CN115166737A - Time sequence InSAR surface deformation data void processing method based on unmanned aerial vehicle - Google Patents
Time sequence InSAR surface deformation data void processing method based on unmanned aerial vehicle Download PDFInfo
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Abstract
The invention discloses a time sequence InSAR surface deformation data void processing method based on an unmanned aerial vehicle, which comprises the steps of 1, collecting a preposed time sequence InSAR surface deformation data grid of a target area; step 2, filling data in a post-timing InSAR surface deformation data cavity of the unmanned aerial vehicle working platform, and acquiring and processing; step 3, collecting a post-timing InSAR surface deformation data grid of the target area; step 4, filling and predicting training of a front-end time sequence InSAR surface deformation data cavity; step 5, filling holes in post-timing InSAR surface deformation data; and 6, verifying the goodness of fit of the hole filling data according to the prediction training model, and finishing the hole processing of the time sequence InSAR surface deformation data. The method can solve the problem of the time sequence InSAR surface deformation data void, realize the coupling of the time sequence InSAR surface deformation calculation result with the high-precision real surface deformation data, and complete the goodness-of-fit test of the data by depending on the algorithm.
Description
Technical Field
The invention relates to the technical field of InSAR surface deformation monitoring, in particular to a time sequence InSAR surface deformation data hole processing method based on an unmanned aerial vehicle.
Background
Monitoring of surface deformation has long received close attention from various research fields, particularly with respect to natural disasters, geophysical, geotechnical engineering, survey engineering, and the like. The surface deformation has a profound influence on human activities such as social stability evaluation, economic property risk evaluation, landform change research, engineering address exploration research and the like. The earth surface structure with high deformation amplitude and deformation rate for a long time can generate a large amount of stress and energy accumulation on the foundation structure, so that disasters such as ground collapse, landslide, debris flow and the like can be caused, the stability of the structure is further damaged, and the activity safety of human beings is threatened. Therefore, the method for making scientific, regular and effective monitoring on the surface deformation is of great significance to the development of the human society.
In recent years, synthetic Aperture Radar Interferometric Synthetic Aperture Radar (InSAR) technology is mostly used for time-series surface deformation monitoring work. The InSAR technology is derived from the Synthetic Aperture Radar (SAR) working principle. The SAR data is formed into complex pairs, and a group of complex pairs comprise a pair of amplitude and phase and are used for recording the backscattering intensity and microwave transmission period of the measured earth surface of the SAR antenna. Due to the limitation of a phase value range used for recording a period in a single SAR data format, the analysis capability of the three-dimensional structure of the earth surface is weak, and systematic application is difficult. After the development of a double-amplitude and multi-amplitude SAR image differential interference technology, the InSAR technology is proposed based on the principle of 'Young double-slit light interference experiment' of Thomas Young in 1801. When the same earth surface area is measured at a fixed timing, the SAR antennas have a phase difference between adjacent points due to a satellite orbit difference. After a plurality of SAR data are obtained, phase differences in radar image data pixels are extracted in an image pair mode, and an interference phase diagram is obtained. The interference phase diagram is further processed to obtain various data including topographic relief, surface roughness, surface texture information and surface deformation.
At present, mainstream time sequence InSAR technology is divided into a permanent scatterer interferometric method PS-InSAR (Persistent scattering) and a Small Baseline set interferometric method SBAS-InSAR (Small Baseline Subset). The PS-InSAR takes a permanent scatterer with high coherence as an analysis target, and carries out PS point positioning on a strong scatterer in a distinguishing unit to obtain point set information of surface deformation; the SBAS-InSAR reduces decorrelation and elevation error images by a time and visual angle base line control means, and surface set information of surface deformation is obtained after data is filtered, multi-view processed and phase unwrapped.
The precision range of the mainstream time sequence InSAR at present can reach millimeter level, but the common problems are as follows: the obtained surface deformation data are all discontinuous sets, and data holes exist. The reason is that there is a case where the phase span is less than one picture element, or the phase distribution is too dense due to atmospheric disturbance and DEM confusion. Although a related algorithm tries to predictively fill a data hole part from surface deformation result information at present, a data result lacks of inspection and is difficult to apply to real data acquisition work of surface deformation monitoring.
