CN114677275A - High-frequency repeated staring imaging space-based remote sensing load on-orbit testing method - Google Patents
High-frequency repeated staring imaging space-based remote sensing load on-orbit testing method Download PDFInfo
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Abstract
The invention discloses an on-orbit testing method for a high-frequency repeated staring imaging space-based remote sensing load, which comprises the following steps: selecting a field meeting MTF and SNR test requirements, and laying a radiation characteristic target; monitoring solar spectrum information and earth radiation balance information, and measuring the reflectivity of the target and natural ground objects by using a spectrometer; based on synchronous atmospheric aerosol observation data, screening image data continuously observed by GF-4, and rejecting satellite image data obtained at atmospheric disturbance moment; obtaining MTF values of different frequencies in the cut-off frequency through calculation and processing of a linear diffusion function LSF; performing row-column vector statistical calculation on continuous multi-frame image data of the ground object of the uniform scene in a time dimension to obtain a root mean square error, and calculating an SNR (signal to noise ratio) based on the registered multi-frame image uniform ground object. The method can image a target area appointed by a user, and comprises the steps of carrying out high-frequency repeated staring observation on the target area, quickly carrying out splicing imaging on a certain area, and carrying out patrol imaging on a plurality of areas.
Description
Technical Field
The invention relates to the technical field of engineering for quantitatively and comprehensively evaluating the quality of remote sensing images, in particular to an on-orbit testing method for a high-frequency repeated staring imaging space-based remote sensing load.
Background
The GF-4 satellite is an important component of 'high-resolution special item' in China, aims to acquire multispectral and mid-infrared image information with extremely high time resolution and medium spatial resolution in the national range and in the peripheral region, can meet the wide requirements of users such as disaster reduction, forestry, meteorology and the like, has extremely high social and economic benefits, and has technical leading property and advancement. The satellite is provided with a staring camera set (visible light wave band and infrared wave band) with a geosynchronous orbit resolution of 50 meters, an imaging area is 7000km multiplied by 7000km, a single scene coverage area is 400km multiplied by 400km, a minute-level high-time-resolution remote sensing monitoring task can be realized, and the design life is 8 years.
The invention relates to a research on GF-4 satellite image quality evaluation technology. The quality of the satellite remote sensing image is directly related to the reliability and accuracy of the remote sensing image information acquisition, and the method is an important basis for the design of a remote sensing imaging system and the processing and application of the remote sensing information. Since the optical remote sensing imaging process is influenced by various factors such as atmosphere, irradiation, temperature, sensors, ground feature characteristics and the like, quantitative and comprehensive evaluation of the quality of the remote sensing image is a complex system engineering. In terms of the remote sensing image, the optical remote sensing image quality is generally expressed as radiation quality and geometric quality, wherein the radiation quality mainly comprises a Modulation Transfer Function (MTF) and a Signal to Noise Ratio (SNR), and is also an important basis for evaluating the optical remote sensing imaging quality. According to the overall scheme arrangement, the research is based on the characteristics of GF-4 satellite imaging, and on-orbit Modulation Transfer Function (MTF) and signal-to-noise ratio (SNR) test work is carried out.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a high-frequency repeated gaze imaging space-based remote sensing load on-orbit testing method, which can complete task maneuvering response within a few minutes, and image a target area specified by a user, wherein the quality of an optical remote sensing image is generally expressed as radiation quality and geometric quality as far as the remote sensing image is concerned, wherein the radiation quality mainly comprises a Modulation Transfer Function (MTF) and a Signal Noise Ratio (SNR), and is also an important basis for evaluating the quality of the optical remote sensing imaging, and the research provides a more efficient and accurate method for acquiring the MTF and the SAR.
The purpose of the invention is realized by the following technical scheme:
a high-frequency repeated staring imaging space-based remote sensing load on-orbit testing method comprises the following steps:
A. selecting a field meeting MTF and SNR test requirements, and laying a radiation characteristic target;
B. monitoring solar spectrum information and earth radiation balance information, and measuring the reflectivity of the target and the natural ground object by using a spectrometer;
C. based on synchronous atmospheric aerosol observation data, image data continuously observed by GF-4 are screened, and satellite image data obtained at atmospheric disturbance moment are eliminated;
D. Obtaining MTF values of different frequencies in the cut-off frequency by calculating and processing a line spread function LSF;
E. and performing column vector statistical calculation on continuous multi-frame image data of the ground object of the uniform scene in a time dimension to obtain a root mean square error, and calculating the SNR (signal to noise ratio) based on the registered multi-frame image uniform ground object.
One or more embodiments of the invention may have the following advantages over the prior art:
the method can quantitatively, comprehensively and efficiently evaluate the quality of the remote sensing image, can finish task maneuvering response within a few minutes, and can image a target area appointed by a user, including high-frequency repeated staring observation of the target area, rapid splicing imaging of a certain area, and inspection imaging of a plurality of areas.
