Red tide extraction method based on two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra

Abstract

The present invention discloses a red tide extraction method based on two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra, including: constructing four two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra, including the distance factor h_R of the line connecting the two coordinate points from the red band coordinate point to the green-near-infrared band coordinate point in the wavelength-band value two-dimensional space; the distance factor h_G of the line connecting the two coordinate points from the green band coordinate point to the blue-red band coordinate point; the slope factor slope of the line connecting the two points in the green-red band G‑R ; the slope factor slope of the line connecting the two points in the red-near-infrared band R‑NIR . The present invention realizes the rapid automatic extraction of the position and scope of red tides through the comprehensive superposition in the form of multi-factor decision-making and discrimination. The technical solution of the present invention reduces the dependence of the red tide extraction index on spectra other than blue, green, red, and near-infrared, solves the problem of poor extraction accuracy of a single extraction index, and can realize the extraction of red tide information through the superposition discrimination of the four two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra.

Description

Technical Field

[0001] The present invention belongs to the field of remote sensing technology, and particularly relates to a method for extracting red tides based on two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra. Background Art

[0002] Traditional monitoring of algal bloom disasters mainly relies on manual inspections, video monitoring, buoy monitoring, etc., which are basically point or small-area monitoring methods. The monitoring range is small, the monitoring cost is high, and there are easily monitoring blind spots. By taking advantage of the macroscopic, periodic, and low-cost characteristics of remote sensing technology, large-scale water areas such as lakes and reservoirs can be scanned and monitored regularly and quickly, enabling rapid grasp of the outbreak situation of algal blooms, such as location, area, dynamic changes, etc. Moreover, with the development of remote sensing satellite technology, more and more satellite data can be used to monitor algal blooms, and the quality of algal bloom remote sensing monitoring products has been significantly improved in terms of spatial and temporal scales.

[0003] Currently, most algal bloom remote sensing monitoring applications are based on multispectral satellite data, mainly relying on image classification and single extraction indices. Affected by various factors such as imaging differences of multi-source data and water body suspended sediment, it has a strong dependence on the spectral resolution of satellite data, poor generality of multi-source remote sensing data, and low extraction accuracy, greatly reducing the effectiveness of algal bloom remote sensing monitoring. Conventional red tide extraction indices cannot meet the rapid and automatic extraction of red tide objects from multi-source remote sensing satellite data, hindering the application of remote sensing technology in high-frequency red tide monitoring. Therefore, a red tide extraction method, system, and storage medium based on two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra are needed. Summary of the Invention

[0004] The main purpose of the present invention is to overcome the deficiencies of the prior art and provide a method for extracting red tides based on two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra, which solves the problems of dependence of conventional red tide extraction indices on spectral data other than blue, green, red, and near-infrared, low extraction accuracy or low efficiency of red tide objects, and can achieve the extraction of red tide objects through comprehensive superposition discrimination and step-by-step threshold judgment based on blue, green, red, and near-infrared spectral characteristic factors.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] In the first aspect, the present invention provides a method for extracting red tides based on two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra, including the following steps:

[0007] Calculate the first distance factor h_R of the two-point connection line from the red-band coordinate point to the green-near-infrared band coordinate point in the wavelength-band value two-dimensional spectral space;

[0008] Calculate the second distance factor h_G of the line connecting the green-band coordinate point to the blue-red band coordinate point in the two-dimensional spectral space of wavelength-band value;

[0009] Calculate the first slope factor slope of the line connecting the green-red band points in the two-dimensional spectral space of wavelength-band value G-R ;

[0010] Calculate the second slope factor slope of the line connecting the red-near infrared band points in the two-dimensional spectral space of wavelength-band value R-NIR ;

[0011] Based on the first distance factor h_R, the second distance factor h_G, the first slope factor slope G-GR and the second slope factor slope R-NIR , through factor synthesis and superposition, and step-by-step threshold judgment, realize the rapid and automatic extraction of the location and scope of red tides.

