A nighttime urban remote sensing imaging method based on adaptive series TDI technology

Abstract

The present invention relates to the field of image recognition technology, and specifically to a nighttime urban remote sensing imaging method based on adaptive series TDI technology, comprising the following steps: establishing a brightness characterization value model; obtaining a brightness characterization value by quantifying the amount of photogenerated charge received by a detector pixel; obtaining a maximum brightness characterization value allowed by the detector through full well capacity calibration, and determining a brightness characterization threshold in combination with a set safety factor; establishing an adaptive series calculation model to achieve dynamic optimization of the TDI integral series; integrating and accumulating the signal according to the determined integral series, and outputting the signal, thereby effectively avoiding overexposure problems in highlight areas, enhancing the ability to capture dark details, and widening the dynamic range of remote sensing images.

Description

Technical Field

[0001] The present invention relates to the field of image recognition technology, and in particular to a nighttime urban remote sensing imaging method based on adaptive series TDI technology. Background Art

[0002] In remote sensing imaging systems, time delay integration (TDI) technology has been widely used in high-speed push-broom remote sensing satellite cameras because it can effectively improve the imaging signal-to-noise ratio (SNR) and imaging clarity. TDI technology uses multiple levels of integration, such as Figure 1 The figure shows the principle diagram of TDI technology, where n is the integral series. The target's projection on the detector's image plane moves a pixel-sized distance during each line period. The charge of the target scene on multiple rows of pixels is synchronously transferred and accumulated, thereby enhancing weak signals and improving image quality. This is particularly suitable for low-light imaging on high-speed moving platforms. However, traditional TDI remote sensing systems have the following technical problems:

[0003] TDI has a fixed integration order, lacking flexibility and adaptability. The integration order of currently used TDI CCD or CMOS detectors is fixed during the hardware design phase and is often limited by the number of detector rows or wiring structure. This makes it impossible to flexibly adjust the order based on the brightness characteristics of the target scene during the execution of the task. This severely limits imaging performance in urban night scenes with a wide dynamic range of brightness.

[0004] Bright areas are prone to overexposure, making it difficult to preserve details in dark areas. Nighttime urban remote sensing scenes often feature both strong light sources (such as streetlights and vehicle lights) and low-reflectivity areas (such as grass, parks, and open spaces), resulting in a wide dynamic range. When capturing bright areas with a fixed TDI level, excessive charge accumulation can lead to saturation and overexposure. In dark areas, underexposure weakens the signal, resulting in a loss of detail and a reduction in overall image quality.

[0005] In view of this, the present invention provides a nighttime urban remote sensing imaging method based on adaptive series TDI technology. Summary of the Invention

[0006] The purpose of the present invention is to provide a nighttime urban remote sensing imaging method based on adaptive series TDI technology to solve the defects of poor image uniformity and insufficient ability to capture dark details in the existing technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a nighttime urban remote sensing imaging method based on adaptive series TDI technology, comprising the following steps:

[0009] Step S1: Establishing a brightness characterization value model to quantify the amount of photogenerated charge received by the detector pixel and obtain a brightness characterization value;

[0010] Step S2: Obtain the maximum brightness characterization value allowed by the detector through full well capacity calibration, and determine the brightness characterization threshold value in combination with a set safety factor;

[0011] Step S3: Establishing an adaptive series calculation model to achieve dynamic optimization of the TDI integral series;

[0012] Step S4: Integrate and accumulate the signal according to the determined integration level, and output the signal.

[0013] As a preferred implementation of the first aspect of the present invention, the construction logic of the brightness representation value model is:

[0014] Step S101: converting the actual brightness into the amount of photogenerated charge received by the pixel based on the TDI satellite camera detector;

[0015] Step S102: establishing a charge-to-voltage mapping relationship to convert the photogenerated charge obtained in step S101 into a measurable analog voltage signal;

[0016] Step S103: converting the analog voltage signal into a digital voltage signal through an analog-to-digital converter, and using the digital voltage signal to represent the brightness representation value.

