Infrared spectrum image substance identification method

Abstract

The invention provides an infrared spectrum image substance identification method which comprises the following steps: S1, acquiring and storing a plurality of interference images to form an interference data cube; s2, converting the interference data cube into a brightness temperature spectrum data cube; s3, replacing a brightness temperature curve of an invalid point in the brightness temperature spectral data cube with interpolation of brightness temperature curves of pixel points around the invalid point to obtain a brightness temperature spectral data cube after bad element processing; the invalid points are determined through pre-bad element calibration; s4, performing standard normal transformation and multi-size feature extraction on the brightness temperature spectral data cube after bad element processing to obtain a feature spectral data cube; and S5, calculating a correlation coefficient of the characteristic spectrum data cube and the standard library subjected to airspace interception sampling, and obtaining an identification result according to the correlation coefficient. The method can effectively reduce the substance detection false alarm rate of the identification result.

Description

Technical Field

[0001] The invention belongs to the field of spectrum analysis, and in particular relates to a method for identifying substances using infrared spectrum images. Background Art

[0002] Infrared spectroscopy refers to the infrared absorption spectrum obtained by recording the absorption conditions of the molecules based on the characteristics of the transition of vibrational energy levels and rotational energy levels in the molecules caused by the selective absorption of certain wavelengths of infrared rays by the molecules. The corresponding type of substance can be accurately identified by comparing the infrared absorption spectrum with the standard spectrum. Nowadays, infrared spectroscopy material identification has been widely used in many fields. However, when the infrared spectrometer is actually used to collect the infrared spectrum of the test sample, due to the limited process level of the detector in the infrared spectrometer, there will be a certain number of bad elements on the detector, resulting in many invalid points in the corresponding brightness temperature spectrum data, which in turn leads to an increase in the false alarm rate of the material detection in the final identification result, reducing the accuracy of the identification result. Summary of the invention

[0003] The purpose of the present invention is to provide a method for material recognition using infrared spectrum images, so as to reduce the false alarm rate of material detection and improve the accuracy of recognition results.

[0004] In order to solve the above technical problems, the present invention provides a method for identifying substances using infrared spectrum images, comprising: S1, collecting and storing a number of interference images to form an interference data cube; S2, converting the interference data cube into brightness temperature spectrum data cube; S3, replacing the brightness temperature curve of the invalid point in the brightness temperature spectrum data cube with the interpolation of the brightness temperature curves of the pixel points around the invalid point, to obtain the brightness temperature spectrum data cube after bad element processing; the invalid point is determined by pre-bad element calibration; S4, performing standard normal transformation and multi-size feature extraction on the brightness temperature spectral data cube after bad element processing to obtain a characteristic spectral data cube; S5. Calculate the correlation coefficient between the characteristic spectrum data cube and the standard library sampled by spatial domain interception, and obtain the recognition result according to the correlation coefficient. According to the above scheme, the buffer area for storing the interference image adopts a first-in-first-out strategy. Assuming that the size of the buffer area is 2n frames, the formation process of the interference data cube includes: S101, when the mth external trigger signal is received, buffering n frames of interference images in a buffer area; S102, extract all 2n interference images in the buffer area, compare each of the 2n interference images with the mth interference image in the interference data cube acquired last time. Correlation matching is performed respectively, and then the frame with the highest correlation among the 2n frames of interference images is selected. Stored in the mth frame of the interference data cube acquired this time.

[0005] According to the above scheme, step S2 includes: S201, performing registration, alignment, cropping and Fourier transformation on the interference image in the interference data cube to obtain an original spectral data cube; S202, performing radiation calibration on the original spectral data cube according to pre-calibrated data to obtain a radiance spectral data cube; S203, converting the radiance spectrum data cube into the brightness temperature spectrum data cube.

[0006] According to the above scheme, the registration, alignment and cropping includes: S2011, averaging all interference images in the interference data cube to obtain an interference image background; S2012, determining feature points of the interference image according to the interference image background, the interference image, and the size of the interference image; S2013. According to the coordinates of the feature points in the interferometer scanning direction during acquisition, the interference image is registered, aligned and cropped.