[0012] As a preferred technical solution, the calculation method of the first distance factor h_R is as follows:

[0013] h_R = L R ×S R

[0014] where L R represents the distance from the red-band scatter point to the line connecting the green-near infrared band scatter points in the two-dimensional spectral space; S R represents the sign of L R , positive when the red-band scatter point is above the line connecting the green-near infrared band scatter points, and negative when below;

[0015]

[0016]

[0017] where f(x) indicates the band value of the x band, x is blue for the blue band, x is green for the green band, x is red for the red band, and x is nir for the near infrared band; λ x represents the wavelength of the x band, with the unit of nm.

[0018] As a preferred technical solution, the calculation method of the second distance factor h_G is as follows:

[0019] h_G = L G ×S G

[0020] where L G represents the distance from the green-band scatter point to the line connecting the blue-red band scatter points in the two-dimensional spectral space; S G represents the sign of L GFor the symbol, if the scatter points in the green band are above the line connecting the scatter points in the blue - red bands, it is positive; if below, it is negative.

[0021]

[0022]

[0023] As an optimized technical solution, the calculation method of the first slope factor slope G-R is as follows:

[0024] slope G-R = [f(green) - f(red)] ÷ [λ green - λ red ;

[0025] Among them, f(green) represents the band value of the green band, f(red) represents the band value of the red band, λ green represents the wavelength of the green band, and λ ted represents the wavelength of the red band.

[0026] As an optimized technical solution, the calculation method of the second slope factor slope R-NIR is as follows:

[0027] slope R-NIR = [f(red) - f(nir)] ÷ [λ red - λ nir ;

[0028] Among them, f(red) represents the band value of the red band, f(nir) represents the band value of the near - infrared band, and the λ red represents the wavelength of the red band, and λ nir represents the wavelength of the near - infrared band.

[0029] As an optimized technical solution, through factor comprehensive superposition and step - by - step threshold judgment, the rapid automatic extraction of the red - tide position and range is realized, specifically:

[0030] Judge the logical relationship between the first distance factor h_R and the threshold thd1. If h_R > thd1, it is a non - red - tide object; otherwise, continue to judge;

[0031] Judge the logical relationship between the second distance factor h_G and the threshold thd2. If h_G ≤ thd2, it is a red - tide object; otherwise, continue to judge;

[0032] Judge the logical relationship between a slope factor h_G and the thresholds thd3, thd4. If thd3 < h_G < thd4, continue to the next step of judgment; otherwise, it is a non - red - tide object;

[0033] Judge the second slope factor slope G-R for its logical relationship with the threshold thd5. If slope G-R ≥ thd5, it is a non - red - tide object; otherwise, continue the judgment;

[0034] Judge the factor slope R-NIR for its logical relationship with the threshold thd6. If slope G-GR ≥ thd6, it is a red - tide object; otherwise, it is a non - red - tide object.

[0035] As an optimized technical solution, thd1 - thd6 will vary with the image data of different satellite sensors.

[0036] In a second aspect, the present invention provides a red - tide extraction system based on two - dimensional spatial characteristic factors of blue, green, red, and near - infrared spectra, which is applied to the red - tide extraction method based on two - dimensional spatial characteristic factors of blue, green, red, and near - infrared spectra, and includes a first calculation module, a second calculation module, a third calculation module, a fourth calculation module, and a red - tide extraction module;

[0037] The first calculation module is used to calculate the first distance factor h_R of the line connecting the red - band coordinate point to the green - near - infrared - band coordinate point in the wavelength - band - value two - dimensional spectral space;

[0038] The second calculation module is used to calculate the second distance factor h_G of the line connecting the green - band coordinate point to the blue - red - band coordinate point in the wavelength - band - value two - dimensional spectral space;

[0039] The third calculation module is used to calculate the first slope factor slope of the line connecting the two points of the green - red band in the wavelength - band - value two - dimensional spectral space G-R ;

[0040] The fourth calculation module is used to calculate the second slope factor slope of the line connecting the two points of the red - near - infrared band in the wavelength - band - value two - dimensional spectral space R-NIR ;

[0041] The red - tide extraction module is used to realize the rapid automatic extraction of the red - tide position and range based on the first distance factor h_R, the second distance factor h_G, the first slope factor slope G-R and the second slope factor slope R-NIR , through factor comprehensive superposition and step - by - step threshold judgment.