[0017] As a preferred embodiment of the first aspect of the present invention, the derivation formula for converting actual brightness into photogenerated charge received by a pixel is:

[0018] ;

[0019] in: is the amount of photogenerated charge; is the detector quantum efficiency; is the wavelength of incident light; is Planck's constant; is the speed of light; are all physical constants; is the incident light power; is the single-stage TDI integration time; is the pixel photosensitive area; in the TDI imaging process, each level of TDI integration time will integrate the photoelectric signal once when the target image moves along the detector

[0020] As a preferred embodiment of the first aspect of the present invention, the formula for converting the photogenerated charge into an analog voltage signal is:

[0021] ;

[0022] in: is an analog voltage signal; is the amount of photogenerated charge; is the integrating capacitor, is the amplifier gain.

[0023] As a preferred embodiment of the first aspect of the present invention, the analog voltage signal is converted into a brightness representation value by an analog-to-digital converter. , satisfying the following relationship:

[0024] ;

[0025] in: is the brightness representation value; is an analog voltage signal; Refers to the least significant bit voltage of the analog-to-digital converter, and k is the proportional coefficient;

[0026] Defining Detector Response Factors , then the brightness representation value formula is equivalently replaced by:

[0027] ;

[0028] Convert the brightness representation value and the amount of photogenerated charge into a linear relationship.

[0029] As a preferred embodiment of the first aspect of the present invention, the full well capacity of the detector is determined by a ground strong light calibration experiment. And the corresponding maximum brightness representation value , the two still satisfy the relationship:

[0030] ;

[0031] Set safety factor The brightness characterization threshold is: , It is a constant with a value range of 0.7-0.9.

[0032] As a preferred implementation of the first aspect of the present invention, the construction logic of the adaptive series calculation model is:

[0033] Step S301: performing image preprocessing on the pixel brightness representation values in the image collected by each row of detectors to eliminate the detector dark current noise, wherein the dark current noise is measured by the ground detector calibration experiment;

[0034] ;

[0035] in: For this line The brightness representation value of each pixel, is the brightness representation value of the pixel after correction; is the detector dark current noise; in addition, the brightness representation value of the pixel after correction is greater than or equal to zero;

[0036] Step S302: averaging multiple pixels in a single row to obtain an average brightness representation value;

[0037] Step S303: Calculate the maximum allowable integral limit, and ensure that the product of the allowed TDI integral level and the average brightness representation value is less than the brightness representation threshold, and the TDI integral level is not greater than the maximum level supported by the detector hardware;

[0038] Step S304: Add the noise estimation term to the series decision in step S303, comprehensively consider the brightness threshold and the brightness noise standard deviation, and the final TDI integral series calculation formula is:

[0039] ;

[0040] in: is the brightness characterization threshold; is the average brightness representation value; is the standard deviation of brightness noise, obtained by fitting the ground experiment; when the average brightness characterization value is zero, the TDI integral level is the maximum TDI integral level, maximizing the signal accumulation.

[0041] As a preferred implementation manner of the first aspect of the present invention, the output signal is:

[0042] ;

[0043] ;

[0044] in: For the The first The brightness representation value of the pixel after correction, For the The first step in the integration process The amount of photogenerated charge generated by each pixel; Output signal

[0045] In a second aspect, the present invention provides an electronic device, comprising:

[0046] at least one processor; and,

[0047] A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the first aspect.

[0048] In a third aspect, the present invention provides a computer-readable storage medium storing instructions, which, when executed on a computer, causes the computer to execute the method described in the first aspect.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] By constructing a brightness representation value model and introducing an adaptive TDI integral progression adjustment mechanism based on average brightness, this method achieves dynamic response to scenes of varying brightness, significantly improving the overall quality of nighttime remote sensing imagery. Compared to traditional fixed-progress TDI methods, the proposed scheme effectively avoids overexposure in bright areas while enhancing the ability to capture dark details and broadening the dynamic range of remote sensing imagery. This method boasts a simple structure and efficient computation, making it easy to integrate into existing remote sensing imaging systems. It is particularly suitable for imaging tasks with drastic brightness variations, such as urban nightscapes. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a schematic diagram of the TDI series principle in the background technology of the present invention;