Therefore, a technical method for filling the holes in the time sequence InSAR surface deformation data is needed at present, so that the technical method can be used for real data acquisition work and the integrity and the accuracy of the surface deformation monitoring data are improved.
Disclosure of Invention
The invention provides a time sequence InSAR surface deformation data hole processing method based on an unmanned aerial vehicle, aiming at the defects of the prior art, and the time sequence InSAR surface deformation data hole processing method based on the unmanned aerial vehicle can realize the coupling of time sequence InSAR surface deformation calculation results with high-precision real surface deformation data and complete the goodness-of-fit inspection of the data by means of an algorithm.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a time sequence InSAR earth surface deformation data void processing method based on an unmanned aerial vehicle comprises the following steps.
Step 1, acquiring a preposed time sequence InSAR surface deformation data set: setting a time sequence change point of InSAR surface deformation data hole processing, wherein InSAR surface deformation data positioned in front of the time sequence change point are called pre-time sequence InSAR surface deformation data, and InSAR surface deformation data positioned behind the time sequence change point are called post-time sequence InSAR surface deformation data; and acquiring preposed time sequence InSAR surface deformation data once in each revisit period according to the determined InSAR data range, revisit period and time sequence change point, thereby obtaining a preposed time sequence InSAR surface deformation data set containing cavity position information.
Step 2, acquiring a filling data set of a rear time sequence cavity of the unmanned aerial vehicle platform: and (3) the unmanned aerial vehicle sets a flight path according to the cavity position information in the step 1, and starts from the time sequence change point in the step 1, and collects primary platform rear time sequence cavity filling data according to the set flight path when the time sequence change point of each revisit period starts according to the revisit period and the time sequence change point in the step 1, so that an unmanned aerial vehicle platform rear time sequence cavity filling data set is obtained.
Step 3, acquiring a post-timing InSAR surface deformation data set: according to the data range of the unmanned aerial vehicle platform post-timing cavity filling data set in the step 2, starting from the timing change point in the step 1, referring to the revisit period and the timing change point in the step 1, and acquiring post-timing InSAR surface deformation data once at the beginning of the timing change point of each revisit period, so that a post-timing InSAR surface deformation data set containing cavity position information is obtained.
Step 4, acquiring a preposed time sequence hole filling prediction set: and (2) predicting and filling all the holes in the pre-timing InSAR surface deformation data set obtained in the step (1), so as to form a pre-timing hole filling prediction set.
Step 5, acquiring a post-timing hole filling data set: and (3) fusing and filling the holes in the post-timing InSAR surface deformation data set obtained in the step (3) by using the post-timing hole filling data set of the unmanned aerial vehicle platform obtained in the step (2), and calling the post-timing InSAR surface deformation data set after hole filling as a post-timing hole filling data set.
Step 6, goodness of fit inspection: performing goodness-of-fit inspection on the post-sequence cavity filling data set obtained in the step 5 by adopting the pre-sequence cavity filling prediction set obtained in the step 4, and judging that the cavity filling is finished when goodness-of-fit inspection indexes meet set thresholds; otherwise, repeating the step 5 to the step 6 until the goodness-of-fit test index meets the requirement of the set threshold.
In step 5, the method for acquiring the post-timing-sequence hole filling data set Fixed set includes the following steps.
Step 51, coordinate system unify: setting the unmanned aerial vehicle platform post-timing hole filling data set obtained in the step 2 as a Patch set, and setting the post-timing InSAR surface deformation data set obtained in the step 3 as a Raw set; next, the Patch set and the Raw set are projected and converted to the same coordinate system.
Step 52, raw set boundary extraction: boundary extraction is performed for each hole in the Raw set.
Step 53, patch set pixel clipping: and performing pixel clipping processing on the Patch set.
Step 54, generating Fixed set: and (4) performing grid data fusion on the Raw set after the boundary is extracted in the step 52 and the Patch set after the pixel is cut in the step 53, so as to form a Fixed set.
In step 54, the Raw set and the Patch set perform the grid data fusion method, which includes the following steps.
Step 54-1, matching the gray level of the pixels: according to the pixel gray matching method, embedding and covering are carried out on pixels in the Patch set, and objects are corresponding holes extracted through boundaries in the Raw set.
