Data acquisition and processing method for long-range detection of hyperspectral lidar

Abstract

The present application relates to a data acquisition and processing method for long-distance detection of a hyperspectral laser radar, comprising: determining multiple reference channels and their noise thresholds from multiple channels, calculating a first comprehensive threshold-crossing segment timing interval based on the waveform data and noise threshold of each reference channel; when the reference channel intersection constraint is enabled to a first preset threshold and the number of hits is within a preset value interval, eliminating the comprehensive threshold-crossing segments that do not meet the preset conditions in the first comprehensive threshold-crossing segment timing interval to obtain a second comprehensive threshold-crossing segment timing interval; obtaining a final comprehensive threshold-crossing segment timing interval based on the reference channel intersection constraint, the preset length before and after the threshold-crossing, the first comprehensive threshold-crossing segment timing interval or the second comprehensive threshold-crossing segment timing interval. Thus, the problem that the simple threshold-crossing method in the background technology is not applicable to the ground observation scene of the hyperspectral laser radar under long-distance strong solar background light is solved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data acquisition and processing method for long-range detection using a hyperspectral laser radar. Background Art

[0002] As an active Earth observation technology, LiDAR (LiDAR) can directly and rapidly acquire three-dimensional spatial information of observed targets. It boasts 24 / 7 operation, high spatial resolution detection, and a short operating cycle. However, traditional single-wavelength LiDAR detection information only includes the geometric distance information of the observed target and lacks spectral information. Hyperspectral LiDAR, on the other hand, includes dozens of wavelengths and can acquire not only the spatial geometric distance information of the observed target but also rich spectral information of the observed target, achieving so-called "spatial-spectral integration." Compared with traditional detection technologies such as LiDAR and hyperspectral imaging, hyperspectral LiDAR technology overcomes the limitations of simultaneous acquisition of spatial and spectral information of ground objects, and holds broad prospects in areas such as land cover classification, marine resource exploration, vegetation remote sensing monitoring, and three-dimensional urban construction.

[0003] The general working principle of hyperspectral lidar is that a high-energy, wide-band laser emits laser pulses through a scanning unit to the observed target. The backscattered echo from the observed target returns to the system and is divided into dozens of bands by the spectroscopic unit. The data is then collected by dozens of acquisition channels in the acquisition chassis. The collected data is cached and processed on the acquisition board and then transmitted to the computer and finally stored on the hard disk. By performing a series of related processing and analysis on the final stored raw waveform data, the spatial geometry and spectral information of the observed target can be extracted from it.

[0004] Hyperspectral lidar (LIDAR) acquires echo data at a high rate and in large volumes. This is because hyperspectral LIDAR operations require high-density, rapid ground scanning, which necessitates detecting and collecting as many echoes from laser footsteps as possible within a given timeframe. Therefore, the echo data sampling rate must be sufficiently high. Furthermore, the acquisition of data from dozens of channels enables a total echo data sampling rate dozens of times higher than that of traditional single-wavelength LIDAR, reaching hundreds of GHz. Combined with the acquisition ADC's quantization bit rate of over a dozen bits, the total data rate can reach terabits per second. Such large amounts of data are typically limited by a noise threshold. This threshold is typically set slightly above the average noise level. In low-light environments, this noise level is generally very low. This method eliminates the noise component of the total echo fragment from each laser trigger, while retaining the relatively low-profile transmitted and received pulse echoes and a small amount of surrounding noise fragments.

[0005] However, in order to achieve long-distance ground detection throughout the day, especially when the solar background light is strong, the amplitude of the noise pulse can be comparable to the amplitude of the echo signal pulse; in this way, only setting the noise threshold value cannot effectively eliminate the noise part that accounts for the majority of the total echo segment. If it is not removed, it will affect the subsequent transmission and storage links, thus making it impossible to achieve long-distance detection of hyperspectral lidar. Summary of the Invention

[0006] The present application provides a data acquisition and processing method for long-distance detection by a hyperspectral lidar, so as to solve the problem that the simple over-threshold method is not applicable to the ground observation scene of the hyperspectral lidar under long-distance strong solar background light. By constraining the window, optimizing the reference channel and combining the waveform processing methods of each reference channel, the high-intensity noise pulses widely distributed in the echo waveform are further eliminated, thereby enabling the hyperspectral lidar to achieve long-distance detection under strong solar background light.

[0007] The first embodiment of the present application provides a data acquisition and processing method for long-range detection of a hyperspectral laser radar, comprising the following steps:

[0008] Acquire waveform data of multiple channels collected by the hyperspectral lidar under each laser pulse triggering, determine multiple reference channels from the multiple channels, and determine the noise threshold of each reference channel;

[0009] Calculating a first integrated threshold-crossing segment timing interval according to the waveform data of each reference channel and the noise threshold of each reference channel;

[0010] Determining, based on the multiple reference channels, a reference channel intersection constraint enable and a hit count, and when the reference channel intersection constraint enable is a first preset threshold and the hit count is within a preset value range, eliminating the comprehensive cross-threshold segments that do not meet the preset conditions in the first comprehensive cross-threshold segment timing interval to obtain a second comprehensive cross-threshold segment timing interval;

[0011] The final integrated threshold crossing segment timing interval is obtained according to the reference channel intersection constraint enable, the preset pre- and post-threshold length, the first integrated threshold crossing segment timing interval or the second integrated threshold crossing segment timing interval.