[0052] Figure 2 This is a flow chart of the nighttime urban remote sensing imaging method of the present invention;

[0053] Figure 3 For the present invention, it is assumed that a satellite takes a scene picture;

[0054] Figure 4 This is a schematic diagram of different brightness areas of a scene simulated using different color blocks and the letter "a" in the present invention;

[0055] Figure 5 This is a schematic diagram of brightness representation values corresponding to different pixels in the satellite push-scan direction of the present invention;

[0056] Figure 6 This is a schematic diagram of satellite push-broom imaging simulation after the dynamic optimization of TDI integration series in the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] In the description of the present invention, it should be noted that the terms "vertical", "up", "down", "horizontal", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limiting the present invention.

[0059] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0060] Example 1

[0061] See also Figure 2 The present invention provides a technical solution: a nighttime urban remote sensing imaging method based on adaptive series TDI technology. The method uses brightness representation values to represent photogenerated charge and actual brightness. During satellite push-scan imaging, the average brightness representation value of each row is calculated, and the integration series of the row signal is optimized in real time based on the value. This method improves low-light imaging quality while avoiding signal saturation. The method includes the following steps:

[0062] Step S1: Establishing a brightness characterization value model to quantify the amount of photogenerated charge received by the detector pixel and obtain a brightness characterization value;

[0063] It should be noted that the brightness representation value model includes establishing a mapping relationship between "actual scene brightness ↔ photogenerated charge ↔ analog voltage signal ↔ digital brightness value"; mapping the incident light intensity (brightness) in the physical world into a digital signal that can be used for algorithm processing, providing a basis for subsequent dynamic series decision-making.

[0064] Specifically, the construction logic of the brightness representation value model is:

[0065] Step S101: Based on the amount of photogenerated charge received by the TDI satellite camera detector pixel The derived formula establishes the relationship between the amount of photogenerated charge and the actual scene brightness. The higher the actual scene brightness, the greater the light power incident on the detector pixel per unit time, and the amount of photogenerated charge stimulated also increases accordingly. Therefore, through physical modeling, the incident light power can be converted into the amount of photogenerated charge, achieving a basic mapping between the actual scene brightness and the amount of photogenerated charge.

[0066] To further explain: The derivation formula for the amount of photogenerated charge received by the TDI satellite camera detector pixel is:

[0067]

[0068] in: is the amount of photogenerated charge; is Planck's constant; is the speed of light; are all physical constants; is the detector quantum efficiency; is the pixel sensitive area, both of which are determined by the precision of the satellite camera; is the wavelength of incident light; is the incident light power, and both are related to the actual brightness of the scene captured by the camera. For the same TDI satellite camera, the greater the actual brightness of the scene, the higher the incident light power, and the amount of photogenerated charge received by the pixel. The more; = is the single-stage TDI integration time. During TDI imaging, each stage integrates the photoelectric signal as the target image moves along the detector. Step S102: Establishing a charge-to-voltage mapping relationship converts the photoelectric signal into a measurable electrical signal. The detector internally converts the photogenerated charge into an analog voltage signal via an integrating capacitor and an amplifier circuit. The greater the charge, the higher the voltage output, paving the way for subsequent digital quantization.

[0069] Further explanation: The photogenerated charge is converted from charge to voltage and then output as an analog voltage signal.

[0070]

[0071] in: is an analog voltage signal; is the amount of photogenerated charge; is the integrating capacitor, is the amplifier gain.

[0072] Step S103: Establish a voltage-to-digital brightness conversion mechanism. Using an analog-to-digital converter (ADC), the analog voltage signal is converted into a digital signal, generating a "brightness representation value." This brightness representation value directly participates in subsequent image processing and integral series determination, serving as the final output of the entire brightness mapping process. This completes the physical-circuit-algorithm mapping chain from "actual brightness to digital value."

