CN111199557A - Quantitative analysis method and system for decay of remote sensor - Google Patents
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Abstract
The embodiment of the invention provides a quantitative analysis method and a system for decay of a remote sensor, wherein the method comprises the following steps: projecting satellite remote sensing data of the same geographic area obtained at different times to the same geographic grid, and constructing satellite data sets of different time phases of the same geographic area; calculating the apparent reflectivity of each satellite remote sensing data; acquiring invariant pixels of the same geographic area based on IR-MAD conversion; performing orthogonal regression on the apparent reflectivity of each spectral channel based on the invariant pixel, and acquiring a decay value sequence of the satellite remote sensor in each spectral channel; and establishing a remote sensor decay monitoring model according to the decay value sequence. The method is not limited to any specific sensor or geographical area, can identify the invariant pixels of any image pair in the satellite remote sensing data, effectively integrates the change information of each channel, completes the on-orbit decay tracking of the satellite remote sensor, has strong adaptability, and can be applied to the reprocessing and information mining of large-scale space data sets.
Description
Technical Field
The invention relates to the technical field of satellite remote sensing, in particular to a quantitative analysis method and system for decay of a remote sensor.
Background
The satellite remote sensor can provide multi-spectral wave band and multi-temporal satellite remote sensing data, and in most satellite remote sensing application scenes, the satellite remote sensing is required to provide observation data with long-time sequence consistency, so that high-precision radiometric calibration of the remote sensing data is an important premise for quantitative application of the remote sensing data. However, due to various factors such as vibration during emission, severe space environment and instrument aging, the radiation response characteristics of the radiation calibration system are attenuated along with space-time changes, so that the decay of a remote sensor needs to be quantitatively analyzed and tracked, and further the radiometric calibration is completed.
There are many on-orbit calibration and monitoring methods for satellite remote sensing, including alternative calibration based on a uniform calibration field using a radiation transmission model and synchronous field measurement parameters (or other source parameters), radiation tracking using a high-brightness earth uniform stable target (such as desert, glacier and deep convection cloud), radiation tracking using a moon target, and cross calibration based on a reference remote sensor or a wave band.
The on-orbit radiation response change of the earth-based uniform and stable target monitoring remote sensing instrument is a calibration tracking method which is low in cost and increasingly widely applied. The decay of the instrument during the time period is analyzed by the difference of the radiation response of the satellite in the areas with stable reflectivity, and then the relative radiation normalization of the satellite data is carried out. For example: the desert target Dunhuanggi in northwest China is used for reflecting the calibration of a solar band satellite remote sensor in the China remote sensing satellite radiation calibration field (CRCS). In addition, there is a calibration and tracking method using glacier targets (Dome C and Greenland) in north and south as earth stability targets. However, these calibration tracking methods based on earth uniform and stable targets all need to have a certain priori knowledge on the ground, and the selection process of the targets is time-consuming and labor-consuming.
With the development of information technology, some mathematical methods are also gradually applied to the change detection of satellite data, and the conventional methods include an image difference method and a ratio method applied to single-channel data. However, the satellite data is generally multi-channel data, so that these conventional methods are not suitable for detecting the change of the satellite data because the information of each channel cannot be integrated.
In addition, the Principal Component Analysis (PCA) method can effectively integrate the variation information of each channel, i.e., integrate the variation information of each channel on several main components through a linear variation, and then perform variation detection through analysis of the several main components. However, the principal component analysis method still has its limitation, and although it can integrate the variation information of a plurality of channels and eliminate the correlation between the channels, it cannot eliminate the correlation between the channels at different times with respect to the satellite data obtained at different times, so its detection of the variation information still has its great limitation.
Disclosure of Invention
The embodiment of the invention provides a quantitative analysis method and a system for decay of a remote sensor, which are used for solving the defects that the selection of a constant characteristic field is complex and the correlation of spectral channels of different time phases cannot be effectively eliminated when the decay of the remote sensor is tracked, or at least partially solving the technical defects.
In a first aspect, an embodiment of the present invention provides a method for quantitative analysis of decay of a remote sensor, including: projecting satellite remote sensing data of the same geographic area obtained at different times to the same geographic grid, and constructing satellite data sets of different time phases of the same geographic area; calculating the apparent reflectivity corresponding to each satellite remote sensing data in the satellite data set; based on IR-MAD conversion, obtaining invariant pixels of the same geographical area according to a preset invariant pixel judgment threshold; performing orthogonal regression on the apparent reflectivity of each spectral channel based on the invariant pixel, and acquiring a decay value sequence of each spectral channel of the satellite remote sensor in each time period; and establishing a remote sensor decay monitoring model according to the decay value sequence.
Further, the acquiring of the invariant pixel of the same geographical area based on the IR-MAD transform according to the preset invariant pixel determination threshold includes: acquiring satellite remote sensing data at two different moments in a satellite data set to construct an image pair; according to the apparent reflectivity corresponding to each satellite remote sensing data, performing IR-MAD transformation on the image pair to acquire all MAD components of the image pair; normalizing all the MAD components according to the standard deviation corresponding to the MAD components; and determining the invariant pixel on the image pair according to a preset invariant pixel judgment threshold.