Step 54-2, boundary smoothing: smoothing each hole boundary embedded with a pixel element to form a Fixed set.
In step 54-2, the method for smoothing the edge of each hole includes the following steps.
Step 54-2A, calculating point location weight: respectively calculating point location weights of each pixel in the boundary field of each cavity; the point location weight M is calculated according to the formula.
In the formula, w 1 And w 2 All are weight coefficients, set values; a is the earth surface deformation data corresponding to the current pixel;
the average value of the surface deformation data corresponding to all pixels in the current cavity boundary field is obtained; b is the distance from the current pixel to the boundary of the corresponding cavity;
and representing the average value of the distance from the center point of the corresponding hole to the boundary of the current hole.
Step 54-2B, smoothing: and carrying out smooth calculation on the current pixel by adopting a Gaussian kernel function for the pixel with the point location weight higher than the set weight threshold, and assigning the calculation result to the current pixel to realize the smoothness of the current pixel.
In the step 1, the method for acquiring the surface deformation data set of the preposed time sequence InSAR comprises the following steps.
Step 11, determining the range of the target area: and setting the surface deformation data range and the geographic position of the preposed time sequence InSAR to be collected.
Step 12, determining a revisit period: determining the type of an InSAR data system according to the geographical position of the earth surface to be filled; and determining a revisit period according to the satellite model corresponding to the InSAR data system.
Step 13, positioning a time sequence change point: and positioning a time sequence change point according to the continuously measured preposed time sequence InSAR surface deformation data.
Step 14, producing a preposed time sequence InSAR surface deformation data set: and in each revisit period, acquiring primary preposed time sequence InSAR earth surface deformation data by using a corresponding InSAR data system, thereby obtaining a preposed time sequence InSAR earth surface deformation data set containing cavity position information.
In step 14, the preposed time sequence InSAR surface deformation data set comprises at least 15 preposed time sequence InSAR surface deformation data with uniform time sequence intervals; in step 12, the method for determining the type of the InSAR data system is as follows.
A. When the floor area of the artificial structure in the geographical position of the earth surface to be filled exceeds a set value or the relief degree of the terrain is smooth, the InSAR data system uses a PS-InSAR system.
B. And when the floor area of the artificial structure in the geographical position of the earth surface to be filled does not exceed a set value or the relief degree of the terrain is steep, the InSAR data system uses SBAS-InSAR.
In step 2, a radar signal source and a wave band switcher are arranged in the unmanned aerial vehicle.
The radar signal source can generate a radar signal source with a specified wave band, and the coverage range of the radar wave band comprises a space wave radar with the wavelength range of 0.1 meter to 1 meter.
The waveband switcher can switch the wavelength range of the current working radar signal source.
And 4, predicting and filling all the holes in the pre-sequence InSAR surface deformation data set obtained in the step 1 by generating an ROC curve and adopting the ROC curve to perform interpolation prediction.
In step 6, the goodness-of-fit test index is a coefficient of certainty R 2 。
In step 6, if the preposed time sequence hole filling prediction set obtained in step 4 is set as valid set, then R 2 The calculation formula of (2) is as follows.
In the formula, RSS is the sum of squares of residual errors between a Validation set and a Fixed set; TSS is the total deviation of Validation set from Fixed set.
The invention has the following beneficial effects:
1. the method has good investigation effect on reflection information of various ground objects through the multi-band radar wavelength emission range contained in the ultrahigh frequency radar, and has good matching property with metadata formats of various InSAR data acquisition platforms.
2. Through the cooperative work of the unmanned aerial vehicle working platform and the ground information processing platform, the problem that the radar original data is difficult to interpret and judge the usability is solved.
3. The requirement that the randomness of the filling position of the data hole is met through the high maneuverability of the unmanned aerial vehicle platform, and the problem that the usability of the time sequence InSAR surface deformation data is unstable is solved.
4. By coupling high-precision real surface deformation data obtained by sampling of an unmanned aerial vehicle with a time sequence InSAR surface deformation calculation result, the problem that a surface deformation data hole is difficult to generate filling data with high availability and reliability by training of an InSAR data algorithm is solved.
