[0056] Wherein, v is the satellite running speed. In the embodiment of the present invention, v is 7 km / s.

[0057] For the calculated distance along the track x i Perform normalization to make the minimum value 0. The distance along the track after normalization It can be expressed as:

[0058]

[0059] Planning the along-track distance of each original photon point helps to eliminate inconsistencies and noise in the data, improve the accuracy and reliability of the data, and provide a basis for subsequent grouping and clustering.

[0060] In operation S1202, a plurality of original photon points are grouped based on timestamp information to obtain at least one group of light spots.

[0061] In some embodiments, the original photon points returned by the same pulse are grouped into a group (e.g. Figure 2 As shown). In this case, the photon points returning from the same pulse satisfy the same time condition t i =t j , so the grouping rule can be expressed as:

[0062]

[0063] Among them, G j represents the jth group of light spots, T j are the timestamps of the same group of spots, {t1, t2,…,t j} is the set of timestamps of all original photon points.

[0064] In operation S1203, Euclidean clustering is performed on the photon points in each group of light spots to determine the photon points that need to be retained in the group of light spots, wherein the photon points that need to be retained are the first photon points.

[0065] In some embodiments, the following operations are performed on each group of light spots: Euclidean clustering is performed on the photon points in the group of light spots based on a distance threshold to obtain a clustered point set; the median point in the point set is calculated; the photon point closest to the median point is determined as the first photon point, and the first photon point data consists of the first photon point in each group of light spots.

[0066] Specifically, for each group of light spots G j , using the distance threshold d min Perform Euclidean clustering to obtain the clustered point set C j Among them, the distance threshold d min The value of can be determined according to the ratio of removing vertical redundant points as needed. In this invention, the distance threshold is set to d min =0.5m.

[0067] For the photon point G j Each point set in , calculate the median point of its two-dimensional coordinates ,in, , . Find the median distance point of the point set The nearest photon point p i :

[0068]

[0069] p i That is the first photon point, p i As the reserved points of the point set. The first photon point data is composed of the reserved points of each point set in each group of light spots.

[0070] In operation S130, the distances between adjacent first photon points in the first photon point data are adjusted so that the distances between the plurality of first photon points remain consistent.

[0071] In some embodiments, the horizontal resolution of the photon point cloud data collected by the satellite in different areas, at different times and with different beams is different. By adjusting the horizontal resolution, the photon point cloud data in different areas, at different times and with different beams can be kept consistent in horizontal resolution, thereby improving the consistency and comparability of the data, allowing the denoising algorithm to more accurately identify and remove noise and interference in the data, and reducing the impact of the horizontal resolution on the denoising algorithm.

[0072] In a specific implementation process, operation S130 may include, for example, operation S1301 to operation S1302.

[0073] In operation S1301, light spot spacing information is determined based on environmental information.

[0074] In some embodiments, the environmental information may include, for example, lighting conditions and satellite information. Taking the ICESat-2 satellite as an example, since the sampling rate of the satellite is 10 kHz and the theoretical ground spot spacing along the track is about 0.7 m, the spot spacing information d can be track The spot spacing d can also be set to 0.7m according to actual conditions (such as atmospheric conditions, surface reflectivity, satellite attitude, etc.). track For example, for daytime data, due to strong solar radiation, signal photons are often submerged in noise photons. In order to make the signal photons appear continuously and clearly, d track Set to a value less than 0.7m to increase the overlap between adjacent spots, thereby improving the detectability and continuity of the signal.

[0075] In operation S1302, the along-track distance of the first photon spots is adjusted according to the light spot spacing information, so that the distances between the plurality of first photon spots remain consistent.

[0076] In some embodiments, for the light spot G j , the adjusted distance along the track is: In addition, for data sets with particularly sparse signal photons, d can be further reduced. track , ensuring that the signal photons are still distributed continuously after adjustment. By adjusting the spot spacing, the photon data can have a higher resolution along the track.

[0077] In operation S140, an optimal density and an optimal distance corresponding to each photon point are calculated based on linear fitting.

[0078] In some embodiments, the optimal density density of each photon point after adjustment is calculated based on a linear fitting method. i and optimal distance dist i .

[0079] Figure 3 The schematic diagram of linear feature calculation in an embodiment of the present invention is schematically shown.