Further, the determining of the invariant pixel on the image pair by the preset invariant pixel judgment threshold specifically includes:
wherein N is the number of spectral channels, MADiFor the MAD component of the ith spectral channel,
is MADiK is a pixel judgment threshold;
chi-square distribution with degree of freedom N2Upper-divided bit-point values at probability prob. Further, the obtaining of the satellite remote sensing data at two different times in the satellite data set to construct an image pair includes: selecting two satellite remote sensing data with approximately the same ground orbit from a satellite data set to construct an image pair; wherein, the longitude of the intersection point of the two satellite remote sensing data with the approximately same ground orbits and the equator is within +/-1 DEG when the satellite remote sensor passes the borderAnd (4) obtaining.
Further, the above acquiring satellite remote sensing data at any two times in the satellite data set to construct an image pair further includes:
the time interval of the satellite remote sensing data at any two moments is as follows: 365.25 x y ± 20days, wherein y is 0,1,2,3, … years.
Further, before performing IR-MAD transformation on the image pair and acquiring the MAD component of the image pair, performing invalid pixel mask processing and registration processing on two satellite remote sensing images in the image pair; the method for processing the invalid pixel mask of the two satellite remote sensing images in the image pair comprises the following steps:
based on a threshold discrimination method, removing invalid pixels in the image pair according to the information of each spectral channel of the image pair; removing pixels of water and ocean areas in the image pair by using an ocean land mask; according to the orbit height of the satellite corresponding to the image pair, eliminating pixels of the image pair, of which the observation angle exceeds a preset range;
the registration processing of the two satellite remote sensing images in the image pair includes:
taking any one satellite remote sensing image in the image pair as a reference image, moving another satellite remote sensing image in different directions, and obtaining Pearson correlation coefficients at corresponding positions; and when the Pearson correlation coefficient is determined to take the maximum value, the relative position of the two satellite remote sensing images in the image pair is the registration position.
Further, the calculating the apparent reflectivity corresponding to each satellite remote sensing data in the satellite data set includes:
wherein, αiFor the scaling slope of the DN value of the ith spectral channel, βiIs the intercept of DN value of the ith spectral channel, wherein the DN value is the radiation observed value corresponding to the satellite remote sensing data, d2Is a sun-ground distance correction factor, thetasAt the zenith angle of the sun, ρiIs notAnd changing the apparent reflectivity of the image element in the ith spectral channel.
Further, the performing orthogonal regression on the apparent reflectivity of each spectral channel to obtain a decay value sequence of each spectral channel of the satellite remote sensor in a time period includes:
wherein the content of the first and second substances,
is represented by t1And t2The invariant pixel between the pair of time point images is at t1The apparent reflectance of the ith spectral band at that time,
is represented by t1And t2The invariant pixel between image pairs at all times is at t2Apparent reflectance of the ith spectral band at time, SRi(t1) Represents t1Radiation response of time-of-day satellite remote sensors, SRi(t2) Represents t2Radiation response of time-of-day satellite remote sensors, mi(t1,t2) For satellite remote sensors at t1-t2Decay value of the ith spectral channel in the time period.
Further, in the above-mentioned establishing a remote sensor decay monitoring model according to the decay value sequence, the polynomial function of the remote sensor decay monitoring model is:
where t is the number of days from the start time of the data in the decay value sequence, M is the order of the polynomial function and Pi(0)＝1、a01, l is the order of the polynomial;
coefficient of order I of polynomiallDetermined by a non-linear least squares fit, comprising:
where j denotes the number of different image pairs and N is the number of spectral channels.
In a second aspect, embodiments of the present invention provide a system for quantitative analysis of decay of a remote sensor, comprising: the system comprises a data set acquisition unit, a data conversion unit, an invariant pixel calibration unit, a decay value sequence acquisition unit and a modeling and analysis unit, wherein: the data set acquisition unit is used for projecting the satellite remote sensing data of the same geographical area acquired at different times to the same geographical grid and constructing satellite data sets of different time phases of the same geographical area; the data conversion unit is used for calculating the apparent reflectivity corresponding to each satellite remote sensing data in the satellite data set; the invariant pixel calibration unit is used for acquiring invariant pixels of the same geographic area based on IR-MAD conversion according to a preset invariant pixel judgment threshold; the decay value sequence acquisition unit is used for performing orthogonal regression on the apparent reflectivity of each spectral channel to acquire a decay value sequence of each spectral channel of the satellite remote sensor in each time period; and the modeling and analyzing unit is used for establishing a remote sensor decay monitoring model according to the decay value sequence.
In a third aspect, embodiments of the present invention provide an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method for quantitative analysis of decay of a remote sensor according to the first aspect.
In a fourth aspect, embodiments of the invention provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method for quantitative analysis of decay of a remote sensor according to the first aspect.