[0073] Further explanation: The analog voltage signal is converted into a brightness representation value through an analog-to-digital converter , satisfying the following relationship:

[0074] ;

[0075] in: Refers to the least significant bit voltage of the analog-to-digital converter, and k is the proportional coefficient. Define the detector response coefficient , then the above formula can be written as:

[0076] ;

[0077] In summary, the relationship between the brightness representation value and the amount of photogenerated charge is converted into a linear relationship. The greater the actual scene brightness, the higher the amount of photogenerated charge and the higher the brightness representation value. The size of the brightness representation value can reflect the amount of photogenerated charge and the actual brightness. The accumulation process of photogenerated charge can be represented by accumulating the brightness representation value.

[0078] Step S2: Obtain the maximum brightness characterization value allowed by the detector through full well capacity calibration, and determine the brightness characterization threshold value in combination with a set safety factor;

[0079] It should be noted that to avoid signal saturation in bright areas, the maximum brightness value corresponding to the detector pixel's maximum charge (full well capacity) was calibrated in ground-based experiments. A safety factor was introduced to set a brightness threshold. This threshold serves as a reference for determining whether a certain area is too bright and is used to dynamically adjust the TDI integration level.

[0080] Specifically, the full well capacity of the detector is determined through ground-based strong-light calibration experiments. And the corresponding maximum brightness representation value , the two still satisfy the relationship:

[0081] ;

[0082] In order to alleviate the overexposure problem in the highlight area, a safety factor is set here. The brightness characterization threshold is: , is a constant, and the safety factor can be determined according to the actual situation. The general range is 0.7-0.9. In the following calculation, . Establish a strict series decision formula to ensure that the signal accumulation is within the physically feasible range.

[0083] Step S3: Establishing an adaptive series calculation model to achieve dynamic optimization of the TDI integral series;

[0084] Specifically, the construction logic of the adaptive series calculation model is:

[0085] Perform image preprocessing on the pixel brightness representation values in the image collected by each row of detectors to eliminate the detector dark current noise , the dark current noise is measured by ground detector calibration experiment:

[0086] ;

[0087] in: For this line The brightness representation value of each pixel, is the brightness representation value of the pixel after correction; is the detector dark current noise, the above formula ensures that , to avoid the negative brightness representation value affecting the integral series calculation;

[0088] For each row of images collected by the detector, the average brightness representation value of each row of images is calculated; the average brightness representation value The formula is:

[0089] ;

[0090] in: is the number of pixels in a single row. The maximum TDI integral level N allowed for this row should satisfy:

[0091] ;

[0092] at the same time: ( is the maximum number of stages supported by the detector hardware), and a noise estimation term is added to the stage decision, where ⌊ ⌋ is the floor symbol:

[0093] ;

[0094] in: is the brightness characterization threshold; is the average brightness representation value; is the brightness noise standard deviation, which can be obtained by fitting the ground experiment. = 0, when the scene is completely black, forced To maximize signal accumulation. Based on the above formula, the TDI integration level N will increase with the average brightness representation value of each line of image. changes with the change of N, achieving the dynamic optimization effect of the integral series N (such as the low light area , integral series , strong light area , integral series ), avoiding local overexposure or underexposure caused by global levels or fixed channel levels in traditional methods, and effectively improving the low-light imaging capability of satellite cameras.

[0095] The ratio between the average brightness characterization value and the brightness characterization value threshold is extracted based on the adaptive series calculation model to achieve dynamic optimization of the TDI integral series.