[0007] According to the above scheme, the Fourier transform in step S201 is a fast Fourier transform; in step S203, the radiance spectrum data cube is converted into the brightness temperature spectrum data cube using the Planck formula.

[0008] According to the above scheme, the bad element calibration includes: S301, collecting brightness temperature spectrum data cube of a black body at a certain temperature; S302, determining whether a point in the blackbody brightness temperature spectrum data cube is an invalid point based on the mean, standard deviation, and a set threshold of the brightness temperature curve of the point, and treating the detection point on the detector corresponding to the invalid point as a spectrum bad element.

[0009] According to the above scheme, the airspace intercept sampling includes: S501, performing inverse Fourier transform on the spectrum curve of the standard library to obtain the interference curve of the standard library; S502, performing wavelet denoising on the interference curve of the standard library; S503, intercepting the interference curve of the standard library after wavelet denoising according to the set spectral resolution before and after interception; S504, performing Fourier transform on the intercepted interference curve of the standard library to obtain a standard library that has been sampled by spatial domain interception.

[0010] The present invention also provides an infrared spectrum image material identification device, comprising: An acquisition module, used for acquiring interference images; A storage module, used for storing interference images and forming interference data cubes; Brightness temperature spectrum data cube conversion module, used to convert interference data cube into brightness temperature spectrum data cube; A bad pixel processing module is used to replace the brightness temperature curve of the invalid point in the brightness temperature spectrum data cube with the interpolation of the brightness temperature curve of the pixel points around the invalid point to obtain the brightness temperature spectrum data cube after bad pixel processing; the invalid point is determined by pre-bad pixel calibration; The characteristic spectrum data cube conversion module is used to perform standard normal transformation and multi-size feature extraction on the brightness temperature spectrum data cube after bad element processing to obtain the characteristic spectrum data cube; The recognition module is used to calculate the correlation coefficient between the characteristic spectrum data cube and the standard library sampled by spatial domain interception, and obtain the recognition result according to the correlation coefficient.

[0011] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the infrared spectral image material identification method described above when executing the computer program.

[0012] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the infrared spectral image material identification method described above are implemented.

[0013] The beneficial effect of the present invention is that the present invention replaces the brightness temperature curve corresponding to the spectral bad element in the brightness temperature spectral data by performing bad element calibration, effectively eliminates and supplements the invalid data in the spectral data, thereby reducing the false alarm rate of material detection in the recognition result and improving the recognition accuracy.

[0014] Furthermore, the present invention detects invalid points according to the brightness temperature curve characteristics of the brightness temperature spectrum data, and then screens out the spectral bad elements corresponding to the invalid points. Compared with the traditional bad element calibration method, this method can screen out the spectral bad elements more comprehensively.