The quantitative analysis method and the system for decay of the remote sensor provided by the embodiment of the invention are not limited to any specific sensor or geographical area, the invariant pixel identification is carried out on any image in the satellite remote sensing data, the change information of each channel is effectively integrated, the on-orbit decay tracking of the satellite remote sensor is completed, the adaptability is strong, and the method and the system can be applied to reprocessing of large-scale space data sets and information mining.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram of a method for quantitative analysis of decay of a remote sensor according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a remote sensor decay-based quantitative analysis system provided by an embodiment of the invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
In the prior art, tracking decay of a satellite remote sensor generally comprises the following large steps: 1. selecting an earth stable target (field); 2. calculating the apparent reflectivity and filtering the polluted area (cloud, sand and dust, etc.); 3. correcting the BRDF effect of the target; 4. tracking the apparent reflectivity of the long-sequence data of the stable target; 5. and establishing an instrument response tracking monitoring model of the stable target.
Based on the scheme, the following defects are caused in the prior art: on one hand, for the method for manually selecting the invariant feature field, the stable target on the ground is selected manually, the method needs to have certain priori knowledge on the ground, and the selection process is time-consuming and labor-consuming. On the other hand, for the principal component analysis method, although it can integrate the variation information of a plurality of channels and eliminate the correlation between the channels, for the satellite data obtained from different times, it cannot eliminate the correlation between the channels at different times, so its detection of the variation information is still limited.
To effectively overcome the disadvantages in the prior art, embodiments of the present invention provide a method for quantitative analysis of decay of a remote sensor, as shown in fig. 1, including but not limited to the following steps:
step S1: projecting satellite remote sensing data of the same geographic area obtained at different times to the same geographic grid, and constructing satellite data sets of different time phases of the same geographic area;
step S2: calculating the apparent reflectance corresponding to each satellite remote sensing data in the satellite data set;
step S3: based on IR-MAD conversion, according to a preset invariant pixel judgment threshold value, acquiring the invariant pixel of the same geographic area;
step S4: performing orthogonal regression on the apparent reflectivity of each spectral channel based on the invariant pixel, and acquiring a decay value sequence of each spectral channel of the satellite remote sensor in each time period;
step S5: and establishing a remote sensor decay monitoring model according to the decay value sequence.
The remote sensing satellite data is the reflection of the remote sensing satellite in space for detecting the earth surface object to the electromagnetic wave, and combines the emitted electromagnetic wave, thereby extracting the object information and completing the remote identification of the object. In the actual operation process, the electromagnetic waves are converted and identified to generate a visual image, namely a satellite remote sensing image. Wherein each satellite remote sensing image is a real-time landform photo with information such as longitude and latitude.
The geographic grid is a data form which divides a geographic space into regular grids, each grid is called a unit, and corresponding attribute values are given to the units to represent satellite remote sensing data. In the embodiment of the invention, satellite remote sensing images obtained at different times are projected to the same geographical grid, namely, all satellite remote sensing data in the same geographical area are subjected to geographical rasterization by a remote sensing imager on a remote sensing satellite at different time points in a time period, and the rasterized satellite remote sensing data are assembled into a satellite data set, so that the radiation normalization of the satellite remote sensing data is realized.
The apparent reflectivity refers to the reflectivity of the top of the atmospheric layer, and the value of the apparent reflectivity is equal to the sum of the reflectivity of the earth surface and the reflectivity of the atmosphere. In the embodiment of the invention, the apparent reflectivity corresponding to each satellite remote sensing data can be calculated and obtained through a satellite data set which is formed by geography rasterizing the satellite remote sensing data received by the multiband sensor.
The IR-MAD (infrared modulated Multivariate alternating detection, abbreviated as IR-MAD) transform is a method for detecting a change or an invariant feature in a scene, and is widely applied to multi-element image detection change because the method can efficiently capture the change situation of an unstable point, accurately acquire change information, has small influence of external factors and the like in the dual-temporal, multi-variable and hyper-variable image data mining. The IR-MAD core idea is that the initial weight of each pixel is 1, each iteration is endowed with a new weight of each pixel in 2 images, pixels which do not change have a larger weight through calculation, and the finally obtained weight is the basis for determining whether each pixel changes. After a plurality of iterations, the weight of each pixel tends to be stable until the change is smaller than the set threshold value or no longer changes, and the iteration is stopped. Because the satellite remote sensing data received by the satellite sensor needs to pass through the atmosphere and other interferences in the process of returning to the ground, the radiometric value (DN value) in the satellite remote sensing data cannot truly reflect the information of the earth surface, in the embodiment of the invention, the apparent reflectivity is calculated by each satellite remote sensing data in the satellite data set, and then the satellite remote sensing data is converted into the apparent reflectivity. Then, performing image conversion on each apparent reflectivity to obtain a satellite remote sensing picture corresponding to each satellite remote sensing data, and further determining picture pairs of all satellite remote sensing pictures; and finally, determining the invariant pixels (NCPs) in each picture pair based on IR-MAD transformation, thereby obtaining the invariant pixels of the same geographical area.
The orthogonal regression used in the embodiment of the present invention may be an orthogonal polynomial regression. Where orthogonal polynomial regression is the process of data processing using orthogonal polynomial table arrangement tests and regression analysis. The method is different from the general polynomial regression prepared by the least square method, the estimation of the regression coefficients of the method is independent, if the statistical test shows that a certain regression coefficient has no significant difference with zero, only the regression coefficient is deleted from the regression equation, and other regression coefficients do not need to be recalculated. Since the orthogonal regression is a common mathematical model, it is not described in detail in the embodiments of the present invention.