5. And the fitting goodness of filling data is checked by matching the front InSAR time sequence data and the rear InSAR time sequence data with the sampling data of the unmanned aerial vehicle, so that the problem of filling confidence control of surface deformation data cavities is solved.
Drawings
Fig. 1 shows a flow chart of the method for processing the void of the time sequence InSAR surface deformation data based on the unmanned aerial vehicle.
Fig. 2 shows a schematic diagram of the unmanned-aerial-vehicle-based time-series InSAR earth surface deformation data void processing work system.
Fig. 3 shows an InSAR raw data intercept for implementing the processing method of the present invention.
Fig. 4 shows the implementation object interception, time sequence InSAR data holes of the processing method of the present invention.
Fig. 5 shows the effect of intercepting results and filling timing-sequence InSAR data holes by implementing the processing method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and specific preferred embodiments.
In the description of the present invention, it is to be understood that the terms "left side", "right side", "upper part", "lower part", etc., indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and that "first", "second", etc., do not represent an important degree of the component parts, and thus are not to be construed as limiting the present invention. The specific dimensions used in the present example are only for illustrating the technical solution and do not limit the protection scope of the present invention.
In the embodiment, a landslide and a debris flow prone mountain area are taken as research objects, and the effectiveness of the filling work of the surface deformation data cavity in the research area is verified through the experiment of the invention.
As shown in fig. 2, the time sequence InSAR earth surface deformation data void processing work system based on the unmanned aerial vehicle includes:
unmanned aerial vehicle of many rotors, unmanned aerial vehicle carry on earth's surface three-dimensional information acquisition module, data storage module, data transmission module and ground information processing platform.
The earth's surface three-dimensional information acquisition module that unmanned aerial vehicle platform carried on includes:
the ultrahigh frequency radar signal source is used for generating a radar signal source of a specified waveband, the coverage range of the radar waveband comprises a space wave radar with the wavelength range of 0.1 m to 1 m, and the ultrahigh frequency radar signal source has good adaptability with the currently mainstream InSAR data acquisition signals of C waveband and L waveband.
And the band switcher is used for switching the wavelength range of the currently working radar signal source.
And the transmitter is used for transmitting the radar signal of the specified wave band.
And the receiver is used for receiving the transmitted radar signals after the radar signals are absorbed and backscattered by the earth surface.
And the signal converter is used for converting the radar signals received by the receiver into data with a special format which can be recognized by a computer.
The data storage module that unmanned aerial vehicle platform carried on includes:
and the high-performance solid state disk is used for storing the primary data acquired by the ground surface information of the unmanned aerial vehicle platform after primary signal processing.
And the portable power supply is used for supplying power to the micro central processing computing unit and the high-performance solid state disk, so that data damage caused by unstable power supply is prevented.
And the micro central processing and calculating unit is used for calculating and processing radar signal original data processing, data compression and storage processing and data transmission processing of original data quality control information to the ground information processing platform.
The ground information processing platform comprises:
and the high-performance computing workstation is used for performing data transfer and decompression of the original data, processing the subsequent time sequence InSAR surface deformation data and monitoring quality control information in the unmanned aerial vehicle data acquisition process.
And the display unit is used for displaying quality control information in the information acquisition process of the unmanned aerial vehicle and displaying subsequent time sequence InSAR earth surface deformation data processing results.
The beneficial effect of above-mentioned operating system does: by utilizing the high maneuverability and the high adaptability of a working mode of the unmanned aerial vehicle platform, different radar signal sources are automatically switched according to needs, and earth surface information acquisition is carried out on various terrain environments; through signal switching, a multiband radar signal source is generated, the multiband radar signal source can be compatible with a radar signal band adopted by a current mainstream InSAR data acquisition platform, and the accuracy of data hole filling work is improved.
The method for processing the time sequence InSAR surface deformation data void based on the unmanned aerial vehicle comprises the following steps.
Step 1, acquiring a preposed time sequence InSAR (interferometric synthetic Aperture Radar) surface deformation data set: setting a time sequence change point of hole processing of the InSAR surface deformation data, calling the InSAR surface deformation data before the time sequence change point as preposed time sequence InSAR surface deformation data, and calling the InSAR surface deformation data behind the time sequence change point as postposition time sequence InSAR surface deformation data; and acquiring preposed time sequence InSAR surface deformation data once in each revising period according to the determined InSAR data range, revising period and time sequence change point, thereby obtaining a preposed time sequence InSAR surface deformation data set containing the hole position information.