Drawings
FIG. 1 is a flow chart of an on-orbit testing method for a high-frequency repeated staring imaging space-based remote sensing load;
FIG. 2 is an overview of the study area;
FIG. 3 is a flow chart of a pulse method MTF test based on GF-4 high-frequency repeated gazing observation data;
FIG. 4 is a schematic view of radiation target deployment;
FIG. 5 is a schematic diagram of high-resolution four-satellite high-frequency repeated staring observation (micro-dislocation) data registration;
FIG. 6 is a schematic diagram of LSF recovery based on registered multi-frame GF-4 satellite data;
Fig. 7 is a flow chart of an SNR test based on GF-4 high frequency repetitive gaze observation data.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the following embodiments and accompanying drawings.
The main area of the experiment is on a farm, and the geographic coordinates are 125 degrees, 45 degrees to 126 degrees, 30 degrees and 46 degrees, 12 degrees to 46 degrees, 22 degrees of east longitude and north latitude.
As shown in fig. 1, the process of the on-orbit testing method for the space-based remote sensing load for high-frequency repeated staring imaging comprises the following steps:
and step 50, performing row-column vector statistical calculation on continuous multi-frame image data of the ground object of the uniform scene in a time dimension to obtain a root mean square error, and calculating the SNR (signal to noise ratio) based on the multi-frame image uniform ground object after registration.
In this embodiment, a test result in a certain research area (fig. 2) is taken as an example to explain in detail a process of an on-orbit test of space-based remote sensing load by using high-frequency repeated gaze imaging of the present invention.
As shown in FIG. 3, the impulse MTF test technology based on GF-4 high-frequency repeated gazing observation data comprises the following steps:
1) laying radiation characteristic targets
As shown in fig. 4, the low reflectivity area is selected as the background feature, and the high reflectivity radiation characteristic target (60%) is arranged. Given the high resolution of ground in four (50 meters) and the cost of targets, labor, etc., the high reflectivity radiation targets are laid to cover at least 3 x 4 pixels, with a projected target of about 180 x 270 meters (48000 square meters).
2) Simultaneous reflectivity observation of the ground
When a high-resolution fourth satellite GF-4 continuously observes and images a test area, a synchronous spectrum reflectivity test is carried out on the ground, and measured target and background ground object reflectivity are used as input data.
3) High-precision geometric registration of high-frequency repeated staring observation data of GF-4 satellite
As shown in FIG. 5, the GF-4 satellite can complete task maneuvering response within a few minutes and image a target area designated by a user, including high-frequency repeated staring observation of the target area, rapid splicing imaging of a certain area and patrol imaging of a plurality of areas. The research is mainly based on a high-frequency repeated staring observation imaging mode of a GF-4 satellite on the same target area. For the characteristics that 5s exists during single-spectrum-segment imaging, 50s of difference exists during different-spectrum-segment imaging, the difference of a shot target scene can be basically ignored, and the position and the posture of a satellite can have smaller difference, high-precision geometric registration is carried out on data of high-frequency repeated staring imaging, and the registration precision (1 sigma) of the processed single-spectrum-segment multi-frame image data is better than 0.1-0.2 pixel.
4) Line spread function LSF acquisition
And removing satellite image data at the atmospheric disturbance moment based on ground continuous atmospheric parameter observation, and recovering an LSF line diffusion function based on the registered multi-frame image. The LSF recovery based on the registered multi-frame GF-4 satellite data is schematically shown in fig. 6.
5) Calculating MTF
Calculating and recovering to obtain a Line Spread Function (LSF); secondly, normalizing the LSF function; and finally, performing discrete Fourier transform on the normalized LSF, taking the module value of each component within the cutoff frequency after transformation, and performing normalization by taking the MTF value at the frequency 0 as a reference to obtain MTF values of different frequencies within the cutoff frequency, thereby forming a full-band MTF curve. As shown in the following equation:
MTFx＝NORMAL(FFT(NORMAL(LSFx)))
wherein, LSFxIs a line spread function in the X direction; MTFxA modulation transfer function for the X direction; FFT stands for fourier transform processing; norm al stands for normalization treatment.
2. SNR test based on GF-4 high frequency repeat gaze observation data
Fig. 7 is a flow chart of SNR testing based on GF-4 high frequency repetitive gaze observation data, with method steps comprising:
1) selecting a suitable test area
Selecting test areas with different reflectivity ground objects (low, medium and high), wherein the low reflectivity corresponds to a deep sea area, the medium reflectivity corresponds to a Gobi desert of a Dunhuang satellite radiation calibration field, and the high reflectivity corresponds to a bright sand area of a Dunhuang gypsum mine area, and respectively testing the signal-to-noise ratio under different ground object reflectivity conditions.