Specifically, after orthogonal regression processing, attenuation values of each spectral channel in each time period can be acquired, and a data set of an attenuation value sequence is constructed. In the embodiment of the present invention, the decay value of the remote sensor can also be regarded as a measurement value of the relative gain of the sensor in the remote sensor.
Further, in the embodiment of the present invention, after acquiring the sequence data sets of the measured values of the relative gain of the sensor at different periods and different time intervals (i.e. after acquiring the decay value sequence), a remote sensor decay monitoring model about the time sequence is constructed by using a mathematical modeling method, and is used for carrying out quantitative analysis and tracking on the decay of the remote sensor.
The quantitative analysis method for decay of the remote sensor provided by the embodiment of the invention is not limited to any specific sensor or geographical area, the tracking of the satellite remote sensor on-orbit decay is completed by identifying the invariant pixel of any image in the satellite remote sensing data and effectively integrating the change information of each channel, the adaptability is strong, and the method can be applied to reprocessing of large-scale space data sets and information mining.
Based on the content of the foregoing embodiment, as an alternative embodiment, the acquiring invariant pixels of the same geographic area according to apparent reflectivity based on the IR-MAD transform includes:
s31, acquiring satellite remote sensing data of any two moments in the satellite data set to construct an image pair;
step S32, performing linear transformation on the image pair according to the apparent reflectivity corresponding to each satellite remote sensing data to acquire MAD components of the image pair in each spectral channel;
step S33, acquiring the ratio of the MAD component of each spectral channel to the MAD component standard deviation of the corresponding spectral channel;
step S34, determining invariant pixels on the image pair according to the ratio and a preset pixel judgment threshold;
and step S35, determining an invariant pixel of each satellite remote sensing image of the satellite data set, and setting the common invariant pixel as an invariant pixel of the same geographic area.
Nielsen et al, 1998, propose that MAD transforms can be used to detect changing regions in a scene. Canty et al successfully applied it to high resolution data of Landsat and SPOT for automatic relative radiation normalization and improved development into IR-MAD method. According to the statistical characteristic of the satellite remote sensing data, the invariant point (pixel) of the ground table can be automatically selected by setting a pixel judgment threshold k in the embodiment of the invention.
Specifically, t is sorted out from the respective satellite data sets1And t2Image (t) obtained at a given time1) And image (t)2) The two images are set as one image pair. For the pair of images, the MAD components of the individual spectral channels after IR-MAD transformation can be determined by linear transformation of the vector coefficients a and b. This can be achieved by solving a generalized eigenequation, the MAD component being defined as follows:
MADi＝aiImage(t1)i-biImage(t2)i,i＝1...N
wherein N represents the number of spectral channels, MADiThe MAD component for the ith spectral channel.
Based on the content of the foregoing embodiment, as an optional embodiment, wherein the step S34 determines the invariant pixel on the image pair according to the ratio and the preset pixel judgment threshold, specifically, the determination may be performed by the following formula:
if any pixel satisfies the condition in the above formula, it can be regarded as an invariant pixel (NCPs for short). Wherein the content of the first and second substances,
is MADiK is a pixel judgment threshold value and is a quantile of chi-square distribution with N degrees of freedom.
The method for determining the pixel judgment threshold k in the embodiment of the invention can be based on a probability function
Is determined wherein
Chi-square distribution with degree of freedom N2Upper-divided bit-point values at probability prob.
In the embodiment of the invention, the method for determining the NCPs on the image pair by setting the pixel judgment threshold value can completely select the NCPs meeting the condition under the condition of no ground surface prior knowledge, wherein the selected NCPs and the image (t) are1) And an image (t)2) Corresponding to the invariant feature in between. However, since the location of the NCPs is determined from the satellite telemetry data based on statistical characteristics, it is likely to vary from one image pair to another. In this embodiment, statistical probability may be combined to further acquire a plurality of image pairs in the satellite data set, determine the NCPs of each image pair respectively, and finally make the images pairs commonThe NCPs of (1). Since all the image pairs are acquired by satellite imaging in the same geographic area, the acquired common NCPs can be set as NCPs in the same geographic area.
According to the quantitative analysis method for decay of the remote sensor, the invariant pixel positioning is carried out through the image pair arbitrarily established in the satellite data set, and finally the invariant pixel positioning in the same geographic area is completed.
Based on the content of the foregoing embodiment, as an alternative embodiment, the step of obtaining satellite remote sensing data of any two time points in the satellite data set to construct an image pair in step S31 includes, but is not limited to, the following steps: selecting two satellite remote sensing data with approximately the same ground orbit from the satellite data set to construct an image pair; wherein the two satellite remote sensing data with the approximately same ground orbit are acquired within +/-1 DEG of the longitude of the intersection point of the satellite remote sensor and the equator when the satellite remote sensor passes through the border.
Since, for detecting changes or detecting invariant features in a scene using the IR-MAD transform method, its accuracy is affected by many factors. How to select two image images (t) for IR-MAD transformation, especially for large and complex satellite data1) And image (t)2) And the key to accurately perform quantitative analysis and tracking of decay of a remote sensor is how to process and filter data to improve the accuracy of identifying the invariant pixels by an IR-MAD transformation method.