[0015] Furthermore, when collecting interference images based on external trigger signals, the present invention effectively avoids interference image distortion caused by external trigger signal errors by performing correlation matching filtering on the interference images and interference images in the existing interference data cube. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the infrared spectrum image substance identification method according to the first embodiment of the present invention; Figure 2 is a flow chart of interference image acquisition and storage according to the first embodiment of the present invention; Figure 3 is a bad element processing flow chart of the first embodiment of the present invention; Figure 4 is an identification flow chart of the first embodiment of the present invention; Figure 5 is a schematic diagram of the interferometer scanning process of the first embodiment of the present invention; Figure 6 Schematic diagram of the storage method of the interference data cube according to the first embodiment of the present invention; Figure 7 is a schematic diagram of the registration, alignment and cutting process of the first embodiment of the present invention; Figure 8 It is a schematic diagram of the recognition result of the first embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings of the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0018] See also Figure 1 , Figure 4 This embodiment discloses a method for identifying substances using infrared spectroscopy images, including: S1, collecting and storing a number of interference images to form an interference data cube; S2, converting the interference data cube into brightness temperature spectrum data cube; S3, replacing the brightness temperature curve of the invalid point in the brightness temperature spectrum data cube with the interpolation of the brightness temperature curves of the pixel points around the invalid point, to obtain the brightness temperature spectrum data cube after bad element processing; the invalid point is determined by pre-bad element calibration; S4, performing standard normal transformation and multi-size feature extraction on the brightness temperature spectral data cube after bad element processing to obtain a characteristic spectral data cube; S5. Calculate the correlation coefficient between the characteristic spectrum data cube and the standard library sampled by spatial domain interception, and obtain the recognition result according to the correlation coefficient. Further, see Figure 2 The buffer area for storing the interference image adopts a first-in-first-out strategy. Assuming the size of the buffer area is 2n frames, the formation process of the interference data cube (which consumes CPU resources at this time) includes: S101, when the mth external trigger signal is received, buffering n frames of interference images in a buffer area; S102, extract all 2n interference images in the buffer area, compare each of the 2n interference images with the mth interference image in the interference data cube acquired last time. Correlation matching is performed respectively, and then the frame with the highest correlation among the 2n frames of interference images is selected. Stored in the mth frame of the interference data cube collected this time; Repeat the above steps until the interference data cube corresponding to this acquisition is filled; the storage format of the interference data cube can be found in Figure 6 ; In a preferred embodiment of the present invention, the interference data cube can be stored in a hard disk for correlation matching in subsequent acquisitions; compared with the conventional interference image acquisition method that only relies on an external trigger signal, the acquisition method adopted in this embodiment performs autocorrelation verification of the interference image based on an external trigger signal, which can effectively reduce the spectral image distortion caused by the external trigger signal error.

[0019] Furthermore, the step S2 comprises: S201, performing registration, alignment, cropping and Fourier transformation (fast Fourier transformation is used in this embodiment) on the interference image in the interference data cube to obtain an original spectral data cube; S202, performing radiation calibration on the original spectral data cube according to pre-calibrated data to obtain a radiance spectral data cube; S203, (in this embodiment, Planck's formula is used) converting the radiance spectrum data cube into the brightness temperature spectrum data cube.

[0020] Furthermore, the registration, alignment and cropping include: S2011, averaging all interference images in the interference data cube to obtain an interference image background; Assume that there are M frames of interference images in the interference data cube, and the width and height of the interference images are w and h respectively, then the interference image background It is expressed as:

[0021] S2012, determining feature points of the interference image according to the interference image background, the interference image, and the size of the interference image; S2013, according to the coordinates of the feature points in the interferometer scanning direction during acquisition, the interference image is aligned and cropped (see Figure 7 ); In this embodiment, the scanning direction of the interferometer when collecting the interference image is the x direction (see Figure 5 ), so the mth frame interference image The x-coordinate of the feature point It is expressed as:

[0022] In the above formula, i and j are the coordinates in the x-direction and the y-direction respectively.

[0023] Further, see Figure 3 , the bad element calibration includes: S301, collecting brightness temperature spectrum data cube of a black body at a certain temperature; S302, determining whether a certain point in the blackbody brightness temperature spectrum data cube is an invalid point based on the mean value, standard deviation, and a set threshold of the brightness temperature curve of the point, and taking the detection point on the detector corresponding to the invalid point as a spectrum bad element; An example is as follows: Assume that the spectrometer is aimed at a black body with a temperature of T℃, and the corresponding brightness temperature spectrum data cube is collected. The brightness temperature curve corresponding to the point (x, y) in the brightness temperature spectrum data cube is BT. The mean of BT obtained by calculation is meanT, and the standard deviation is stdT. If meanT or stdT does not satisfy the following formula, the pixel corresponding to (x, y) is considered to be an invalid point, and the detection point corresponding to the invalid point is a spectral bad element:

[0024]

[0025] In the above formula, thr1 and thr2 are set thresholds respectively, and the specific values ​​of thr1 and thr2 need to be determined according to the spectral resolution and other spectral characteristics of the spectrometer used.