[0012] According to one embodiment of the present application, calculating the first integrated threshold crossing segment timing interval based on the waveform data of each reference channel and the noise threshold of each reference channel includes:

[0013] Calculate at least one cross-threshold segment in each reference channel signal in which waveform data is greater than a noise threshold of the corresponding reference channel signal, and a timing index and length of each cross-threshold segment;

[0014] Based on the length of the cross-threshold segment corresponding to each reference channel signal, removing the cross-threshold segment smaller than a preset constraint window in each reference channel signal to obtain the remaining cross-threshold segment of each reference channel signal;

[0015] The first integrated threshold-crossing segment timing interval is obtained according to the union of the remaining threshold-crossing segments of each reference channel signal.

[0016] According to one embodiment of the present application, obtaining the final integrated threshold crossing segment timing interval based on the reference channel intersection constraint enable, the preset pre- and post-threshold length, the first integrated threshold crossing segment timing interval, or the second integrated threshold crossing segment timing interval includes:

[0017] If the reference channel intersection constraint is enabled and is a first preset threshold, the final integrated threshold crossing segment timing interval is obtained according to the sum of the second integrated threshold crossing segment timing interval and the preset length before and after the threshold crossing;

[0018] If the reference channel intersection constraint is enabled to a second preset threshold, the final integrated threshold crossing segment timing interval is obtained according to the sum of the first integrated threshold crossing segment timing interval and the preset threshold crossing length.

[0019] According to one embodiment of the present application, after obtaining the final integrated threshold crossing segment timing interval, the method further includes:

[0020] The waveform data of the same timing interval in the multiple channels under each laser pulse triggering are transmitted and stored according to the final integrated threshold crossing segment timing interval.

[0021] According to one embodiment of the present application, determining multiple reference channels from the multiple channels includes:

[0022] According to the waveform data of each channel, channels in a preset band interval among the multiple channels are selected as the multiple reference channels.

[0023] According to the data acquisition and processing method for long-distance detection of a hyperspectral laser radar according to the embodiment of the present application, multiple reference channels and their noise thresholds are determined from multiple channels, and the first comprehensive threshold-crossing segment timing interval is calculated according to the waveform data and noise threshold of each reference channel; when the reference channel intersection constraint is enabled to the first preset threshold and the number of hits is in the preset value interval, the comprehensive threshold-crossing segments that do not meet the preset conditions in the first comprehensive threshold-crossing segment timing interval are eliminated to obtain the second comprehensive threshold-crossing segment timing interval; according to the reference channel intersection constraint, the preset threshold length before and after, the first comprehensive threshold-crossing segment timing interval or the second comprehensive threshold-crossing segment timing interval, the final comprehensive threshold-crossing segment timing interval is obtained. Thus, the problem that the simple threshold-crossing method in the background technology is not applicable to the ground observation scene of the hyperspectral laser radar under long-distance strong solar background light is solved. By constraining the window, optimizing the reference channel and combining the waveform processing methods of each reference channel, the high-intensity noise pulses widely distributed in the echo waveform are further eliminated, so that the hyperspectral laser radar can achieve long-distance detection under strong solar background light.

[0024] A second embodiment of the present application provides a data acquisition and processing device for long-range detection of a hyperspectral laser radar, comprising:

[0025] An acquisition module is used to acquire waveform data of multiple channels collected by the hyperspectral laser radar under each laser pulse triggering, and to determine multiple reference channels from the multiple channels, and to determine a noise threshold of each reference channel;

[0026] a calculation module, configured to calculate a first integrated threshold-crossing segment timing interval based on the waveform data of each reference channel and the noise threshold of each reference channel;

[0027] a processing module, configured to determine, based on the multiple reference channels, a reference channel intersection constraint enable and a hit count, and, when the reference channel intersection constraint enable is a first preset threshold and the hit count is within a preset value range, eliminate the comprehensive cross-threshold segments that do not meet the preset conditions in the first comprehensive cross-threshold segment timing interval to obtain a second comprehensive cross-threshold segment timing interval;

[0028] A determination module is used to obtain a final comprehensive threshold crossing segment timing interval based on the reference channel intersection constraint enable, the preset pre- and post-threshold length, the first comprehensive threshold crossing segment timing interval or the second comprehensive threshold crossing segment timing interval.

[0029] According to one embodiment of the present application, the computing module is configured to:

[0030] Calculate at least one cross-threshold segment in each reference channel signal in which waveform data is greater than a noise threshold of the corresponding reference channel signal, and a timing index and length of each cross-threshold segment;

[0031] Based on the length of the cross-threshold segment corresponding to each reference channel signal, removing the cross-threshold segment smaller than a preset constraint window in each reference channel signal to obtain the remaining cross-threshold segment of each reference channel signal;

[0032] The first integrated threshold-crossing segment timing interval is obtained according to the union of the remaining threshold-crossing segments of each reference channel signal.

[0033] According to one embodiment of the present application, the determining module is configured to:

[0034] If the reference channel intersection constraint is enabled and is a first preset threshold, the final integrated threshold crossing segment timing interval is obtained according to the sum of the second integrated threshold crossing segment timing interval and the preset length before and after the threshold crossing;

[0035] If the reference channel intersection constraint is enabled to a second preset threshold, the final integrated threshold crossing segment timing interval is obtained according to the sum of the first integrated threshold crossing segment timing interval and the preset threshold crossing length.