[0042] In a fourth aspect, the present invention provides an electronic device, and the electronic device includes:

[0043] At least one processor; and,

[0044] A memory communicatively connected to the at least one processor; wherein,

[0045] The memory stores computer program instructions executable by the at least one processor, and the computer program instructions are executed by the at least one processor to enable the at least one processor to execute the red tide extraction method based on the two-dimensional spatial feature factors of blue, green, red, and near-infrared spectra.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium storing a program, which when executed by a processor, implements the red tide extraction method based on the two-dimensional spatial feature factors of blue, green, red, and near-infrared spectra.

[0047] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0048] (1) The red tide extraction method of the present invention has low requirements for the preprocessing of satellite remote sensing images. For satellite remote sensing images that require red tide extraction, no radiation correction preprocessing is needed. At the same time, the algorithm of the present invention has good universality and can be well applied to the image data of different satellite sensors, including spectral feature factors and the hierarchical method. For the image data of the same satellite sensor, the discrimination threshold is also relatively stable. The algorithm of the present invention is a new red tide extraction method that comprehensively discriminates and fuses multiple spectral feature factors in the form of a decision and discrimination tree. It has low spectral dependence and only requires the spectral bands of blue, green, red, and near-infrared for extraction and calculation. The extraction accuracy is better than the current single-factor extraction algorithm, and the four spectral bands of blue, green, red, and near-infrared are the basic spectral bands equipped by all current satellite sensors, which greatly increases the satellite remote sensing data sources that can be used for this algorithm, especially high-spatial-resolution satellite image data sources, and greatly improves the spatial scale and timeliness of red tide monitoring, solving the dilemma that high-precision red tide monitoring is restricted by satellite spatial resolution and band settings.

[0049] (2) The algorithm of the present invention has high extraction accuracy. By constructing multiple spectral feature factors in the two-dimensional spectral space of blue, green, red, and near-infrared, and combining the methods of multi-factor comprehensive discrimination and hierarchical stripping level by level, objects other than red tide, including clean sea water, tidal flats, highly turbid water bodies, etc., are accurately stripped, realizing the high-precision extraction of red tide objects. The research and development of the results of the present invention can greatly expand the satellite data sources for the automatic monitoring of algal bloom disasters, especially the application of high-resolution multispectral data, reduce the requirements of algal bloom recognition and extraction algorithms for spectra, and improve the timeliness of the discovery of algal bloom disasters and the accuracy of recognition and extraction based on multi-source satellite remote sensing data, and improve the early warning and prediction ability of red tide disasters. Description of the Drawings

[0050] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0051] Figure 1 is the flowchart of the red tide extraction method based on the two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra of the present invention.

[0052] Figure 2 is the effect comparison analysis diagram.

[0053] Figure 3 is the block diagram of the red tide extraction system based on the two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra of the present invention.

[0054] Figure 4 is the structural schematic diagram of the storage medium of the embodiment of the present invention. Specific Embodiments

[0055] In order to enable those skilled in the art of the present technology to better understand the solutions of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0056] Referring to "embodiments" in the present application means that the specific features, structures, or characteristics described in connection with the embodiments may be included in at least one embodiment of the present application. The phrase appears in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art understand explicitly and implicitly that the embodiments described in the present application can be combined with other embodiments.

[0057] Please refer to Figure 1 , the red tide extraction method based on the two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra in this embodiment includes the following steps:

[0058] S1. Calculate the first distance factor h_R of the line connecting the red band coordinate point to the green-near-infrared band coordinate point in the two-dimensional spectral space of wavelength-band value;

[0059] Furthermore, the calculation method of the first distance factor h_R is as follows:

[0060] h_R = L R ×SR

[0061] Among them, L R represents the two-dimensional spectral space, the distance from the scatter point in the red band to the line connecting the scatter points in the green-near infrared bands; S R represents the sign of L R . When the scatter point in the red band is above the line connecting the scatter points in the green-near infrared bands, it is positive; when it is below, it is negative.