The method for acquiring the surface deformation data set of the preposed time sequence InSAR preferably comprises the following steps.
Step 11, determining the range of the target area: and setting the surface deformation data range and the geographic position of the preposed time sequence InSAR to be collected.
Step 12, determining a revisit period: determining the type of an InSAR data system according to the geographical position of the earth surface to be filled; and determining a revisit period according to the satellite model corresponding to the InSAR data system.
Step 13, positioning a time sequence change point: and positioning a time sequence change point according to the continuously measured preposed time sequence InSAR earth surface deformation data. The time sequence change point is a time node used for distinguishing a front time sequence InSAR and a rear time sequence InSAR, the positioning meaning is that different satellite data sources have different revisiting cycles, and when InSAR data acquisition of different time periods is carried out in the same area, the time sequence change point is used for fixing front and rear time sequence switching of each time period.
Step 14, producing a preposed time sequence InSAR surface deformation data set: and in each revisit period, acquiring primary preposed time sequence InSAR earth surface deformation data by using a corresponding InSAR data system, thereby obtaining a preposed time sequence InSAR earth surface deformation data set containing cavity position information.
In step 14, the pre-timing-sequence InSAR surface deformation data set comprises at least 15 pieces of pre-timing-sequence InSAR surface deformation data with uniform time-sequence intervals; in step 12, the method for determining the type of the InSAR data system includes:
A. when the occupied area of the artificial structure in the geographical position of the earth surface to be filled exceeds a set value or the relief degree of the terrain is gentle, the InSAR data system uses a PS-InSAR system.
B. When the occupied area of the artificial structure in the geographical position of the earth surface to be filled does not exceed a set value or the relief degree of the terrain is steep, the InSAR data system uses SBAS-InSAR.
Step 2, acquiring a post-timing sequence hole filling data set of the unmanned aerial vehicle platform: the unmanned aerial vehicle sets a flight path according to the hole position information in the step 1, and starts from the time sequence change point in the step 1, and collects one-time post-stage time sequence hole filling data according to the set flight path when the time sequence change point of each revisit period starts by referring to the revisit period and the time sequence change point in the step 1, so that an unmanned aerial vehicle platform post-stage time sequence hole filling data set is obtained.
And step 21, determining a data acquisition track of the unmanned aerial vehicle through the GNSS and the track planning module of the unmanned aerial vehicle platform and the cavity position information in the earth surface deformation preposed time sequence data set with the cavity generated in the step 14, and formulating a data acquisition track planning scheme of the unmanned aerial vehicle in the target area.
And step 22, fitting the revisit period and the positioned time sequence change points of the InSAR data acquisition platform selected in the step 1 through a time sequence calculation module of the unmanned aerial vehicle platform, and formulating an unmanned aerial vehicle data acquisition time sequence scheme.
Step 23, performing post-sequence data acquisition on the target area through a data acquisition module of the unmanned aerial vehicle platform according to the track planning scheme and the time sequence scheme formulated in the steps 21 and 22; data are stored in a data storage module of the unmanned aerial vehicle platform.
And step 24, transmitting the data obtained in the step 23 to an information processing platform through a data storage module of the unmanned aerial vehicle platform, and generating a hole filling data set after the data processing step.
The method for generating the hole filling data set in step 24 preferably includes the following sub-steps.
And step 24-1, transmitting the data stored by the unmanned aerial vehicle platform to an information processing platform, wherein the data comprises the acquired post-timing-sequence hole filling data set, unmanned aerial vehicle position information and a data acquisition timing sequence scheme.
And 24-2, carrying out standardization processing on the data format and unifying the geographic coordinate system.
And 24-3, performing post time sequence combing on the data according to the time sequence scheme so as to accurately align the AR data acquisition platform In the In step.
And 24-4, carrying out simplification processing on the redundant data to eliminate data flaws and noises.
And 24-5, generating a ground elevation texture original model taking the processed time sequence as an axis.