[0036] According to one embodiment of the present application, after obtaining the final integrated threshold-crossing segment timing interval, the determination module is further configured to:

[0037] The waveform data of the same timing interval in the multiple channels under each laser pulse triggering are transmitted and stored according to the final integrated threshold crossing segment timing interval.

[0038] According to one embodiment of the present application, the acquisition module is configured to:

[0039] According to the waveform data of each channel, channels in a preset band interval among the multiple channels are selected as the multiple reference channels.

[0040] According to the data acquisition and processing device for long-distance detection of a hyperspectral laser radar according to the embodiment of the present application, multiple reference channels and their noise thresholds are determined from multiple channels, and a first comprehensive threshold-crossing segment timing interval is calculated based on the waveform data and noise threshold of each reference channel; when the reference channel intersection constraint is enabled to be the first preset threshold and the number of hits is in the preset value interval, the comprehensive threshold-crossing segments that do not meet the preset conditions in the first comprehensive threshold-crossing segment timing interval are eliminated to obtain a second comprehensive threshold-crossing segment timing interval; according to the reference channel intersection constraint, the preset threshold length before and after, the first comprehensive threshold-crossing segment timing interval or the second comprehensive threshold-crossing segment timing interval, the final comprehensive threshold-crossing segment timing interval is obtained. Thus, the problem that the simple threshold-crossing method in the background technology is not applicable to the ground observation scene of the hyperspectral laser radar under long-distance strong solar background light is solved. By constraining the window, optimizing the reference channel and combining the waveform processing methods of each reference channel, the high-intensity noise pulses widely distributed in the echo waveform are further eliminated, so that the hyperspectral laser radar can achieve long-distance detection under strong solar background light.

[0041] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the data acquisition and processing method for long-range detection of a hyperspectral lidar as described in the above embodiment.

[0042] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the data acquisition and processing method for long-range detection of a hyperspectral lidar as described in the above embodiment.

[0043] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0045] Figure 1 This is a flowchart of waveform data acquisition, processing, transmission and storage for an airborne 56-channel hyperspectral lidar system according to one embodiment of the present application;

[0046] Figure 2 This is a flow chart of a data acquisition and processing method for long-range detection of a hyperspectral laser radar according to an embodiment of the present application;

[0047] Figure 3A two-dimensional graph of signal strength-time (sampling points) of waveform data of reference channel 5 according to one embodiment of the present application;

[0048] Figure 4 4-way reference channel waveform data signal strength-time (sampling point) diagram according to one embodiment of the present application;

[0049] Figure 5 This is a flow chart of a data acquisition and processing method for long-range detection by a hyperspectral lidar according to one embodiment of the present application;

[0050] Figure 6 Schematic diagram of a data acquisition and processing device for long-range detection of a hyperspectral laser radar according to an embodiment of the present application;

[0051] Figure 7 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0053] The following describes a data acquisition and processing method for long-range detection of a hyperspectral lidar according to an embodiment of the present application with reference to the accompanying drawings.

[0054] Before introducing the data acquisition and processing method for long-distance detection of a hyperspectral lidar according to an embodiment of the present application, the waveform data acquisition, processing, transmission and storage process of the hyperspectral lidar system according to an embodiment of the present application is first introduced.

[0055] like Figure 1 As shown in FIG, the waveform data acquisition, processing, transmission and storage process of the hyperspectral lidar system includes the following steps:

[0056] S101, the acquisition card ADC module acquires the original signal waveform.

[0057] S102, collecting waveform data cached in the memory on the card.

[0058] S103: The data processing unit of the acquisition card performs redundancy removal on the original waveform data.

[0059] S104, data is transmitted from the acquisition card backplane to the computer.

[0060] S105: The computer writes the data into the disk array for storage.

[0061] Specifically, taking the platform implemented in the embodiment of the present application as an airborne 56-channel hyperspectral lidar system as an example, the sampling frequency of the single-channel ADC (analog / digital converter) in the acquisition unit is 2 GHz, and the sampling bit number is 12 bits; the laser emission pulse repetition frequency is 130 kHz, and the pulse width is 5 ns. It can be seen that the pulse emission interval time is 7.69 microseconds. Due to the limited memory capacity on the acquisition card, it is only used for data caching, that is, the waveform data collected by the system under each laser triggering is cached in the memory on the acquisition card for no more than 7.69 microseconds. Taking the system's ground observation range of 600 meters as an example, the flight time used by the laser pulse from emission to return is obtained by the following formula:

[0062]

[0063] Among them, flytime is the flight time of the laser pulse from emission to return, R is the system's ground observation range, and v is the speed of light.

[0064] To meet the memory cache time requirements on the acquisition card, the data transmission rate of the acquisition card backplane is obtained by the following formula: datarate = 56 × 2Gps × 12bit / sampling × 130kHz × flytime = 698.88Gbps = 87.36GBps

[0065] This rate far exceeds the actual transmission rate limit of 24GBps of the system acquisition card backplane and the computer hard disk write speed limit of 6.5GBps. Therefore, a large amount of redundant and invalid data must be removed before data transmission.