2) Formulating GF-4 satellite imaging plans
And (4) aiming at the selected test area, making an imaging plan, and carrying out high-frequency repeated staring observation on the uniform scene.
3) Geometric registration of GF-4 satellite high-frequency repeated staring observation data
The research is mainly based on a high-frequency repeated staring observation imaging mode of a GF-4 satellite for the same target area. And (3) selecting the feature with the same name point on the satellite image to perform high-precision geometric registration, wherein the registration precision (1 sigma) of the processed single-spectrum multi-frame image data is superior to 1 pixel.
4) Statistics of column vectors of uniform ground objects in multi-frame image time dimension
And analyzing the variation trend of the long-time sequence of the ground objects in the uniform scene, performing statistical calculation on row-column vectors of continuous multi-frame image data of the uniform scene subjected to geometric registration in a time dimension to obtain a root-mean-square error, and calculating the SNR under the imaging condition.
In the research, a pulse method MTF testing technology and an SNR testing technology based on GF-4 high-frequency repeated staring observation data obtain certain results; however, the premise that MTF is calculated by a line spread function LSF recovered from the multi-frame images after registration and SNR is calculated based on the uniform ground features of the multi-frame images after registration is that high geometric registration accuracy is required, the multi-frame image registration method is improved and optimized next, and geometric registration is performed on the multi-frame images of GF4 by using a ground and high-resolution image control point database.
Although the embodiments of the present invention have been described above, the above description is only for the purpose of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. A high-frequency repeated staring imaging space-based remote sensing load on-orbit testing method is characterized by comprising the following steps:
A. selecting a field meeting MTF and SNR test requirements, and laying a radiation characteristic target;
B. monitoring solar spectrum information and earth radiation balance information, and measuring the reflectivity of the target and natural ground objects by using a spectrometer;
C. based on synchronous atmospheric aerosol observation data, screening image data continuously observed by GF-4, and rejecting satellite image data obtained at atmospheric disturbance moment;
D. obtaining MTF values of different frequencies in the cut-off frequency through calculation and processing of a linear diffusion function LSF;
E. performing row-column vector statistical calculation on continuous multi-frame image data of the ground object of the uniform scene in a time dimension to obtain a root mean square error, and calculating an SNR (signal to noise ratio) based on the registered multi-frame image uniform ground object.
2. The high-frequency repeated gazing imaging space-based remote sensing load on-orbit testing method according to claim 1, wherein the A comprises the following steps:
a1, selecting a low-reflectivity area as a background ground object, and laying a high-reflectivity radiation characteristic target as an MTF test target;
a2, selecting test areas of the ground objects with different reflectivities of low, medium and high, and testing the SNR of the ground objects under different reflectivity conditions respectively by corresponding the different reflectivities of low, medium and high to different areas.
3. The high-frequency repeated gaze imaging space-based remote sensing load on-orbit testing method of claim 1, wherein B comprises: and when the GF-4 continuously observes and images the test area, carrying out a synchronous spectrum reflectivity test on the ground, and taking the measured target and background ground object reflectivity as input data.
4. The high frequency repeated gaze imaging space-based remote sensing load on-orbit testing method of claim 1, wherein C comprises:
c1, when testing MTF, carrying out high-precision geometric registration on the data of high-frequency repeated gaze imaging, wherein the registration precision 1 sigma of the processed single-spectrum multi-frame image data is better than 0.1-0.2 pixels;
and C2, when testing the SNR, selecting the same-name point ground object on the satellite image to perform high-precision geometric registration, wherein the registration precision 1 sigma of the processed single-spectrum multi-frame image data is better than 1 pixel.
5. The high frequency repeated gaze imaging space-based remote sensing load on-orbit testing method of claim 1, wherein D comprises:
d1, based on the observation of ground continuous atmospheric parameters, removing satellite image data at atmospheric disturbance moment, and recovering an LSF line diffusion function based on the multi-frame image after registration;
d2, calculating and recovering to obtain a Line Spread Function (LSF); secondly, normalizing the LSF function; finally, performing discrete Fourier transform on the normalized LSF, taking the module value of each component within the cutoff frequency after transformation, and performing normalization by taking the MTF value at the frequency 0 as a reference to obtain the MTF values of different frequencies within the cutoff frequency, thereby forming a full-band MTF curve; as shown in the following equation:
MTFx＝NORMAL(FFT(NORMAL(LSFx)))
wherein is the line spread function in the X direction; a modulation transfer function for the X direction; FFT stands for fourier transform processing; norm al stands for normalization treatment.
6. The high frequency repeated gaze imaging space-based remote sensing load on-orbit testing method of claim 1, wherein E comprises: and analyzing the variation trend of the long-time sequence of the ground objects of the uniform scene, performing statistical calculation on row-column vectors of continuous multi-frame image data of the uniform scene subjected to geometric registration in a time dimension, acquiring a mean value and a standard deviation, and calculating the SNR under the imaging condition.
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