And 24-6, correcting the ground elevation texture original model according to the position information of the unmanned aerial vehicle.
And 24-7, generating a post-time-sequence earth surface deformation data set as a hole filling data set according to the time sequence scheme.
The beneficial effects of the above further scheme are: the time sequence calculation takes the revisit period of the InSAR data acquisition platform as a source, and the data acquisition work of the unmanned aerial vehicle platform and the work of the InSAR data acquisition platform generate space-time intersection, so that space-time sequences in the filling process of data holes are aligned, and the final data finished product can be more accurate.
Step 3, acquiring a post-timing InSAR surface deformation data set: according to the data range of the unmanned aerial vehicle platform post-timing cavity filling data set in the step 2, starting from the timing change point in the step 1, referring to the revisit period and the timing change point in the step 1, and acquiring post-timing InSAR surface deformation data once at the beginning of the timing change point of each revisit period, so that a post-timing InSAR surface deformation data set containing cavity position information is obtained.
The method for acquiring the post-timing-sequence InSAR surface deformation data set specifically refers to step 1, and will not be described herein again.
Step 4, acquiring a preposed time sequence hole filling prediction set: and (3) predicting and filling all the holes in the surface deformation data set of the leading time sequence InSAR obtained in the step (1), so as to form a leading time sequence hole filling prediction set.
Further: preferably, all the holes in the pre-sequence InSAR surface deformation data set obtained in the step 1 are predicted and filled by a method of generating an ROC curve and performing interpolation prediction by adopting the ROC curve.
The generation of ROC curves and interpolation prediction are prior art, and in the present invention, it preferably includes the following sub-steps:
and 41, according to the generated surface deformation preposed time sequence data set with the cavity, carrying out processing steps of mean value calculation, exponential decay, period prediction, data slippage, third-order exponential smoothing and the like by using a time sequence prediction algorithm.
And 42, performing differential autoregressive smoothing processing on the data set according to the processing result of the step 41.
And 43, carrying out white noise detection on the data set obtained by the processing in the step 42, and verifying that the sequence after the difference is non-white noise.
And step 44, determining the period of the prediction model, fitting the residual error, and checking the significance of the model and the parameters.
And step 45, obtaining a model prediction result, named as Validation set, and evaluating the model prediction effect according to the ROC curve.
Further: the above step 42 preferably comprises the following substeps.
And 42-1, differentiating the processed data set, and performing autoregressive smoothing processing on coefficients (p, d and q), wherein p-order autoregression, d-order differentiation and q-order smoothing are respectively adopted.
And 42-2, performing unit root inspection on the data after the difference, acquiring 1%, 5% and 10% rejection original hypothesis statistics and ADF Test Result comparison results, and inspecting p-value to verify the stationarity of the data set.
Step 42-3, judging whether difference is continuously carried out or not according to the p-value detection result of the step 42-2, if so, judging that the data set is stable, and then, going to step 43; otherwise, judging that the data set is not stable, and repeating the steps 42-1 to 42-3.
The beneficial effects of the above further scheme are: the generated front time sequence InSAR surface deformation data cavity is subjected to predictive training through a time sequence prediction model, a verification data set used in the subsequent steps is generated, reflection reference of the prediction data on the real surface deformation condition is carried out, and the surface deformation occurrence trend under the natural condition is subjected to controlled reference in data fitting after the data is collected and processed by an unmanned aerial vehicle, so that the stability of a working system for filling the data cavity is improved, and the usability of the filling result of the rear time sequence InSAR surface deformation data cavity is ensured.
Step 5, acquiring a post-timing cavity filling data set: and (3) fusing and filling the holes in the post-timing InSAR surface deformation data set obtained in the step (3) by using the post-timing hole filling data set of the unmanned aerial vehicle platform obtained in the step (2), and calling the post-timing InSAR surface deformation data set after hole filling as a post-timing hole filling data set.
The method for acquiring the post time sequence hole filling data set Fixed set comprises the following steps.
Step 51, coordinate system unify: setting the unmanned aerial vehicle platform post-timing cavity filling data set obtained in the step 2 as a Patch set, and setting the post-timing InSAR earth surface deformation data set obtained in the step 3 as a Raw set; next, the Patch set and the Raw set are projected and converted to the same coordinate system.