[0066] The following introduces a method for de-redundancy of massive data collection based on a hyperspectral lidar system, which is based on the temporal correlation of waveforms between hyperspectral lidar channels and takes into account the noise pulse characteristics. It is a data collection and processing method for long-distance detection of hyperspectral lidar.

[0067] Specifically, Figure 2 A flowchart of a data acquisition and processing method for long-range detection using a hyperspectral lidar is provided in an embodiment of the present application.

[0068] In this embodiment, the method provided by the present invention can use the FPGA (Field Programmable Array Logic) on the acquisition card to write the logic implementation process.

[0069] like Figure 2 As shown, the data acquisition and processing method for long-range detection of hyperspectral laser radar includes the following steps:

[0070] In step S201, waveform data of multiple channels collected by the hyperspectral laser radar under each laser pulse triggering is obtained, multiple reference channels are determined from the multiple channels, and a noise threshold of each reference channel is determined.

[0071] Optionally, this embodiment takes an airborne 56-channel hyperspectral laser radar as an example to specifically illustrate the present invention.

[0072] Specifically, the waveform data of each channel consists of approximately 8,000 discrete points. Due to the differences in laser emission energy in each band, the time delay errors between channels, and the different responses of the same observation target to each band, the waveforms of the 56 channels are not exactly the same. There are differences in the timing and shape of the echoes of the observation targets. In some weak echo channels, the echo signal strength is even too low, causing the echo to be submerged in the noise.

[0073] Furthermore, in some embodiments, determining multiple reference channels from multiple channels includes: selecting channels within a preset band interval from the multiple channels as the multiple reference channels based on waveform data of each channel.

[0074] Specifically, the embodiment of the present application selects 4 reference channels from 56 acquisition channels, wherein the 4 reference channels are selected as channels near the 700nm band. The selection is based on the fact that the bands corresponding to these 4 reference channels have high emission energy and are not in the absorption spectrum of plants, and can maintain high echo signal intensity for conventional observation targets. In this embodiment, the reference channels are selected as channels 5, 6, 7, and 8, such as Figure 3 and Figure 4 As shown, Figure 3 This is the signal strength-time (sampling point) variation diagram of the waveform data of reference channel 5. Figure 4 This is a graph showing the waveform data signal intensity-time (sampling point) variation of the four reference channels.

[0075] Furthermore, the noise threshold of each reference channel is determined by the signal waveform head-to-tail average method. When the hyperspectral lidar system detects the simplest scene, for example, the emitted laser only interacts with one target and then returns, and the laser path does not penetrate other objects (except air). In this way, the waveform formed only has one emission pulse echo and one target reflection echo near the beginning and before the end, and the rest is noise. The target distance can be calculated based on the time difference between the emission pulse echo and the target reflection echo. Usually, a small area before and at the end of the signal waveform does not contain the emission pulse echo and the target reflection echo. This area can be used as the background noise estimation area. Due to the uncertainty of actual detection, echo signals may appear at the beginning or end of the signal waveform, so the smaller noise level between the two ends is taken as the background noise of the entire waveform. The calculation formula is as follows:

[0076] μnoise =min{μ beginning ,μ ending}

[0077] threshold noise =μ noise +n×σ noise

[0078] Among them, μ noise is the noise average, μ beginning To calculate the starting position of the noise, μ ending To calculate the end position of the noise, threshold noise is the noise threshold, σ noise is the noise standard deviation, n is the noise threshold coefficient, which is generally set to 3 or 4. Here we set it to 3. The length of the first and last areas is selected as 100 sampling points. The mean and standard deviation are substituted into the second formula to calculate the noise threshold.

[0079] In step S202 , a first integrated threshold-crossing segment timing interval is calculated based on the waveform data of each reference channel and the noise threshold of each reference channel.

[0080] Furthermore, in some embodiments, a first comprehensive cross-threshold segment timing interval is calculated based on the waveform data of each reference channel and the noise threshold of each reference channel, including: calculating at least one cross-threshold segment in each reference channel signal whose waveform data is greater than the noise threshold of the corresponding reference channel signal, and the timing index and length of each cross-threshold segment; based on the length of the cross-threshold segment corresponding to each reference channel signal, eliminating the cross-threshold segments in each reference channel signal that are smaller than a preset constraint window to obtain the remaining cross-threshold segments of each reference channel signal; and obtaining the first comprehensive cross-threshold segment timing interval based on the union of the remaining cross-threshold segments of each reference channel signal.

[0081] The cross-threshold segment constraint factor, known as the preset constraint window, means that for an acquisition card with a 2GHz sampling rate per channel and a 5ns laser pulse width, the sampling segment length is 10 sampling points. After the laser pulse interacts with the observed target, the echo signal pulse width must be at least 10 sampling points due to the "broadening" phenomenon. Therefore, a constraint window of 10 is set, indicating that cross-threshold segments exceeding 10 sampling points are initially considered valid. This value can be modified based on the laser pulse width and acquisition card sampling rate and is not specifically limited here.