[0096] Example: Assume a TDI satellite camera with a maximum integration level of 16 (i.e. = 16), the brightness characterization value model has been established and the full well capacity calibration has been performed, that is, steps S1 and S2 have been completed, wherein the full well capacity brightness characterization value = 400, safety factor , then the threshold is , shoot the following hypothetical scene as Figure 3 As shown:

[0097] Different color blocks are used to represent different brightness areas of the scene, and the letter "a" is used to represent the image details that the satellite can capture at this brightness. It can be noticed that in the darker color blocks, the feature "a" is relatively blurred, which requires the satellite to have a strong ability to capture dark details in order to invert a clearer image. Different color blocks and the letter "a" are used to simulate different brightness areas of the scene and the corresponding features. Figure 4 As shown, along the satellite sweep direction, the details of the brightness representation values corresponding to different pixels are as follows Figure 5 As shown in the figure, the satellite sweep direction is from left to right. The hypothetical scene contains 10 rows, each row corresponds to 12 detectors, that is, 12 pixels. The corrected brightness representation values of the pixels corresponding to different color blocks have been marked in the color blocks. The brightness representation values corresponding to the color block colors from light to dark are 200, 160, 120, 100, 80, 40, and 10.

[0098] For the images collected by each row of detectors, calculate the average brightness representation value , and the cumulative number of times N of different rows is obtained as shown in the following table. The noise estimation item is not considered during the calculation:

[0099]

[0100] After dynamically optimizing the TDI integration series and accumulating the integration of each row, the satellite image is shown in step S4.

[0101] Step S4: Integrate and accumulate the signal according to the determined integration level, and output the signal.

[0102] Specifically, the output signal is:

[0103] ;

[0104] ;

[0105] in: For the The first step in the integration process The brightness representation value of the pixel after correction, For the The first step in the integration process The amount of photogenerated charge generated by each pixel; is the output signal.

[0106] Based on the example in step S3, it is assumed that the brightness of the scene remains unchanged during the satellite push-scan process, that is, the brightness representation value of the i-th pixel after correction is the same during each integration process, that is, the amount of photogenerated charge generated is the same, and the final simulated image after signal accumulation is as follows Figure 6 As shown in the figure, the details of the dark area are much clearer after accumulation.

[0107] This embodiment establishes a brightness characterization value model, full-well capacity calibration, and adaptive integral series calculation to achieve dynamic optimization of the integral series of the TDI satellite camera, broaden the dynamic range of the dynamic camera, and improve the camera's ability to capture dark details and urban low-light imaging capabilities, making it particularly suitable for nighttime urban scenes.

[0108] Example 2

[0109] The parts not described in this embodiment are the same as those in Embodiment 1. This embodiment shows an electronic device, including:

[0110] at least one processor; and,

[0111] A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the nighttime urban remote sensing imaging method based on the adaptive series TDI technology described in the first aspect.

[0112] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memories store at least one computer program, and the at least one computer program is loaded and executed by the processor to implement a nighttime urban remote sensing imaging method based on adaptive series TDI technology provided by the above-mentioned various method embodiments.

[0113] The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also include components such as a wired or wireless network interface and an input / output interface for input and output.

[0114] Example 3

[0115] The parts not described in this embodiment are as in Example 1. This embodiment also provides a computer-readable storage medium storing instructions. When the instructions are executed on a computer, the computer executes the nighttime urban remote sensing imaging method based on the adaptive series TDI technology.

[0116] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0117] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0118] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A nighttime urban remote sensing imaging method based on adaptive series TDI technology, characterized by: The following steps are involved: Step S1: Establishing a brightness characterization value model to quantify the amount of photogenerated charge received by the detector pixel and obtain a brightness characterization value; Step S2: Obtain the maximum brightness characterization value allowed by the detector through full well capacity calibration, and determine the brightness characterization threshold value in combination with a set safety factor; Step S3: Establishing an adaptive series calculation model, the adaptive series calculation model includes performing dark current noise correction on the brightness representation value of each row of pixels in the image collected by the detector, calculating the average brightness representation value of each row of pixels, and determining the TDI integral series allowed for each row of pixels based on the brightness representation threshold and the noise estimation term, thereby achieving dynamic optimization of the TDI integral series; Step S4: integrating and accumulating the photogenerated charge corresponding to the corrected brightness characterization value according to the integration order determined in step S3, and outputting a signal.