[0080] Among them, the optimal density of photon points is calculated i include:

[0081] Search the target first photon point p by rotating the angle i The neighborhood point set N within the first radius r i ; Determine the first photon point p in the neighborhood point set iThe corresponding first straight line L, where the first straight line L passes through point p i And the first straight line L satisfies the distance from the neighborhood point to the first straight line L within the threshold d thr The condition with the most points under the condition, the first straight line L can be expressed as:

[0082]

[0083] in, , is the rotation angle.

[0084] Calculate each point p j (x j ,y j ) to the first straight line L:

[0085]

[0086] If the distance d from the point to the first straight line L j ≤d thr , then the point is counted into the number of points of the current line.

[0087] After determining the first straight line L, the optimal density of the target first photon point is determined based on the first straight line, wherein the target first photon point p i The optimal density density i That is, the first straight line L is at the threshold d thr Points under conditions.

[0088] Among them, the optimal distance dist of the photon point is calculated i Includes: Fitting the target first photon point p based on the Random Sample Consensus (RANSAC) algorithm i The corresponding second straight line L ransac , where L ransac The first photon point p is the target i The neighborhood point set N i The best straight line in; calculate the target first photon point p i To the second straight line L ransac The distance is the optimal distance dist of the first photon point of the target i .

[0089] In operation S150, the optimal density and the optimal distance are compared with a preset threshold, and signal photon points and noise photon points in the first photon point data are determined based on the comparison result.

[0090] In some embodiments, the signal photon point in the first photon point data is identified based on a preset threshold. The preset threshold includes a first threshold densitythr and the second threshold dist thr .

[0091] Determining the signal photon point and the noise photon point in the first photon point data based on the comparison result includes: performing the following operations on each first photon point: comparing the optimal density of the first photon point with the first threshold value to obtain a first relationship; comparing the optimal distance of the first photon point with the second threshold value to obtain a second relationship; judging whether the first photon point meets the preset condition based on the first relationship and the second relationship; determining the first photon point as a signal photon point if the first photon point meets the preset condition; and determining the first photon point as a noise photon point if the first photon point does not meet the preset condition. The preset condition is that the first relationship is that the optimal density of the first photon point is greater than the first threshold value, and the second relationship is that the optimal distance of the first photon point is less than the second threshold value.

[0092] The expression of the identification condition of the signal photon is:

[0093]

[0094] Among them, signal i =1 means point p i is the signal photon, signal i =0 means point p i is a noise photon.

[0095] Figure 4 The following schematically shows a location distribution diagram of an example in an embodiment of the present invention. Figure 4 (a) is Example 1, Figure 4 (b) is Example 2.

[0096] The embodiment of the present invention further illustrates operations S110-S150 based on Example 1 and Example 2. Example 1 is photon point cloud data acquired at night, and Example 2 is photon point cloud data acquired during the day. Both Example 1 and Example 2 are in mountainous areas with complex terrain conditions, which can comprehensively evaluate the performance of the signal photon point cloud filtering method provided by the embodiment of the present invention in a diverse geographical environment.

[0097] In Example 1, the terrain is undulating and the noise ratio is high. This environment usually places higher requirements on the processing of photon point cloud data. Example 2 includes a flat area with relatively flat terrain and low noise ratio, so as to test the effect of the signal photon point cloud filtering method provided by the embodiment of the present invention under conditions of different data density and small terrain changes. By testing these two highly representative examples, the performance of the method of the present invention in processing data with different terrains and different noise conditions can be effectively demonstrated.

[0098] Figure 5The optimal distance graph calculated based on the data of Example 1 according to an embodiment of the present invention is schematically shown; Figure 6 The optimal density map calculated based on the data of Example 1 according to an embodiment of the present invention is schematically shown.

[0099] Figure 7 The optimal distance graph calculated based on the data of Example 2 according to an embodiment of the present invention is schematically shown; Figure 8 The optimal density map calculated based on the data of Example 2 according to the present invention is schematically shown.

[0100] based on Figure 5 , Figure 6 and Figure 7 , Figure 8 You can clearly see the signal photon distribution and the corresponding optimal density density i and optimal distance dist i , for instance 1 and instance 2, the first threshold density thr and the second threshold dist thr The values ​​are 20 and 0.5 respectively, that is, different instances use the same first threshold and second threshold.

[0101] For each point p in the first point cloud data i , calculate its signal flag signal based on the preset conditions i , generate signal photon point cloud , and the noisy photon point cloud .

[0102] like Figure 5 and Figure 7 As shown, the optimal distance distribution results of the photon point cloud in Example 1 and Example 2 are shown. The results are calculated by the linear feature analysis method of the present invention, reflecting the difference in distance characteristics between signal photons and noise photons.