[0062]

[0063]

[0064] Among them, f(x) indicates the band value of the x band. When x is blue, it represents the blue band; when x is green, it represents the green band; when x is red, it represents the red band; when x is nir, it represents the near infrared band; λ x represents the wavelength of the x band, with the unit of nm.

[0065] S2. Calculate the second distance factor h_G from the coordinate point of the green band in the wavelength-band value two-dimensional spectral space to the line connecting the coordinate points of the blue-red bands.

[0066] Furthermore, the calculation method of the second distance factor h_G is as follows:

[0067] h_G = L G ×S G

[0068] Among them, L G represents the two-dimensional spectral space, the distance from the scatter point of the green band to the line connecting the scatter points of the blue-red bands; S G represents the sign of L G . When the scatter point of the green band is above the line connecting the scatter points of the blue-red bands, it is positive; when it is below, it is negative.

[0069]

[0070]

[0071] S3. Calculate the first slope factor slope of the line connecting the two points of the green-red bands in the wavelength-band value two-dimensional spectral space G-R ;

[0072] Furthermore, the calculation method of the first slope factor slope G-R is as follows:

[0073] slope G-R = [f(green) - f(red)] ÷ [λ green - λ red ;

[0074] Among them, f(green) represents the band value of the green band, f(red) represents the band value of the red band, and λ green represents the wavelength of the green band, and λ red represents the wavelength of the red band.

[0075] S4. Calculate the second slope factor slope of the line connecting two points in the red-near-infrared band of the wavelength-band value two-dimensional spectral space R-NIR ;

[0076] Furthermore, the calculation method of the second slope factor slope R-NIR is as follows:

[0077] slope R-NIR = [f(red) - f(nir)] ÷ [λ red - λ nir

[0078] Among them, f(red) represents the band value of the red band, f(nir) represents the band value of the near-infrared band, and the λ red represents the wavelength of the red band, and λ nir represents the wavelength of the near-infrared band.

[0079] S5. Based on the first distance factor h_R, the second distance factor h_G, the first slope factor slope G-R and the second slope factor slope R-NIR , through factor comprehensive superposition and step-by-step threshold judgment, realize the rapid automatic extraction of the red tide position and range. It can be understood that it means through the discrimination of multiple indicators, stacked layer by layer, similar to the idea of decision tree discrimination, to achieve the final separation and extraction purpose.

[0080] Furthermore, through factor comprehensive superposition and step-by-step threshold judgment, realize the rapid automatic extraction of the red tide position and range, specifically:

[0081] S51. Judge the logical relationship between the first distance factor h_R and the threshold thd1. If h_R > thd1, it is a non-red tide object; otherwise, continue to judge; it can be understood that the threshold is obtained through artificial sample statistics;

[0082] S52. Judge the logical relationship between the second distance factor h_G and the threshold thd2. If h_G ≤ thd2, it is a red tide object; otherwise, continue to judge;

[0083] S53. Judge the logical relationship between the first slope factor h_G and the thresholds thd3 and thd4. If thd3 < h_G < thd4, continue to the next step of judgment; otherwise, it is a non-red tide object;

[0084] S54. Judge the second slope factor slope​G-R The logical relationship with the threshold thd5. If slope G-R ≥thd5, it is a non - red - tide object; otherwise, continue to judge;

[0085] S55. Judge the logical relationship between the factor slope R-NIR and the threshold thd6. If slope G-R ≥thd6, it is a red - tide object; otherwise, it is a non - red - tide object.

[0086] The present invention is a new red - tide extraction method that comprehensively discriminates and fuses multiple spectral feature factors in the form of a decision - making and discrimination tree. It has low spectral dependence and only requires the spectral calculation of four bands: blue, green, red, and near - infrared for extraction. The extraction accuracy is better than the current single - factor extraction algorithm. Moreover, the four bands of blue, green, red, and near - infrared are the basic spectral bands equipped in all current satellite sensors, which greatly increases the satellite remote - sensing data sources available for this algorithm, especially the satellite image data sources with high spatial resolution. The spatial scale and timeliness of red - tide monitoring are greatly improved, solving the dilemma that high - precision red - tide monitoring is restricted by satellite spatial resolution and band settings.