[0026] Furthermore, the spatial domain intercept sampling includes: S501, performing inverse Fourier transform on the spectrum curve of the standard library to obtain the interference curve of the standard library; S502, performing wavelet denoising on the interference curve of the standard library; S503, intercepting the interference curve of the standard library after wavelet denoising according to the set spectral resolution before and after interception; Specifically, assuming that the spectral resolution before sampling is , the spectral resolution after sampling is , then the intercepted interference curve center accounts for the total length of the interference curve The part is taken as the intercepted spectrum curve; S504, performing Fourier transform on the intercepted interference curve of the standard library to obtain a standard library that has been sampled by spatial domain interception; Traditional sampling methods usually directly sample the standard substance library according to the spectral resolution of the characteristic spectral data cube, obtain the sampled standard substance library, and then calculate the correlation coefficient with the characteristic spectral data cube to finally obtain the detection result; direct sampling will lead to the loss of spectral information of the standard substance library, and then lead to a decrease in the detection effect; the spatial domain interception sampling method adopted in this embodiment can reduce the loss of standard library information caused by direct sampling, thereby improving the accuracy of material detection.

[0027] Using the above identification method, the substance identified in this embodiment is NH3 (see Figure 8 ).

[0028] Embodiment 2: The technical principle of this embodiment is basically the same as that of the first embodiment. On the basis of the first embodiment, this embodiment provides an infrared spectrum image material identification device, including: An acquisition module, used for acquiring interference images; A storage module, used for storing interference images and forming interference data cubes; Brightness temperature spectrum data cube conversion module, used to convert interference data cube into brightness temperature spectrum data cube; A bad pixel processing module is used to replace the brightness temperature curve of the invalid point in the brightness temperature spectrum data cube with the interpolation of the brightness temperature curve of the pixel points around the invalid point to obtain the brightness temperature spectrum data cube after bad pixel processing; the invalid point is determined by pre-bad pixel calibration; The characteristic spectrum data cube conversion module is used to perform standard normal transformation and multi-size feature extraction on the brightness temperature spectrum data cube after bad element processing to obtain the characteristic spectrum data cube; The recognition module is used to calculate the correlation coefficient between the characteristic spectrum data cube and the standard library sampled by spatial domain interception, and obtain the recognition result according to the correlation coefficient.

[0029] Embodiment three: This embodiment provides a computer device, such as a smart phone, tablet computer, laptop computer, desktop computer, rack server, blade server, tower server or cabinet server (including an independent server or a server cluster composed of multiple servers) that can execute programs. The computer device of this embodiment includes at least but is not limited to: a memory and a processor that can communicate with each other through a system bus.

[0030] In this embodiment, the memory (i.e., readable storage medium) includes flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM). The memory may also be an external storage device of a computer device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Of course, the memory may also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory is generally used to store the operating system and various application software installed on the computer device, such as the program code of the infrared spectral image material identification method in the first embodiment. In addition, the memory may also be used to temporarily store various data that have been output or are to be output.

[0031] In some embodiments, the processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor is generally used to control the overall operation of a computer device. In this embodiment, the processor is used to run the program code stored in the memory or process data, such as running the infrared spectral image material identification device to implement the infrared spectral image material identification method in the first method embodiment.

[0032] Embodiment 4: The present application also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, and the program realizes the corresponding function when executed by the processor. The computer-readable storage medium of this embodiment is used to store the program code of the infrared spectral image material identification method, and when executed by the processor, the infrared spectral image material identification method of embodiment one is realized.

[0033] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0034] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying substances using infrared spectral images, characterized in that: include: S1, collecting and storing a number of interference images to form an interference data cube; S2, converting the interference data cube into brightness temperature spectrum data cube; S3, replacing the brightness temperature curve of the invalid point in the brightness temperature spectrum data cube with the interpolation of the brightness temperature curves of the pixel points around the invalid point, to obtain the brightness temperature spectrum data cube after bad element processing; the invalid point is determined by pre-bad element calibration; S4, performing standard normal transformation and multi-size feature extraction on the brightness temperature spectral data cube after bad element processing to obtain a characteristic spectral data cube; S5. Calculate the correlation coefficient between the characteristic spectrum data cube and the standard library sampled by spatial domain interception, and obtain the recognition result according to the correlation coefficient.