[0103] like Figure 6 and Figure 8 As shown, the optimal density distribution results of the photon point cloud in Example 1 and Example 2 are shown. The results are calculated by the linear feature analysis method of the present invention, reflecting the difference in density characteristics between signal photons and noise photons.

[0104] Fig. 9 A diagram schematically shows the land photon identification result of Example 1 in an embodiment of the present invention; Fig.10 The diagram schematically shows the land photon identification result of Example 2 in the embodiment of the present invention.

[0105] like Fig. 9 and Fig.10 As shown, the identification results of land signal photons in Example 1 and Example 2 are shown. The gray dots in the figure represent the identified signal photons, and the black dots represent the noise photons.

[0106] Example 2 is daytime data, the number of noise photons increases significantly, and the distribution becomes more complex. This result is calculated by the linear characteristic analysis method of the present invention, which can clearly reflect the difference in distance and density characteristics between signal photons and noise photons, and provides basic data support for the accurate identification of signal photons.

[0107] Compared with Example 1, the noise photon density in Example 2 is lower and the signal photons are drowned by the noise photons. The present invention accurately extracts the high-density signal photon area through the linear feature analysis method, effectively overcoming the interference of noise photons. By combining the density and distance characteristics of the photon point cloud, the present invention can accurately distinguish signal photons (gray dots) from noise photons in different terrain environments and noise distribution conditions, proving the robustness and adaptability of the method in complex data environments.

[0108] Fig.11 The consistency analysis between the signal photon filtering result of the data of Example 1 in the embodiment of the present invention and the GLO-30 DEM elevation data is schematically shown; Fig.12 The consistency analysis between the signal photon filtering results of the Example 2 data in the embodiment of the present invention and the GLO-30 DEM elevation data is schematically shown.

[0109] like Fig.11 As shown in Example 1, the R of the fitting result between the elevation data extracted from the signal photon and the DEM data is 2 =0.984, and RMSE is 4.453 m, indicating that the extraction results have high consistency. Fig.12 As shown in Example 2, the signal photon extraction results are more consistent with the DEM data, R 2 =0.997, and the RMSE is 2.758 m, indicating that the extraction accuracy of this method is better in areas with relatively flat terrain. The consistency analysis results of the elevation data after filtering of the example data and the GLO-30 DEM elevation data show that the signal photon filtering method provided by the present invention can effectively extract signal photons under different terrain conditions and maintain a high degree of consistency with high-resolution DEM data.

[0110] The signal photon point cloud data filtering method based on linear feature analysis provided in an embodiment of the present invention significantly improves the accuracy and robustness of signal photon extraction in photon point cloud data processing by combining the signal photon filtering method based on linear feature analysis with vertical and horizontal resolution optimization adjustment and optimal density and optimal distance feature extraction technology.

[0111] On the one hand, the present invention makes full use of the linear distribution characteristics of the photon point cloud by introducing the optimal density and optimal distance features to achieve accurate distinction between signal photons and noise photons. In complex terrain and sparse data scenarios, the extraction accuracy of signal photons is significantly improved and the misjudgment rate is reduced. This feature is particularly suitable for areas with obvious linear features such as mountains and canyons, effectively improving the photon extraction accuracy.

[0112] On the other hand, by removing redundant points in the vertical direction and uniformly adjusting the horizontal resolution, the problem of unstable extraction effect of existing technologies in areas with large terrain changes is solved. Whether in mountainous areas with large undulations or in flat terrain, it shows strong robustness and adaptability, meeting the needs of high-precision signal photon extraction under diverse terrain conditions. In addition, the automatic recognition method based on linear features reduces the dependence on fixed parameters (such as density threshold and clustering radius), so that the algorithm does not need to frequently adjust parameters for different data scenarios, has higher versatility and operability, reduces dependence on fixed thresholds, and improves the flexibility of the method.

[0113] In addition, in response to the situation where the proportion of noise photons in the data collected during the day is relatively high, the signal photon point cloud data filtering method provided by the present invention effectively distinguishes signal photons from noise photons through linear feature analysis, significantly improving the photon extraction performance in high-noise environments. Even if the signal photons are submerged in the noise photons, the signal photons can be effectively identified, effectively optimizing the extraction effect in high-noise photon scenes.