[0087] In another embodiment, taking the red - tide extraction in the East China Sea as an example, and using the remote - sensing satellite data of OceanSat - 1D satellite images as an example, the red - tide extraction based on the method of the present invention is carried out, which specifically includes the following steps:

[0088] S1. Obtained 1 scene of OceanSat - 1D image data covering the offshore area outside the Yangtze River Estuary, namely "H1D_OPER_CZI_L1C_20200817T050938_20200817T051033_00968_10";

[0089] S2. Using the method of the present invention, calculate the four spectral feature factors of the OceanSat - 1D image data, h_R, h_G, slope G-R and slope R-NIR ;

[0090] S3. Using the method, system, and medium of the present invention, judge the logical relationship between the factor h_R and the threshold - 0.056. If h_R > - 0.056, it is a non - red - tide object; otherwise, continue to judge;

[0091] S4. Judge the logical relationship between the factor h_G and the threshold - 0.0133. If h_G ≤ - 0.0133, it is a red - tide object; otherwise, continue to judge;

[0092] S5. Judge the logical relationship between the factor h_G and the thresholds - 0.0133 and 0.0144. If - 0.0133 < h_G < 0.0144, continue to the next step of judgment; otherwise, it is a non - red - tide object;

[0093] S6. Determine the factor slope G-R for its logical relationship with the threshold value of -21. If slope G-R ≥ -21, it is a non-red tide object; otherwise, continue the judgment;

[0094] S7. Determine the factor slope R-NIR for its logical relationship with the threshold value of -16. If slope G-R ≥ -16, it is a red tide object; otherwise, it is a non-red tide object.

[0095] S8. Through the comprehensive discrimination in the above steps, finally obtain two types of objects: red tides and non-red tides.

[0096] In this embodiment, the red tide information of some regions is also extracted from the scene image data by the method of manual visual interpretation, and compared with the extraction results of the method of the present invention. Since the manual visual interpretation is the best, the two results are compared to reflect the accuracy of the automatic extraction results of the present method.

[0097] The results show that compared with the manual visual interpretation, please refer to Figure 2 , from the perspective of the position boundaries of the extraction results, the visually extracted results are generally basically consistent with the extraction results of the algorithm of this study; referring to Table 1 for the quantitative extraction area, compared with the visually extracted results, the average relative error is 8.94%, and the extraction accuracy of the method of the present invention reaches 91.06%.

[0098] Table 1

[0099]

[0100] It should be noted that for the foregoing method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously.

[0101] Based on the same idea as the red tide extraction method based on the two-dimensional spatial feature factors of blue, green, red, and near-infrared spectra in the above embodiments, the present invention also provides a red tide extraction system based on the two-dimensional spatial feature factors of blue, green, red, and near-infrared spectra. This system can be used to execute the above red tide extraction method based on the two-dimensional spatial feature factors of blue, green, red, and near-infrared spectra. For the sake of convenience of description, in the structural schematic diagram of the embodiment of the red tide extraction system based on the two-dimensional spatial feature factors of blue, green, red, and near-infrared spectra, only the parts related to the embodiments of the present invention are shown. Those skilled in the art can understand that the illustrated structure does not constitute a limitation on the device, and it may include more or fewer components than those illustrated, or combine certain components, or have different component arrangements.