In an embodiment of the invention, a method is provided for selecting two image images (t) for IR-MAD transformation1) And image (t)2) In the screening method of (1), firstly, from the viewpoint of analysis of variable influence, it is assumed that the difference of radiation measurement received by the sensor is caused by the linear effect of sensor decay, so that it is necessary to eliminate or reduce the influence of other possible variation factors as much as possible, mainly including conditions of environment (earth surface and atmosphere) and observation (geometry and illumination)The influence of (c).
In particular, in embodiments of the invention, the above-described image pairs are selected from data having approximately the same ground orbit, i.e., a satellite transit (equivalent to a satellite sensor transit) within ± 1 ° longitude from the equatorial intersection, the viewing and illumination geometry of each pixel in the image pair is very similar.
Of course, the above-mentioned accuracy range is set within ± 1 ° in the present embodiment, and a better quantitative analysis result of the decay of the remote sensor can be obtained, but it is not to be considered as a limitation to the protection scope of the present embodiment, and for example, it may be set to ± 0.5 ° or ± 2 ° according to the requirement of the detection accuracy, and the present embodiment is not specifically limited.
Based on the content of the foregoing embodiment, further, the foregoing step S31 may further include setting a time interval of the satellite telemetry data at any two time points as: 365.25 x y ± 20days, wherein y is 0,1,2,3, … years.
Specifically, to minimize seasonal variations due to surface variations, solar declination and stratospheric ozone concentration variations, any given image was compared only to other images within 365.25 × y ± 20days (y ═ 0,1,2,3, … years) of the data set time interval.
For example: image (t)1) For the graphic taken on the first day, then image (t) is selected2) Considering the time of (t) and obtaining the image2) The time period of (A) is: within 20days, within 365 +/-20 days, within 365 x 2 +/-20 days …
According to the quantitative analysis method for the sensor decay, provided by the embodiment of the invention, the selection of the graph pair is limited, a large number of image pairs meeting the requirements can be obtained from the long-sequence satellite data, and the precision of the quantitative analysis method for the remote sensor decay is effectively improved.
Based on the content of the above embodiment, further, in the embodiment of the present invention, the image pair is subjected to linear transformation, and before the MAD component of each spectral channel of the image pair is acquired, the method further includes performing invalid pixel mask processing and registration processing on two satellite remote sensing images in the image pair;
the invalid pixel mask processing of the two satellite remote sensing images in the image pair can include the following steps:
based on a threshold discrimination method, removing invalid pixels in the image pair according to the information of each spectral channel of the image pair; removing pixels of water and ocean areas in the image pair by using an ocean land mask; and according to the orbit height of the satellite corresponding to the image pair, eliminating pixels of the image pair with observation angles exceeding a preset range.
Further, the registration processing of the two satellite remote sensing images in the graphic pair may include the following steps:
taking any one satellite remote sensing image in the image pair as a reference image, moving another satellite remote sensing image in different directions, and obtaining Pearson correlation coefficients at corresponding positions; and when the Pearson correlation coefficient is determined to take the maximum value, the relative position of the two satellite remote sensing images in the image pair is the alignment position.
Specifically, the method for quantitatively analyzing decay of a remote sensor provided by the embodiment of the present invention further needs to perform image-pair-based preprocessing on data before performing IR-MAD transformation on a determined image pair to improve the stability of quantitative analysis and the accuracy of invariant pixel identification. These include, among others: a mask of invalid pels in the two images constituting the image pair and a mutual registration of the two images.
Since the main purpose of this embodiment is to detect an invariant pixel in a scene based on the principle of statistics, in order to improve the stability of the result, it is preferable to remove a target, such as a cloud, that significantly changes in the scene. Meanwhile, the spectral reflection characteristics of different channels in water and ocean areas are obviously different, and signals of some channels are very weak, so that pixels with poor spectral reflection characteristics need to be removed before detection of the invariant pixels.
For interference pixels with significant changes, such as clouds in a scene, only a plurality of spectral band information beneficial to detecting the clouds (or other interference pixels) need to be integrated, and the interference pixels can be detected and removed by using a threshold discrimination method.
Furthermore, because the satellite remote sensing data generally has a marine land mask, for pixels with poor spectral reflection characteristics such as water bodies and oceans, the water bodies in the scene can be removed by using the mask.
In addition, for the area far away from the track of the substellar point, namely the area with a large observation angle of the sensor, the image elements will be distorted to some extent and the resolution will be reduced, so that the image elements with the observation angle of the sensor larger than a certain range need to be removed, and the range can be determined according to the orbit height of the satellite. The method is a compromise processing mode for improving the accuracy of the invariant pixel and the number of the pixel points for statistical analysis.
In addition, due to the fact that the satellite transit orbits are different at different times, although the projection process is carried out according to the longitude and latitude information in the data, the data at different times are projected on the same geographic grid and have slight geographic deviation. Therefore, image registration is a problem that needs to be solved first before proceeding to the next operation. In the embodiment of the present invention, one of the images may be used as a reference image, the other image may be slightly moved in different directions, and Pearson correlation coefficients are solved, so that when there is a maximum correlation coefficient in a certain position, it can be considered that the two images have been registered.