[0082] Specifically, the timing of the sampling points whose amplitude of the waveform data of each reference channel is greater than the noise threshold is recorded. If there are continuous sampling points whose amplitudes are all greater than the noise threshold in the timing, this set of continuous sampling points is regarded as a threshold-crossing segment. The timing of the first sampling point of the threshold-crossing segment is the timing index of the threshold-crossing segment. The total number of sampling points of the threshold-crossing segment minus 1 is recorded as the length of the threshold-crossing segment. For a single point, that is, the amplitudes of the two adjacent sampling points on the left and right are both lower than the noise threshold, no record is made. Therefore, taking the above-mentioned reference channels 5, 6, 7, and 8 as an example, the timing index and length of the segments where the signal waveform of each reference channel exceeds their respective noise thresholds are calculated. The results are shown in Table 1.

[0083] Table 1

[0084]

[0085] Furthermore, the threshold segments whose length is less than the preset constraint window (here set to 10) in each reference channel are eliminated. The results after elimination are shown in Table 2.

[0086] Table 2

[0087]

[0088] Furthermore, the remaining cross-threshold segment timing intervals of the four reference channels in Table 2 are combined to obtain the first comprehensive cross-threshold segment timing index and length. The combined result is shown in Table 3.

[0089] Table 3

[0090]

[0091] Therefore, the first comprehensive threshold-crossing segment timing interval can be determined according to the first comprehensive threshold-crossing segment timing index and length.

[0092] In step S203, the reference channel intersection constraint enable and the hit number are determined based on multiple reference channels, and when the reference channel intersection constraint enable is the first preset threshold and the hit number is in the preset value range, the comprehensive over-threshold segments that do not meet the preset conditions in the first comprehensive over-threshold segment timing interval are eliminated to obtain the second comprehensive over-threshold segment timing interval.

[0093] Optionally, the first preset threshold may be 1, and the preset value interval may be {1, 2, 3, 4}.

[0094] Specifically, taking the reference channels 5, 6, 7, and 8 as an example, the embodiment of the present application introduces two constraint factors based on the above four reference channels—reference channel intersection constraint enable and hit count. Optionally, the reference channel intersection constraint enable value can be 0 or 1. When it is 0, it means that the hit count is invalid, and when it is 1, it means that the hit count is valid. The hit count value range is {1, 2, 3, 4}, which means that when at least one of the four reference channels has an intersection between the over-threshold segment in this channel and the first comprehensive over-threshold segment timing interval, the comprehensive over-threshold segment is retained, otherwise it is further eliminated.

[0095] For example, set the reference channel intersection constraint to 1 and the hit number to 4, and intersect each fragment of the comprehensive cross-threshold fragment set in Table 3 with the cross-threshold fragment sets of the four reference channels in Table 2. If the number of non-empty sets in the four intersections is not less than the hit number 4, then the fragment of the comprehensive cross-threshold fragment set is retained. Otherwise, the fragment of the comprehensive cross-threshold fragment set is eliminated. The final second comprehensive cross-threshold fragment timing index and length are shown in Table 4:

[0096] Table 4

[0097]

[0098] Therefore, the second comprehensive threshold-crossing segment timing interval can be determined according to the second comprehensive threshold-crossing segment timing index and length.

[0099] It should be noted that during the implementation of the embodiment, the larger the value of the hit number is set, the stricter the retention condition for each segment of the comprehensive threshold segment set in Table 3. This means that the probability of noise interference pulses appearing around each reference channel at the same time is lower. When the detection environment solar background light is strong enough, there will be a large number of densely distributed high-intensity noise pulses in each channel waveform. In this case, setting the hit number value to a larger value can better ensure that these high-intensity noise segments are effectively eliminated. However, if the detection environment background light intensity is not considered and the hit number is directly set to 4, it is possible to eliminate the effective echo of the detection target. Therefore, the hit number should be determined in combination with the actual operating environment of the hyperspectral lidar.

[0100] In step S204, a final integrated threshold crossing segment timing interval is obtained according to whether the reference channel intersection constraint is enabled, the preset pre- and post-threshold crossing lengths, the first integrated threshold crossing segment timing interval or the second integrated threshold crossing segment timing interval.

[0101] Further, in some embodiments, the final comprehensive cross-threshold segment timing interval is obtained based on the reference channel intersection constraint enable, the preset pre- and post-threshold length, the first comprehensive cross-threshold segment timing interval or the second comprehensive cross-threshold segment timing interval, including: if the reference channel intersection constraint enable is the first preset threshold, then the final comprehensive cross-threshold segment timing interval is obtained based on the sum of the second comprehensive cross-threshold segment timing interval and the preset pre- and post-threshold length; if the reference channel intersection constraint enable is the second preset threshold, then the final comprehensive cross-threshold segment timing interval is obtained based on the sum of the first comprehensive cross-threshold segment timing interval and the preset pre- and post-threshold length.

[0102] Optionally, the second preset threshold may be 0.

[0103] The pre- and post-threshold lengths are set to extend the cross-threshold segment by a certain length, which can be set to 15 sampling points. This parameter is used to subtract 15 from the left edge of the cross-threshold segment's timing interval and add 15 to the right edge. This parameter serves two purposes: first, it allows for a more complete capture of the entire cross-threshold segment by adding some background noise; second, it allows for the subsequent waveform decomposition and fitting of the cross-threshold segment to preserve some baseline information.