2. The infrared spectrum image substance identification method according to claim 1, characterized in that: The buffer area for storing the interference image adopts a first-in-first-out strategy. Assuming that the size of the buffer area is 2n frames, the formation process of the interference data cube includes: S101, when the mth external trigger signal is received, buffering n frames of interference images in a buffer area; S102, extract all 2n interference images in the buffer area, compare each of the 2n interference images with the mth interference image in the interference data cube acquired last time. Correlation matching is performed respectively, and then the frame with the highest correlation among the 2n frames of interference images is selected. Stored in the mth frame of the interference data cube acquired this time.

3. The infrared spectrum image substance identification method according to claim 1, characterized in that: The step S2 comprises: S201, performing registration, alignment, cropping and Fourier transformation on the interference image in the interference data cube to obtain an original spectral data cube; S202, performing radiation calibration on the original spectral data cube according to pre-calibrated data to obtain a radiance spectral data cube; S203, converting the radiance spectrum data cube into the brightness temperature spectrum data cube.

4. The infrared spectrum image substance identification method according to claim 3, characterized in that: The registration, alignment and cropping comprises: S2011, averaging all interference images in the interference data cube to obtain an interference image background; S2012, determining feature points of the interference image according to the interference image background, the interference image, and the size of the interference image; S2013. According to the coordinates of the feature points in the interferometer scanning direction during acquisition, the interference image is registered, aligned and cropped.

5. The infrared spectrum image substance identification method according to claim 3, characterized in that: In step S201, the Fourier transform is a fast Fourier transform; in step S203, the radiance spectrum data cube is transformed into the brightness temperature spectrum data cube using the Planck formula.

6. The infrared spectrum image substance identification method according to claim 1, characterized in that: The bad element calibration includes: S301, collecting brightness temperature spectrum data cube of a black body at a certain temperature; S302, determining whether a point in the blackbody brightness temperature spectrum data cube is an invalid point based on the mean, standard deviation, and a set threshold of the brightness temperature curve of the point, and treating the detection point on the detector corresponding to the invalid point as a spectrum bad element.

7. The infrared spectrum image substance identification method according to claim 1, characterized in that: The spatial domain intercept sampling includes: S501, performing inverse Fourier transform on the spectrum curve of the standard library to obtain the interference curve of the standard library; S502, performing wavelet denoising on the interference curve of the standard library; S503, intercepting the interference curve of the standard library after wavelet denoising according to the set spectral resolution before and after interception; S504, performing Fourier transform on the intercepted interference curve of the standard library to obtain a standard library that has been sampled by spatial domain interception.

8. An infrared spectrum image material identification device, characterized in that: include: An acquisition module, used for acquiring interference images; A storage module, used for storing interference images and forming interference data cubes; Brightness temperature spectrum data cube conversion module, used to convert interference data cube into brightness temperature spectrum data cube; A bad pixel processing module is used to replace the brightness temperature curve of the invalid point in the brightness temperature spectrum data cube with the interpolation of the brightness temperature curve of the pixel points around the invalid point to obtain the brightness temperature spectrum data cube after bad pixel processing; the invalid point is determined by pre-bad pixel calibration; The characteristic spectrum data cube conversion module is used to perform standard normal transformation and multi-size feature extraction on the brightness temperature spectrum data cube after bad element processing to obtain the characteristic spectrum data cube; The recognition module is used to calculate the correlation coefficient between the characteristic spectrum data cube and the standard library sampled by spatial domain interception, and obtain the recognition result according to the correlation coefficient.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the infrared spectral image substance identification method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the infrared spectrum image substance identification method according to any one of claims 1 to 7 are implemented.

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