[0114] Based on the above-mentioned signal photon point cloud data filtering method based on linear feature analysis, the present invention also provides a signal photon point cloud data filtering system based on linear feature analysis. Fig.13 The system is described in detail.

[0115] Fig.13 The structural block diagram of the signal photon point cloud data filtering system based on linear feature analysis according to an embodiment of the present invention is schematically shown.

[0116] like Fig.13 As shown, the signal photon point cloud data filtering system 1300 based on linear feature analysis of this embodiment includes an acquisition module 1310 , a first adjustment module 1320 , a second adjustment module 1330 , a calculation module 1340 and a determination module 1350 .

[0117] The acquisition module 1310 is used to acquire original photon point cloud data, wherein the original photon point cloud data includes a plurality of original photon points. In one embodiment, the acquisition module 1310 may be used to perform the operation S110 described above, which will not be described in detail herein.

[0118] The first adjustment module 1320 is used to screen the original photon points in the vertical direction to obtain first photon point data, wherein the first photon point data includes a plurality of first photon points, and the number of the first photon points is less than the number of the original photon points. In one embodiment, the first adjustment module 1320 can be used to perform the operation S120 described above, which will not be described in detail herein.

[0119] The second adjustment module 1330 is used to adjust the distance between adjacent first photon points in the first photon point data so that the distance between multiple first photon points remains consistent. In one embodiment, the second adjustment module 1330 can be used to perform the operation S130 described above, which will not be described in detail here.

[0120] The calculation module 1340 is used to calculate the optimal density and optimal distance corresponding to each photon point based on linear fitting. In one embodiment, the calculation module 1340 can be used to perform the operation S140 described above, which will not be described in detail here.

[0121] The determination module 1350 is used to compare the optimal density and the optimal distance with a preset threshold, and determine the signal photon points and the noise photon points in the first photon point data based on the comparison result. In one embodiment, the determination module 1350 can be used to perform the operation S150 described above, which will not be repeated here.

[0122] According to an embodiment of the present invention, any multiple modules among the acquisition module 1310, the first adjustment module 1320, the second adjustment module 1330, the calculation module 1340 and the determination module 1350 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present invention, at least one of the acquisition module 1310, the first adjustment module 1320, the second adjustment module 1330, the calculation module 1340 and the determination module 1350 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in a proper combination of any of them. Alternatively, at least one of the acquisition module 1310 , the first adjustment module 1320 , the second adjustment module 1330 , the calculation module 1340 , and the determination module 1350 may be at least partially implemented as a computer program module, and when the computer program module is executed, a corresponding function may be performed.

[0123] Fig.14A block diagram of an electronic device suitable for implementing a signal photon point cloud data filtering method based on linear feature analysis according to an embodiment of the present invention is schematically shown.

[0124] like Fig.14 As shown, the electronic device 1400 according to an embodiment of the present invention includes a processor 1401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1402 or a program loaded from a storage part 1408 to a random access memory (RAM) 1403. The processor 1401 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1401 may also include an onboard memory for caching purposes. The processor 1401 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0125] In RAM 1403, various programs and data required for the operation of electronic device 1400 are stored. Processor 1401, ROM 1402 and RAM 1403 are connected to each other via bus 1404. Processor 1401 performs various operations of the method flow according to the embodiment of the present invention by executing the program in ROM 1402 and / or RAM 1403. It should be noted that the program can also be stored in one or more memories other than ROM 1402 and RAM 1403. Processor 1401 can also perform various operations of the method flow according to the embodiment of the present invention by executing the program stored in one or more memories.

[0126] According to an embodiment of the present invention, the electronic device 1400 may further include an input / output (I / O) interface 1405, which is also connected to the bus 1404. The electronic device 1400 may further include one or more of the following components connected to the input / output (I / O) interface 1405: an input portion 1406 including a keyboard, a mouse, etc.; an output portion 1407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 1408 including a hard disk, etc.; and a communication portion 1409 including a network interface card such as a LAN card, a modem, etc. The communication portion 1409 performs communication processing via a network such as the Internet. A drive 1410 is also connected to the input / output (I / O) interface 1405 as needed. A removable medium 1411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1410 as needed, so that a computer program read therefrom is installed into the storage portion 1408 as needed.

[0127] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although each embodiment is described above, it does not mean that the measures in each embodiment cannot be used in combination advantageously. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all be included in the protection scope of the present invention.