[0104] For example, when the reference channel intersection constraint is enabled as the second preset threshold, the final threshold crossing segment timing index and length are obtained by adding the first comprehensive threshold crossing segment timing index and length in Table 3 to the preset length before and after the threshold crossing. The results are shown in Table 5:

[0105] Table 5

[0106]

[0107] Furthermore, when the reference channel intersection constraint is enabled to 1 and the number of hits is 4, the final threshold crossing segment timing index and length are obtained by adding the preset length before and after the threshold crossing according to the second comprehensive threshold crossing segment timing index and length in Table 4. The results are shown in Table 6:

[0108] Table 6

[0109]

[0110] Therefore, the final integrated threshold crossing segment timing interval can be determined according to the final integrated threshold crossing segment timing index and length.

[0111] Furthermore, in some embodiments, after obtaining the final integrated threshold crossing segment timing interval, it also includes: transmitting and storing the same timing interval waveform data in multiple channels under each laser pulse triggering according to the final integrated threshold crossing segment timing interval.

[0112] Specifically, the waveform data in the same timing interval in 56 channels under each laser pulse triggering is transmitted and stored according to the obtained final threshold-crossing segment timing interval.

[0113] Through calculation, we can know that after data collection and redundancy removal, the data rate calculation formula required for transmission is as follows:

[0114]

[0115] Among them, datarate opt To transmit the required data rate, SL opt is the total length of the sampling sequence after redundancy removal, SL initial It is the sampling length of each channel under each laser trigger before collecting redundancy, and datarate is the transmission rate of the acquisition card.

[0116] When the reference channel intersection constraint is enabled to 0, the total length of the above-mentioned sampling sequence after de-redundancy is the sum of the length of the threshold segment in Table 5, 228. When the reference channel intersection constraint is enabled to 1 and the hit count is 4, the total length of the above-mentioned sampling sequence after de-redundancy is the sum of the length of the threshold segment in Table 6, 101. initial The sampling length of each channel under each laser trigger before redundancy is collected, which is 8000. It can be calculated that the data rate that the airborne 56-channel hyperspectral lidar system needs to transmit in these two cases is approximately 2.490GBps and 1.103GBps respectively. This shows that the requirements for the transmission rate of the acquisition card backplane and the write rate of the computer hard disk are greatly reduced.

[0117] In order to facilitate those skilled in the art to more clearly and intuitively understand the data acquisition and processing method for long-range detection of hyperspectral laser radar in the embodiment of the present application, the following is combined with Figure 5 Provide detailed explanation.

[0118] like Figure 5 As shown, the data acquisition and processing method for long-range detection of hyperspectral laser radar includes the following steps:

[0119] S501, start.

[0120] S502: Acquire 56 channels of raw data collected by the hyperspectral lidar system during each laser pulse.

[0121] S503 , selecting 4 channels from the 56 channels as reference channels, calculating the noise threshold of each reference channel, and calculating the timing (sampling point) index and length of the threshold-crossing segment of the signal waveform of each reference channel.

[0122] S504: Set the size of the cross-threshold segment constraint window to remove the cross-threshold segments of the signal wave in each reference channel whose length is less than the constraint window.

[0123] S505 , performing a union of the remaining threshold-crossing segment timing intervals of the signal waveforms of the four reference channels to obtain a combined threshold-crossing segment timing interval.

[0124] S506 , determining whether the reference channel intersection constraint enable is 1, if so, executing S507 , otherwise executing S510 .

[0125] S507, calculate how many sub-reference channel cross-threshold segments intersect with each integrated cross-threshold segment (the number ranges from {1, 2, 3, 4}), and determine whether the number is ≥ the hit number. If so, execute S508, otherwise, execute S509.

[0126] S508: Further remove the over-threshold segment.

[0127] S509: retain the cross-threshold segment.

[0128] S510: Add the lengths before and after the threshold crossing to obtain the final time series interval of the threshold crossing segment.

[0129] S511 , only the 56-channel channel signal waveform data in the threshold crossing segment timing interval obtained in the previous step and the final threshold crossing segment timing index value and segment length obtained under the laser triggering are transmitted and stored.

[0130] S512, end.

[0131] Therefore, the present invention utilizes the temporal correlation of the echo waveforms between the reference channels of the hyperspectral lidar, and obtains the data segment interval that needs to be retained by calculating and processing only a few reference channel waveforms. Parameters such as the constraint window, reference channel intersection constraint enable, and hit number are introduced into the calculation and processing method, which can further eliminate the temporal segments where strong background light pulse noise is located, and ultimately ensure that the hyperspectral lidar can still perform long-distance ground detection and collection normally even under strong sunlight interference during the day.

[0132] According to the data acquisition and processing method for long-distance detection of a hyperspectral laser radar according to the embodiment of the present application, multiple reference channels and their noise thresholds are determined from multiple channels, and the first comprehensive threshold-crossing segment timing interval is calculated according to the waveform data and noise threshold of each reference channel; when the reference channel intersection constraint is enabled to the first preset threshold and the number of hits is in the preset value interval, the comprehensive threshold-crossing segments that do not meet the preset conditions in the first comprehensive threshold-crossing segment timing interval are eliminated to obtain the second comprehensive threshold-crossing segment timing interval; according to the reference channel intersection constraint, the preset threshold length before and after, the first comprehensive threshold-crossing segment timing interval or the second comprehensive threshold-crossing segment timing interval, the final comprehensive threshold-crossing segment timing interval is obtained. Thus, the problem that the simple threshold-crossing method in the background technology is not applicable to the ground observation scene of the hyperspectral laser radar under long-distance strong solar background light is solved. By constraining the window, optimizing the reference channel and combining the waveform processing methods of each reference channel, the high-intensity noise pulses widely distributed in the echo waveform are further eliminated, so that the hyperspectral laser radar can achieve long-distance detection under strong solar background light.