Finally, after the image pair is subjected to the preprocessing, the image pair can be identified to the invariant image element in the scene by using IR-MAD transformation. And further performing orthogonal regression on the apparent reflectivity of each spectral channel of the identified invariant pixel, so as to obtain the decay measured value of each channel of the sensor in the time period.
Based on the content of the foregoing embodiment, as an optional real-time exchange rate, the apparent reflectance corresponding to each satellite remote sensing data in the satellite data set calculated in step S2 may be determined by the following formula:
wherein, αiFor the scaling slope of the DN value of the ith spectral channel, βiIs the intercept of DN value of the ith spectral channel, wherein the DN value is the radiation observed value corresponding to the satellite remote sensing data, d2Is a sun-ground distance correction factor, thetasAt the zenith angle of the sun, ρiThe apparent reflectivity of the unchanged image element in the ith spectral channel is obtained.
Because the satellite remote sensing data needs to penetrate through interference sources such as the atmosphere when returning to the ground receiving device, the radiation measured value (DN value) received by the satellite sensor cannot truly reflect the information of the earth surface, and the quantitative analysis method for decay of the remote sensor provided by the embodiment of the invention carries out certain correction and converts the radiation measured value into the apparent reflectivity convenient for mathematical modeling through the calculation formula when analyzing the radiation measured value.
Based on the content of the foregoing embodiment, in the embodiment of the present invention, the step S4 performs orthogonal regression on the apparent reflectivity of each spectral channel to obtain the decay value sequence of each spectral channel of the satellite remote sensor in the time period, which includes but is not limited to the following calculation methods:
wherein the content of the first and second substances,
is represented by t1And t2The invariant pixel between the pair of time point images is at t1The apparent reflectance of the ith spectral band at that time,
is represented by t1And t2The invariant pixel between image pairs at all times is at t2Apparent reflectance of the ith spectral band at time, SRi(t1) Represents t1Radiation response of time-of-day satellite remote sensors, SRi(t2) Represents t2Radiation response of time-of-day satellite remote sensors, mi(t1,t2) For satellite remote sensors at t1-t2Decay value of the ith spectral channel in the time period.
In particular, the measurement difference of the satellite remote sensing data received by the satellite remote sensor is caused by a linear effect, namely, the linear effect caused by decay of the remote sensor. Wherein for each of the slave t1And t2Time-acquired image (t)1) And image (t)2) The image pair has the following relationship with respect to the invariant point in the scene:
the conversion may obtain:
wherein m isi(t1,t2) Is calculated from a linear regression of the NCPs between the image pairs. Thus, comparison of one image with the other provides a measure of the relative change in the responsivity of the remote sensor over the period between the acquisition of the two images. By comparing pairs of images taken over intervals from days to years apart, one can create relatively distant sensor attenuation values m over one or more intervalsi(t1,t2) Large data sequences. For analysis of this data sequence, a near continuous record of the remote sensor's change over time can be provided and can be used for polynomial fitting to obtain a curve describing the remote sensor's responsiveness over time.
Further, based on the content of the above embodiment, the step S5 is to establish a remote sensor decay monitoring model according to the decay value sequence, wherein the polynomial function of the remote sensor decay monitoring model may be set as:
where t is the number of days from the start time of the data in the decay value sequence, and M is a plurality of termsOrder of the formula function and Pi(0)＝1、a01, l is the order of the polynomial;
coefficient of order I of polynomiallDetermined by a non-linear least squares fit, comprising:
where j denotes the number of different image pairs and N is the number of spectral channels.
On the other hand, according to the embodiments, the sequence data sets of the remote sensor attenuation rates at different times and different time intervals can be acquired. After the image pair registration is performed according to the matching criteria of the image pair described in the above embodiment, some of the images are frequently reused, and some of the images are less used.
On the other hand, the quantitative analysis method for decay of the remote sensor provided by the embodiment of the invention is easily influenced by serious changes of atmospheric water vapor and aerosol in two images, for example, sand storm occurs in some images.
To exclude scenes that are strongly influenced by the atmosphere when the image is analyzed by the IR-MAD, in this embodiment, the image pair filtering criteria can be formulated according to the intercept of the regression curve. That is, channels having a regression intercept reflectivity greater than 3% for different channel reflectivities of NCPs are removed during comparison of the two images of an image pair. This cut-off screening method is used because in some cases, when only one or some of the channels are affected and disturbed, it is not necessary to discard the regression of the data of other channels of the image pair. Meanwhile, for the image pair with the image geographic position registration deviation still existing, the correlation coefficient and the regression residual variance of the NCPs are adopted for image pair analysis and filtration.
Further, the time-dependent decay curve of the sensor response (for any channel) may be expressed by a polynomial Pi(t) and the concrete model is as shown in the above embodiment.
After the time interval t1, t2 has been acquired]In case of internal relative change, P is determinediIs to estimateRatio of the polynomials at these two times
Thus, the problem can be attributed to the estimation of the relative change in this interval, mi(t1,t2) And solving for polynomial coefficients al。
Wherein the coefficients (a) of the polynomiall) Determined by a non-linear least squares fit (Nelder-Mead simplex method), i.e. by the minimization formula:
to obtain.