[0102] Please refer to Figure 3 In another embodiment of the present application, a red tide extraction system 100 based on two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra is provided. The system includes a first calculation module 101, a second calculation module 102, a third calculation module 103, a fourth calculation module 104, and a red tide extraction module 105;

[0103] The first calculation module 101 is used to calculate a first distance factor h_R of the two-point connection line from the red-band coordinate point to the green-near-infrared band coordinate point in the wavelength-band value two-dimensional spectral space;

[0104] The second calculation module 102 is used to calculate a second distance factor h_G of the two-point connection line from the green-band coordinate point to the blue-red band coordinate point in the wavelength-band value two-dimensional spectral space;

[0105] The third calculation module 103 is used to calculate a first slope factor slope of the two-point connection line between the green-red bands in the wavelength-band value two-dimensional spectral space G-R ;

[0106] The fourth calculation module 104 is used to calculate a second slope factor slope of the two-point connection line between the red-near-infrared bands in the wavelength-band value two-dimensional spectral space R-NIR ;

[0107] The red tide extraction module 105 is used to perform a rapid automatic extraction of the red tide position and range based on the first distance factor h_R, the second distance factor h_G, the first slope factor slope G-R and the second slope factor slope R-NIR , through factor comprehensive superposition and step-by-step threshold judgment.

[0108] It should be noted that the red tide extraction system based on two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra of the present invention corresponds one-to-one with the red tide extraction method based on two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra of the present invention. The technical features and beneficial effects described in the above embodiments of the red tide extraction method based on two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra are applicable to the embodiments of the red tide extraction based on two-dimensional spatial characteristic factors of blue, green, red, and near-infrared spectra. For specific content, reference can be made to the description in the method embodiments of the present invention, which will not be elaborated here. This is hereby declared.

[0109] In addition, in the implementation manner of the red tide extraction system based on the two-dimensional spatial feature factors of blue, green, red, and near-infrared spectra in the above embodiments, the logical division of each program module is only for illustrative purposes. In practical applications, according to needs, for example, due to the configuration requirements of the corresponding hardware or the convenience of software implementation, the above functions can be assigned to different program modules to complete, that is, the internal structure of the red tide extraction system based on the two-dimensional spatial feature factors of blue, green, red, and near-infrared spectra is divided into different program modules to complete all or part of the functions described above.

[0110] Please refer to Figure 4 , in one embodiment, an electronic device for implementing a red tide extraction method based on two-dimensional spatial feature factors of blue, green, red, and near-infrared spectra is provided. The electronic device 200 may include a first processor 201, a first memory 202, and a bus, and may further include a computer program stored in the first memory 202 and executable on the first processor 201, such as a red tide extraction program 203.

[0111] Among them, the first memory 202 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the first memory 202 may be an internal storage unit of the electronic device 200, such as the mobile hard disk of the electronic device 200. In other embodiments, the first memory 202 may also be an external storage device of the electronic device 200, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 200. Further, the first memory 202 may also include both the internal storage unit and the external storage device of the electronic device 200. The first memory 202 can be used not only to store application software installed in the electronic device 200 and various types of data, such as the code of the red tide extraction program 203, etc., but also to temporarily store data that has been output or will be output.

[0112] In some embodiments, the first processor 201 may be composed of an integrated circuit. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple packaged integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The first processor 201 is the control core (Control Unit) of the electronic device, connecting various components of the entire electronic device through various interfaces and circuits, and executing various functions of the electronic device 200 and processing data by running or executing programs or modules stored in the first memory 202 and calling data stored in the first memory 202.

[0113] Figure 4 Only the electronic device with components is shown. Those skilled in the art can understand that Figure 4 The shown structure does not constitute a limitation on the electronic device 200, and it may include fewer or more components than shown, or combine certain components, or have a different component arrangement.

[0114] The red tide extraction program 203 stored in the first memory 202 of the electronic device 200 is a combination of multiple instructions. When running in the first processor 201, it can achieve:

[0115] Calculating the first distance factor h_R of the two-point connection line from the red band coordinate point to the green-near infrared band coordinate point in the wavelength-band value two-dimensional spectral space;

[0116] Calculating the second distance factor h_G of the two-point connection line from the green band coordinate point to the blue-red band coordinate point in the wavelength-band value two-dimensional spectral space;

[0117] Calculating the first slope factor slope of the two-point connection line between the green-red bands in the wavelength-band value two-dimensional spectral space G-R ;

[0118] Calculating the second slope factor slope of the two-point connection line between the red-near infrared bands in the wavelength-band value two-dimensional spectral space R-NIR ;

[0119] Based on the first distance factor h_R, the second distance factor h_G, the first slope factor slope G-R and the second slope factor slope R-NIR , through factor synthesis and superposition and step-by-step threshold judgment, the rapid automatic extraction of the red tide position and range is realized.