2. The nighttime urban remote sensing imaging method based on adaptive series TDI technology according to claim 1, characterized in that: The construction logic of the brightness representation value model is: Step S101: converting the actual brightness into the amount of photogenerated charge received by the pixel based on the TDI satellite camera detector; Step S102: establishing a charge-to-voltage mapping relationship to convert the photogenerated charge obtained in step S101 into a measurable analog voltage signal; Step S103: converting the analog voltage signal into a digital voltage signal through an analog-to-digital converter, and using the digital voltage signal to represent the brightness representation value.

3. The nighttime urban remote sensing imaging method based on adaptive series TDI technology according to claim 2, characterized in that: The formula for converting actual brightness into the amount of photogenerated charge received by the pixel is: ； in: is the photogenerated charge, is the detector quantum efficiency, is the wavelength of incident light, is Planck's constant, is the speed of light, both are physical constants; is the incident light power; is the single-stage TDI integration time; is the photosensitive area of the pixel; in the TDI imaging process, when the target image moves along the detector, the photoelectric signal is integrated once within each level of TDI integration time.

4. The nighttime urban remote sensing imaging method based on adaptive series TDI technology according to claim 3, characterized in that: The formula for converting the photogenerated charge into an analog voltage signal is: ； in: is an analog voltage signal; is the amount of photogenerated charge; is the integrating capacitor, is the amplifier gain.

5. The nighttime urban remote sensing imaging method based on adaptive series TDI technology according to claim 4, characterized in that: Convert the analog voltage signal into a brightness representation value through an analog-to-digital converter , satisfying the following relationship: ； in: is the brightness representation value; is an analog voltage signal; Refers to the least significant bit voltage of the analog-to-digital converter, and k is the proportional coefficient; Defining Detector Response Factors , then the brightness representation value formula is equivalently replaced by: ； Convert the brightness representation value and the amount of photogenerated charge into a linear relationship.

6. The nighttime urban remote sensing imaging method based on adaptive series TDI technology according to claim 5, characterized in that: Determine the detector's full well capacity through ground-based strong-light calibration experiments And the corresponding maximum brightness representation value , the two still satisfy the relationship: ； Where: Set safety factor , the brightness characterization threshold is: , It is a constant with a value range of 0.7-0.

9.

7. The nighttime urban remote sensing imaging method based on adaptive series TDI technology according to claim 6, characterized in that: The construction logic of the adaptive series calculation model is: Step S301: performing image preprocessing on the brightness representation values of the pixels in the image collected by each row of detectors to eliminate the detector dark current noise, wherein the dark current noise is measured by the ground detector calibration experiment; ； in: For each row The brightness representation value of each pixel, is the brightness representation value of the pixel after correction; is the detector dark current noise; in addition, the brightness representation value of the pixel after correction is greater than or equal to zero; Step S302: averaging multiple pixels in a single row to obtain an average brightness representation value; Step S303: Calculate the maximum allowable integral limit, the product of the TDI integral level allowed for a single row in step S302 and the average brightness representation value is less than the brightness representation threshold, and the TDI integral level is not greater than the maximum level supported by the detector hardware; Step S304: Add the noise estimation term to the series decision in step S303, comprehensively consider the brightness characterization threshold and the brightness noise standard deviation, and the final TDI integral series calculation formula is: ； in: is the average brightness representation value; is the standard deviation of brightness noise, obtained by fitting through ground experiments; The maximum TDI integral level supported by the detector hardware. When the average brightness representation value is zero, the maximum value of the TDI integral level N is the maximum TDI integral level. , maximizes signal accumulation.

8. The nighttime urban remote sensing imaging method based on adaptive series TDI technology according to claim 7, characterized in that: The output signal is: ； ； in: For the The first step in the integration process The brightness representation value of the pixel after correction, For the The first step in the integration process The amount of photogenerated charge generated by each pixel; is the output signal.

9. An electronic device, characterized in that: include: at least one processor; as well as, A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor so as to enable the at least one processor to execute a nighttime urban remote sensing imaging method based on adaptive series TDI technology as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the nighttime urban remote sensing imaging method based on the adaptive series TDI technology as described in any one of claims 1 to 8.

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