Claims

1. A signal photon point cloud data filtering method based on linear feature analysis, characterized in that: The method comprises: Acquire original photon point cloud data, wherein the original photon point cloud data includes a plurality of original photon points; Screening the original photon points in a vertical direction to obtain first photon point data, wherein the first photon point data includes a plurality of first photon points, and the number of the first photon points is less than the number of the original photon points; Adjusting the distances between adjacent first photon points in the first photon point data so that the distances between the plurality of first photon points remain consistent; Calculate the optimal density and optimal distance corresponding to each photon point based on linear fitting; The optimal density and the optimal distance are compared with a preset threshold, and the signal photon points and the noise photon points in the first photon point data are determined based on the comparison result.

2. The signal photon point cloud data filtering method according to claim 1, characterized in that: The step of screening the original photon points in the vertical direction to obtain first photon point data includes: Calculating the along-track distance of the original photon point based on the timestamp information of the original photon point, and normalizing the along-track distance of the original photon point; Grouping the multiple original photon points based on the timestamp information to obtain at least one group of light spots; Euclidean clustering is performed on the photon points in each group of light spots to determine the photon points that need to be retained in the group of light spots, wherein the photon points that need to be retained are the first photon points.

3. The signal photon point cloud data filtering method according to claim 2, characterized in that: The performing Euclidean clustering on the photon points in each group of light spots to determine the photon points that need to be retained in the group of light spots includes: Perform the following operations for each set of spots: Performing Euclidean clustering on the photon points in the group of light spots based on the distance threshold to obtain a clustered point set; Calculate the median point in the point set; The photon point closest to the median point is determined as the first photon point, wherein the first photon point data is composed of the first photon point in each group of light spots.

4. The signal photon point cloud data filtering method according to claim 1, characterized in that: The step of adjusting the distance between adjacent first photon points in the first photon point data so that the distance between the plurality of first photon points remains consistent includes: Determine the spot spacing information based on the environmental information; The along-track distance of the first photon spots is adjusted according to the light spot spacing information, so that the distances between the multiple first photon spots remain consistent.

5. The signal photon point cloud data filtering method according to claim 1, characterized in that: The method of calculating the optimal density and the optimal distance corresponding to each photon point based on linear fitting includes: For each first photon point, perform the following operations: Searching for a neighborhood point set of the target first photon point within a first radius by rotating the angle; Determine a first straight line corresponding to the target first photon point in the neighborhood point set; An optimal density of the target first photon points is determined based on the first straight line.

6. The signal photon point cloud data filtering method according to claim 5, characterized in that: The method of calculating the optimal density and optimal distance corresponding to each photon point based on linear fitting also includes: Fitting a second straight line corresponding to the first photon point of the target based on a random sampling consensus algorithm; The distance from the target first photon point to the second straight line is calculated, and the distance is the optimal distance of the target first photon point.

7. The signal photon point cloud data filtering method according to claim 1, characterized in that: The preset threshold includes a first threshold and a second threshold, and comparing the optimal density and the optimal distance with the preset threshold, and determining the signal photon points and the noise photon points in the first photon point data based on the comparison result, comprises: For each first photon point, perform the following operations: comparing the optimal density of the first photon point with the first threshold to obtain a first relationship; Comparing the optimal distance of the first photon point with the second threshold to obtain a second relationship; Determining whether the first photon point meets a preset condition based on the first relationship and the second relationship; When the first photon spot meets a preset condition, determining the first photon spot as a signal photon spot; When the first photon point does not satisfy a preset condition, the first photon point is determined to be a noise photon point.

8. The signal photon point cloud data filtering method according to claim 7, characterized in that: The preset conditions include: The first relationship is that the optimal density of the first photon points is greater than the first threshold and the second relationship is that the optimal distance of the first photon points is less than the second threshold.

9. A signal photon point cloud data filtering system based on linear feature analysis, characterized in that: The system comprises: An acquisition module, used for acquiring original photon point cloud data, wherein the original photon point cloud data includes a plurality of original photon points; A first adjustment module is used to screen the original photon points in a vertical direction to obtain first photon point data, wherein the first photon point data includes a plurality of first photon points, and the number of the first photon points is less than the number of the original photon points; A second adjustment module, used for adjusting the distance between adjacent first photon points in the first photon point data, so that the distance between the plurality of first photon points remains the same; A calculation module, used for calculating the optimal density and optimal distance corresponding to each photon point based on linear fitting; A determination module is used to compare the optimal density and the optimal distance with a preset threshold, and determine the signal photon points and noise photon points in the first photon point data based on the comparison result.

10. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

Images