[0120] Furthermore, if the modules / units integrated in the electronic device 200 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM).

[0121] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it may include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application may include non-volatile and / or volatile memories. Non-volatile memories may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories may include random access memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0122] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0123] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. A red tide extraction method based on two-dimensional spatial characteristic factors of blue, green, red and near-infrared spectra, characterized in that: Including the following steps: Calculating a first distance factor h_R of the line connecting the red band coordinate point to the green - near infrared band coordinate point in the wavelength - band value two - dimensional spectral space; Calculating a second distance factor h_G of the line connecting the green band coordinate point to the blue - red band coordinate point in the wavelength - band value two - dimensional spectral space; Calculate the first slope factor slope of the line connecting two points in the green-red band in the two-dimensional spectral space of wavelength-band value G-R ; Calculate the second slope factor slope of the line connecting two points in the red-near infrared band in the two-dimensional spectral space of wavelength-band value R-NIR ; Based on the first distance factor h_R, the second distance factor h_G, and the first slope factor slope G-R and the second slope factor slope R-NIR Through comprehensive factor superposition and step-by-step threshold judgment, the rapid and automatic extraction of the location and range of red tides can be achieved; Through comprehensive factor superposition and step-by-step threshold judgment, the rapid and automatic extraction of the location and range of red tides can be achieved, specifically: Judging the logical relationship between the first distance factor h_R and the threshold thd1. If h_R > thd1, it is a non - red - tide object; otherwise, continue to judge; Judging the logical relationship between the second distance factor h_G and the threshold thd2. If h_G ≤ thd2, it is a red - tide object; otherwise, continue to judge; Judging the logical relationship between the second distance factor h_G and the thresholds thd3 and thd4. If thd3 < h_G < thd4, continue to the next step of judgment; otherwise, it is a non - red - tide object; Determine the first slope factor slope G-R The logical relationship with the threshold thd5 is that if slope G-R ≥thd5, it is not a red tide object; otherwise, continue to judge; Determine the second slope factor slope R-NIR The logical relationship with the threshold thd6 is that if slope R-NIR ≥thd6, it is a red tide object; otherwise, it is not a red tide object.

2. The method for extracting red tide based on two-dimensional spatial characteristic factors of blue, green, red and near-infrared spectra according to claim 1 is characterized in that: The calculation method of the first distance factor h_R is as follows: h_R=L R ×S R Among them, L R Represents the distance from the scattered points in the red band to the connecting line of the scattered points in the green-near infrared band in the two-dimensional spectral space; S R Indicates L R The sign of is positive if the red band scatter point is above the line connecting the green and near-infrared band scatter points, and negative if it is below the line; S R ={f(red)-{[(λ red -l green )÷(λ nir -l green )]×[f(nir)-f(green)]+f(green)}} ÷|f(red)-{[(λ red -λ green )÷(λ nir -λ green )]×[f(nir)-f(green)]+f(green)}|where f(x) indicates the band value of the x band, x is blue for the blue band, x is green for the green band, x is red for the red band, and x is nir for the near infrared band; λ x Represents the wavelength of the x-band in nm.

3. The red tide extraction method based on two-dimensional spatial characteristic factors of blue, green, red and near-infrared spectra according to claim 1 is characterized in that: The calculation method of the second distance factor h_G is as follows: h_G=L G ×S G Among them, L G Represents the distance from the green band scattered point to the blue-red band scattered point line in the two-dimensional spectral space; S G Indicates L G The sign of the green band is positive if the scattered point is above the line connecting the blue and red band scattered points, and negative if it is below the line; S G ={f(green)-{[(λ green -l blue )÷(λ ren -l blue )]×[f(red)-f(blue)]+f(blue)}}÷ |f(green)-{[(λ green -l blue )÷(λ red -l blue )]×[f(red)-f(blue)]+f(blue)}|。 4. The method for extracting red tide based on two-dimensional spatial characteristic factors of blue, green, red and near-infrared spectra according to claim 1 is characterized in that: The first slope factor slope G-R The calculation method is as follows: slope G-R =[f(green)-f(red)]÷[λ green -λ red ]; Among them, f(green) represents the band value of the green band, f(red) represents the band value of the red band, and λ green Indicates the wavelength of the green band, λ red Indicates the wavelength of the red band.