[0133] Next, a data acquisition and processing device for long-distance detection of a hyperspectral lidar proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0134] Figure 6 It is a block diagram of a data acquisition and processing device for long-range detection of a hyperspectral lidar according to an embodiment of the present application.

[0135] like Figure 6 As shown, the data acquisition and processing device 10 for long-distance detection of a hyperspectral laser radar includes: an acquisition module 100 , a calculation module 200 , a processing module 300 and a determination module 400 .

[0136] Among them, the acquisition module 100 is used to obtain the waveform data of multiple channels collected by the hyperspectral laser radar under each laser pulse triggering, and determine multiple reference channels from the multiple channels, and determine the noise threshold of each reference channel; the calculation module 200 is used to calculate the first comprehensive over-threshold segment timing interval based on the waveform data of each reference channel and the noise threshold of each reference channel; the processing module 300 is used to determine the reference channel intersection constraint enable and the number of hits based on multiple reference channels, and when the reference channel intersection constraint enable is the first preset threshold and the number of hits is in the preset value range, the comprehensive over-threshold segments that do not meet the preset conditions in the first comprehensive over-threshold segment timing interval are eliminated to obtain the second comprehensive over-threshold segment timing interval; the determination module 400 is used to obtain the final comprehensive over-threshold segment timing interval based on the reference channel intersection constraint enable, the preset pre- and post-threshold length, the first comprehensive over-threshold segment timing interval or the second comprehensive over-threshold segment timing interval.

[0137] Furthermore, in some embodiments, the calculation module 200 is used to: calculate at least one cross-threshold segment in each reference channel signal in which the waveform data is greater than the noise threshold of the corresponding reference channel signal, and the timing index and length of each cross-threshold segment; based on the length of the cross-threshold segment corresponding to each reference channel signal, eliminate the cross-threshold segments in each reference channel signal that are smaller than a preset constraint window to obtain the remaining cross-threshold segments of each reference channel signal; and obtain the first comprehensive cross-threshold segment timing interval based on the union of the remaining cross-threshold segments of each reference channel signal.

[0138] Further, in some embodiments, the determination module 400 is used to: if the reference channel intersection constraint is enabled as the first preset threshold, then the final comprehensive cross-threshold segment timing interval is obtained based on the sum of the second comprehensive cross-threshold segment timing interval and the preset length before and after the cross-threshold; if the reference channel intersection constraint is enabled as the second preset threshold, then the final comprehensive cross-threshold segment timing interval is obtained based on the sum of the first comprehensive cross-threshold segment timing interval and the preset length before and after the cross-threshold.

[0139] Furthermore, in some embodiments, after obtaining the final integrated threshold crossing segment timing interval, the determination module 400 is also used to: transmit and store the same timing interval waveform data in multiple channels under each laser pulse triggering according to the final integrated threshold crossing segment timing interval.

[0140] Furthermore, in some embodiments, the acquisition module 100 is configured to select channels within a preset band interval from the multiple channels as multiple reference channels according to the waveform data of each channel.

[0141] It should be noted that the above explanation of the embodiment of the data acquisition and processing method for long-range detection of a hyperspectral laser radar is also applicable to the data acquisition and processing device for long-range detection of a hyperspectral laser radar of this embodiment, and will not be repeated here.

[0142] According to the data acquisition and processing device for long-distance detection of a hyperspectral laser radar according to the embodiment of the present application, multiple reference channels and their noise thresholds are determined from multiple channels, and a first comprehensive threshold-crossing segment timing interval is calculated based on the waveform data and noise threshold of each reference channel; when the reference channel intersection constraint is enabled to be the first preset threshold and the number of hits is in the preset value interval, the comprehensive threshold-crossing segments that do not meet the preset conditions in the first comprehensive threshold-crossing segment timing interval are eliminated to obtain a second comprehensive threshold-crossing segment timing interval; according to the reference channel intersection constraint, the preset threshold length before and after, the first comprehensive threshold-crossing segment timing interval or the second comprehensive threshold-crossing segment timing interval, the final comprehensive threshold-crossing segment timing interval is obtained. Thus, the problem that the simple threshold-crossing method in the background technology is not applicable to the ground observation scene of the hyperspectral laser radar under long-distance strong solar background light is solved. By constraining the window, optimizing the reference channel and combining the waveform processing methods of each reference channel, the high-intensity noise pulses widely distributed in the echo waveform are further eliminated, so that the hyperspectral laser radar can achieve long-distance detection under strong solar background light.

[0143] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0144] Memory 701, processor 702, and a computer program stored in memory 701 and executable on processor 702. When processor 702 executes the program, the data acquisition and processing method for long-range detection of a hyperspectral laser radar provided in the above embodiment is implemented.

[0145] Furthermore, the electronic device further includes:

[0146] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0147] The memory 701 is used to store computer programs that can be run on the processor 702 .

[0148] The memory 701 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0149] If the memory 701, processor 702, and communication interface 703 are implemented independently, the communication interface 703, memory 701, and processor 702 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0150] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0151] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0152] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned data acquisition and processing method for long-range detection of a hyperspectral lidar.