Where j represents a different pair of images. Thus, a decay curve of the remote sensor with respect to the initial data acquisition time is obtained.
Further, after the decay curve of the remote sensor is obtained, quantitative analysis and tracking of the decay of the remote sensor can be completed.
An embodiment of the present invention provides a system for quantitative analysis of decay of a remote sensor, as shown in fig. 2, including but not limited to the following structures: a data set acquisition unit 11, a data conversion unit 12, an invariant pixel calibration unit 13, a decay value sequence acquisition unit 14, and a modeling and analysis unit 15, wherein: the data set acquisition unit 11 is configured to project satellite remote sensing data of the same geographic area obtained at different times to the same geographic grid, and construct satellite data sets of different time phases of the same geographic area; the data conversion unit 12 is used for calculating the apparent reflectivity corresponding to each satellite remote sensing data in the satellite data set; the invariant pixel calibration unit 13 is configured to obtain an invariant pixel of the same geographic area according to the apparent reflectivity based on IR-MAD transformation; the decay value sequence acquisition unit 14 is configured to perform orthogonal regression on the apparent reflectivity of each spectral channel, and acquire a decay value sequence of each spectral channel of the satellite remote sensor in each time period; the modeling and analyzing unit 15 is used for establishing a remote sensor decay monitoring model according to the decay value sequence.
In the quantitative analysis system for decay of a remote sensor provided in the embodiment of the present invention, when the system is in operation, any of the quantitative analysis methods for decay of a remote sensor described in the above embodiments is executed, which is not described in detail in this embodiment.
The quantitative analysis system for decay of the remote sensor provided by the embodiment of the invention is not limited to any specific sensor or geographical area, the tracking of the satellite remote sensor on-orbit decay is completed by identifying the invariant pixel of any image in the satellite remote sensing data and effectively integrating the change information of each channel, the adaptability is strong, and the system can be applied to reprocessing of large-scale space data sets and information mining.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device may include: a processor (processor)310, a communication Interface (communication Interface)320, a memory (memory)330 and a communication bus 340, wherein the processor 310, the communication Interface 320 and the memory 330 communicate with each other via the communication bus 340. The processor 310 may call logic instructions in the memory 330 to perform the following method: projecting satellite remote sensing data of the same geographic area obtained at different times to the same geographic grid, and constructing satellite data sets of different time phases of the same geographic area; calculating the apparent reflectivity corresponding to each satellite remote sensing data in the satellite data set; acquiring invariant pixels of the same geographical area according to the apparent reflectivity based on IR-MAD transformation; performing orthogonal regression on the apparent reflectivity of each spectral channel based on the invariant pixel, and acquiring a decay value sequence of each spectral channel of the satellite remote sensor in each time period; and establishing a remote sensor decay monitoring model according to the decay value sequence.
Furthermore, the logic instructions in the memory 330 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
An embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the method provided by the method embodiments, for example, the method includes: projecting satellite remote sensing data of the same geographic area obtained at different times to the same geographic grid, and constructing satellite data sets of different time phases of the same geographic area; calculating the apparent reflectivity corresponding to each satellite remote sensing data in the satellite data set; based on IR-MAD conversion, acquiring invariant pixels of the same geographical area according to a preset invariant pixel judgment threshold; performing orthogonal regression on the apparent reflectivity of each spectral channel based on the invariant pixel, and acquiring a decay value sequence of each spectral channel of the satellite remote sensor in each time period; and establishing a remote sensor decay monitoring model according to the decay value sequence.
Embodiments of the present invention provide a non-transitory computer-readable storage medium storing server instructions, where the server instructions cause a computer to execute the method provided in the foregoing embodiments, for example, the method includes: projecting satellite remote sensing data of the same geographic area obtained at different times to the same geographic grid, and constructing satellite data sets of different time phases of the same geographic area; calculating the apparent reflectivity corresponding to each satellite remote sensing data in the satellite data set; based on IR-MAD conversion, judging a threshold value according to a preset invariant pixel, and acquiring an invariant pixel of the same geographical area; performing orthogonal regression on the apparent reflectivity of each spectral channel based on the invariant pixel, and acquiring a decay value sequence of each spectral channel of the satellite remote sensor in each time period; and establishing a remote sensor decay monitoring model according to the decay value sequence.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and of course, can also be implemented by hardware. With this understanding in mind, the above technical solutions may be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute the method according to the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for quantitative analysis of decay in a remote sensor, comprising:
projecting satellite remote sensing data of the same geographic area obtained at different times to the same geographic grid, and constructing satellite data sets of different time phases of the same geographic area;
calculating the apparent reflectivity corresponding to each satellite remote sensing data in the satellite data set;
based on IR-MAD conversion, according to a preset invariant pixel judgment threshold value, acquiring the invariant pixel of the same geographic area;
performing orthogonal regression on the apparent reflectivity of each spectral channel based on the invariant pixel, and acquiring a decay value sequence of each spectral channel of the satellite remote sensor in each time period;
and establishing a remote sensor decay monitoring model according to the decay value sequence.