5. The method for extracting red tide based on two-dimensional spatial characteristic factors of blue, green, red and near-infrared spectra according to claim 1 is characterized in that: The second slope factor slope R-NIR The calculation method is as follows: slope R-NIR =[f(red)-f(nir)]÷[λ red -l nir ] Wherein, f(red) represents the band value of the red band, f(nir) represents the band value of the near infrared band, and the λ red Indicates the wavelength of the red band, λ nir Indicates the wavelength of the near-infrared band.

6. The method for extracting red tide based on two-dimensional spatial characteristic factors of blue, green, red and near-infrared spectra according to claim 1 is characterized in that: thd1 - thd6 will vary with the image data of different satellite sensors.

7. A red tide extraction system based on two-dimensional spatial characteristic factors of blue, green, red and near-infrared spectra, characterized in that: Applied to the red - tide extraction method based on the two - dimensional spatial characteristic factors of blue, green, red, and near - infrared spectra described in any one of claims 1 - 6, including a first calculation module, a second calculation module, a third calculation module, a fourth calculation module, and a red - tide extraction module; The first calculation module is used to calculate a first distance factor h_R of the line connecting the red band coordinate point to the green - near infrared band coordinate point in the wavelength - band value two - dimensional spectral space; The second calculation module is used to calculate a second distance factor h_G of the line connecting the green band coordinate point to the blue - red band coordinate point in the wavelength - band value two - dimensional spectral space; The third calculation module is used to calculate the first slope factor slope of the line connecting two points of the green-red band in the wavelength-band value two-dimensional spectral space G-R ; The fourth calculation module is used to calculate the second slope factor slope of the line connecting two points in the red-near infrared band in the wavelength-band value two-dimensional spectral space R-NIR ; The red tide extraction module is used to extract the red tide based on the first distance factor h_R, the second distance factor h_G, and the first slope factor slope G-R and the second slope factor slope R-NIR Through comprehensive factor superposition and step-by-step threshold judgment, the rapid and automatic extraction of the location and range of red tides can be achieved; Through comprehensive factor superposition and step-by-step threshold judgment, the rapid and automatic extraction of the location and range of red tides can be achieved, specifically: Judging the logical relationship between the first distance factor h_R and the threshold thd1. If h_R > thd1, it is a non - red - tide object; otherwise, continue to judge; Judging the logical relationship between the second distance factor h_G and the threshold thd2. If h_G ≤ thd2, it is a red - tide object; otherwise, continue to judge; Judging the logical relationship between the second distance factor h_G and the thresholds thd3 and thd4. If thd3 < h_G < thd4, continue to the next step of judgment; otherwise, it is a non - red - tide object; Determine the first slope factor slope G-R The logical relationship with the threshold thd5 is that if slope G-R ≥thd5, it is not a red tide object; otherwise, continue to judge; Determine the second slope factor slope R-NIR The logical relationship with the threshold thd6 is that if slope R-NIR ≥thd6, it is a red tide object; otherwise, it is not a red tide object.

8. An electronic device, characterized in that: The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores computer program instructions executable by the at least one processor, and the computer program instructions are executed by the at least one processor so that the at least one processor can execute the red - tide extraction method based on the two - dimensional spatial characteristic factors of blue, green, red, and near - infrared spectra described in any one of claims 1 - 6.

9. A computer-readable storage medium storing a program, characterized in that: When the program is executed by the processor, it implements the red - tide extraction method based on the two - dimensional spatial characteristic factors of blue, green, red, and near - infrared spectra described in any one of claims 1 - 6.

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