[0153] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" 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 application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0154] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0155] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A data acquisition and processing method for long-range detection of a hyperspectral laser radar, characterized in that: The following steps are involved: Acquire waveform data of multiple channels collected by the hyperspectral lidar under each laser pulse triggering, determine multiple reference channels from the multiple channels, and determine the noise threshold of each reference channel; Calculating a first integrated threshold-crossing segment timing interval according to the waveform data of each reference channel and the noise threshold of each reference channel; Determining, based on the multiple reference channels, a reference channel intersection constraint enable and a hit count, and when the reference channel intersection constraint enable is a first preset threshold and the hit count is within a preset value range, eliminating the comprehensive cross-threshold segments that do not meet the preset conditions in the first comprehensive cross-threshold segment timing interval to obtain a second comprehensive cross-threshold segment timing interval; Obtaining a final integrated threshold crossing segment timing interval according to the reference channel intersection constraint enable, the preset pre- and post-threshold crossing lengths, the first integrated threshold crossing segment timing interval, or the second integrated threshold crossing segment timing interval; Among them, the final comprehensive over-threshold fragment timing interval is obtained based on the reference channel intersection constraint enable, the preset pre- and post-threshold length, the first comprehensive over-threshold fragment timing interval or the second comprehensive over-threshold fragment timing interval, including: if the reference channel intersection constraint enable is the first preset threshold, then the final comprehensive over-threshold fragment timing interval is obtained based on the sum of the second comprehensive over-threshold fragment timing interval and the preset pre- and post-threshold length; if the reference channel intersection constraint enable is the second preset threshold, then the final comprehensive over-threshold fragment timing interval is obtained based on the sum of the first comprehensive over-threshold fragment timing interval and the preset pre- and post-threshold length.

2. The method according to claim 1, characterized in that The step of calculating the first integrated threshold crossing segment timing interval according to the waveform data of each reference channel and the noise threshold of each reference channel includes: Calculate at least one cross-threshold segment in each reference channel signal in which waveform data is greater than a noise threshold of the corresponding reference channel signal, and a timing index and length of each cross-threshold segment; Based on the length of the cross-threshold segment corresponding to each reference channel signal, removing the cross-threshold segment smaller than a preset constraint window in each reference channel signal to obtain the remaining cross-threshold segment of each reference channel signal; The first integrated threshold-crossing segment timing interval is obtained according to the union of the remaining threshold-crossing segments of each reference channel signal.

3. The method according to claim 1, characterized in that After obtaining the final integrated threshold crossing segment time sequence interval, the method further includes: The waveform data of the same timing interval in the multiple channels under each laser pulse triggering are transmitted and stored according to the final integrated threshold crossing segment timing interval.

4. The method according to claim 1, wherein Determining multiple reference channels from the multiple channels includes: According to the waveform data of each channel, channels in a preset band interval among the multiple channels are selected as the multiple reference channels.

5. A data acquisition and processing device for long-range detection of hyperspectral laser radar, characterized in that: include: An acquisition module is used to acquire waveform data of multiple channels collected by the hyperspectral laser radar under each laser pulse triggering, and to determine multiple reference channels from the multiple channels, and to determine a noise threshold of each reference channel; a calculation module, configured to calculate a first integrated threshold-crossing segment timing interval based on the waveform data of each reference channel and the noise threshold of each reference channel; a processing module, configured to determine, based on the multiple reference channels, a reference channel intersection constraint enable and a hit count, and, when the reference channel intersection constraint enable is a first preset threshold and the hit count is within a preset value range, eliminate the comprehensive cross-threshold segments that do not meet the preset conditions in the first comprehensive cross-threshold segment timing interval to obtain a second comprehensive cross-threshold segment timing interval; A determination module, configured to obtain a final integrated threshold crossing segment timing interval based on whether the reference channel intersection constraint is enabled, a preset pre- and post-threshold crossing length, the first integrated threshold crossing segment timing interval, or the second integrated threshold crossing segment timing interval; The determining module is configured to: if the reference channel intersection constraint is enabled at a first preset threshold, obtain the final integrated threshold crossing segment timing interval based on the sum of the second integrated threshold crossing segment timing interval and the preset lengths before and after the threshold crossing; If the reference channel intersection constraint is enabled to a second preset threshold, the final integrated threshold crossing segment timing interval is obtained according to the sum of the first integrated threshold crossing segment timing interval and the preset threshold crossing length.

6. The device according to claim 5, characterized in that The computing module is configured to: Calculate at least one cross-threshold segment in each reference channel signal in which waveform data is greater than a noise threshold of the corresponding reference channel signal, and a timing index and length of each cross-threshold segment; Based on the length of the cross-threshold segment corresponding to each reference channel signal, removing the cross-threshold segment smaller than a preset constraint window in each reference channel signal to obtain the remaining cross-threshold segment of each reference channel signal; The first integrated threshold-crossing segment timing interval is obtained according to the union of the remaining threshold-crossing segments of each reference channel signal.

7. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the data acquisition and processing method for long-range detection of a hyperspectral lidar as described in any one of claims 1 to 4.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the data acquisition and processing method for long-range detection of a hyperspectral laser radar as described in any one of claims 1 to 4.

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