2. The method of claim 1, wherein the obtaining the invariant pixel of the same geographic region based on IR-MAD transformation according to a predetermined decision threshold of the invariant pixel comprises:
acquiring satellite remote sensing data at two different moments in the satellite data set to construct an image pair;
according to the apparent reflectivity corresponding to each satellite remote sensing data, performing IR-MAD transformation on the image pair to obtain all MAD components of the image pair;
normalizing the MAD components according to the standard deviation corresponding to each MAD component;
and performing square summation on the normalized MAD components, and determining an invariant pixel on the image pair according to the preset invariant pixel judgment threshold.
3. The remote sensor decay quantitative analysis method of claim 2, wherein the determining invariant pixel values over the image pair based on the preset invariant pixel determination threshold comprises:
wherein N is the number of spectral channels,MADifor the MAD component of the ith spectral channel,
is MADiK is an invariant pixel judgment threshold;
chi-square distribution with degree of freedom N2Upper-divided bit-point values at probability prob.
4. The remote sensor decay quantitative analysis method of claim 2, wherein the obtaining of satellite telemetry data at two different times in the satellite dataset to construct an image pair comprises:
selecting two satellite remote sensing data with approximately the same ground orbit from the satellite data set to construct an image pair;
and the longitude of the intersection point of the satellite remote sensor and the equator is acquired within +/-1 degrees when the satellite remote sensor passes through the border.
5. The remote sensor decay quantitative analysis method of claim 2, wherein the obtaining satellite telemetry data at any two times in the satellite dataset to construct an image pair further comprises:
the time interval of the satellite remote sensing data at any two moments is as follows: 365.25 x y ± 20days, wherein y is 0,1,2,3, … years.
6. The remote sensor decay quantitative analysis method of claim 2, further comprising, prior to performing an IR-MAD transformation on the image pair to obtain all MAD components of the image pair:
carrying out invalid pixel mask processing and registration processing on two satellite remote sensing images in the graph pair;
the invalid pixel mask processing is carried out on the two satellite remote sensing images in the image pair, and comprises the following steps:
based on a threshold discrimination method, according to the information of each spectral channel of the image pair, eliminating invalid pixels in the image pair;
removing pixels of a water body and an ocean area in the image pair by using an ocean land mask;
according to the orbit height of the satellite corresponding to the image pair, eliminating pixels of the image pair with observation angles exceeding a preset range;
the registration processing of the two satellite remote sensing images in the image pair comprises the following steps:
taking any one satellite remote sensing image in the image pair as a reference image, moving another satellite remote sensing image in different directions, and acquiring Pearson correlation coefficients at corresponding positions;
and when the Pearson correlation coefficient is determined to be the maximum value, the relative position of the two satellite remote sensing images in the image pair is the registration position.
7. The remote sensor decay quantitative analysis method of claim 2, wherein the calculating the apparent reflectivity for each satellite remote sensing data in the satellite dataset comprises:
wherein, αiFor the scaling slope of the DN value of the ith spectral channel, βiIs the intercept of DN value of the ith spectral channel, wherein the DN value is the radiation observed value corresponding to the satellite remote sensing data, d2Is a sun-ground distance correction factor, thetasAt the zenith angle of the sun, ρiThe apparent reflectivity of the unchanged image element in the ith spectral channel is obtained.
8. The method of claim 2, wherein the performing an orthogonal regression of the apparent reflectance of each spectral channel to obtain a sequence of decay values for each spectral channel over a time period for a satellite remote sensor comprises:
wherein the content of the first and second substances,
is represented by t1And t2The invariant pixel between the pair of time point images is at t1The apparent reflectivity of the ith spectral band at time instant,
is represented by t1And t2The invariant pixel between the pair of time point images is at t2Apparent reflectance of the ith spectral band at time, SRi(t1) Represents t1Radiation response of time-of-day satellite remote sensors, SRi(t2) Represents t2Radiation response of time-of-day satellite remote sensors, mi(t1,t2) For satellite remote sensors at t1-t2Decay value of the ith spectral channel in the time period.
9. The method of claim 8, wherein the building of the remote sensor decay monitoring model according to the decay value sequence comprises a polynomial function of:
where t is the number of days from the start time of the data in the decay value sequence, M is the order of the polynomial function and Pi(0)＝1、a01, l is the order of the polynomial;
coefficient of order I of polynomiallDetermined by a non-linear least squares fit, comprising:
where j denotes the number of different image pairs and N is the number of spectral channels.
10. A system for quantitative analysis of decay of a remote sensor, comprising:
the data set acquisition unit is used for projecting the satellite remote sensing data of the same geographic area obtained at different times to the same geographic grid to construct satellite data sets of different time phases of the same geographic area;
the data conversion unit is used for calculating the apparent reflectivity corresponding to each satellite remote sensing data in the satellite data set;
the invariant pixel calibration unit is used for acquiring invariant pixels of the same geographic area according to a preset invariant pixel judgment threshold value based on IR-MAD conversion;
the decay value sequence acquisition unit is used for performing orthogonal regression on the apparent reflectivity of each spectral channel to acquire a decay value sequence of each spectral channel of the satellite remote sensor in each time period;
and the modeling and analyzing unit is used for establishing a remote sensor decay monitoring model according to the decay value sequence.
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