Step 52, raw set boundary extraction: boundary extraction is performed for each hole in Raw set.
Step 53, patch set pixel clipping: and performing pixel clipping processing on the Patch set.
Step 54, generate Fixed set: and (4) performing grid data fusion on the Raw set after the boundary is extracted in the step 52 and the Patch set after the pixel is cut in the step 53, so as to form a Fixed set.
In step 54, the Raw set and the Patch set perform the grid data fusion method, which includes the following steps.
Step 54-1, pixel gray matching: according to the pixel gray matching method, embedding and covering are carried out on pixels in the Patch set, and objects are corresponding holes extracted through boundaries in the Raw set.
Step 54-2, boundary smoothing: smoothing is performed on each hole boundary embedded with the pixel element, thereby forming a Fixed set.
In step 54-2, the method for smoothing the edge of each hole includes the following steps.
Step 54-2A, calculating point location weight: respectively calculating point location weights of each pixel in the boundary field of each cavity; the point location weight M is calculated according to the formula.
In the formula, w 1 And w 2 All are weight coefficients, set values; a is the earth surface deformation data corresponding to the current pixel;
the average value of the surface deformation data corresponding to all pixels in the current cavity boundary field is obtained; b is the distance from the current pixel to the boundary of the corresponding cavity;
and representing the average value of the distance from the center point of the corresponding hole to the boundary of the current hole.
Step 54-2B, smoothing: performing smooth calculation on the current pixel by adopting a Gaussian kernel function for the pixel with the point location weight higher than the set weight threshold, and assigning the calculation result to the current pixel to realize the smoothness of the current pixel; if the smoothing processing effect of the current pixel cannot well represent the transition effect of the boundary field, different step lengths are tried, and the current step is repeated until the smoothing of the corresponding pixel is completed.
The beneficial effects of the above further scheme are: through post-timing InSAR surface deformation cavity edge monitoring and boundary data smoothing, the surface condition of special terrains at the edge of the cavity, such as cutting, valley, ridge and the like, which have terrain mutation, is reflected to be closer to real landform. According to data calculation and estimation, the fitting degree of the processing result of the hole boundary neighborhood data with the fitted surface curvature value larger than 20% by using the processing method compared with the actually-measured hole boundary neighborhood surface deformation data is superior to that of the data result which is not processed by the method.
Step 6, goodness of fit inspection: performing goodness-of-fit inspection on the post-sequence cavity filling data set obtained in the step 5 by adopting the pre-sequence cavity filling prediction set obtained in the step 4, and judging that the cavity filling is finished when goodness-of-fit inspection indexes meet set thresholds; otherwise, repeating the step 5 to the step 6 until the goodness-of-fit inspection index meets the requirement of the set threshold.
The goodness-of-fit test index is a coefficient of determinability R 2 If the pre-timing hole filling prediction set obtained in step 4 is deemed valid set, then R 2 The calculation formula of (2) is as follows:
wherein RSS is the sum of squares of residuals of Validation set and Fixed set; TSS is the total deviation of the Validation set from the Fixed set.
In the formula, R 2 Has a value range of [0,1 ]]When the distance is closer to 1, it is proved that the goodness of fit is higher as the actual observation point is closer to the sample line.
As shown in fig. 3, the timing change point is recorded, and the timing InSAR raw data of the pre-timing and the post-timing is collected and processed by the steps of the processing method of the present invention. The time sequence InSAR processing mode is SBAS-InSAR. And establishing a connection between the time baseline and the space baseline. And carrying out difference processing on the interference phase, and obtaining an interference coefficient and an interference pattern after processing operation. And acquiring the hidden main phase at the pixel position with better interference effect by phase unwrapping. Whether the modification of the time base line and the space base line is continued or not is determined through interpreting the unwrapping result so as to obtain better phase interference effect and phase unwrapping result. And after processing, acquiring a time sequence SBAS-InSAR surface deformation data set of a front time sequence and a rear time sequence, and displaying a result to select a surface average displacement rate result data grid. After the pre-sequence SBAS-InSAR surface deformation data set is processed in the S4 step, the beneficial effect in the embodiment is that the better reference performance is provided for the goodness-of-fit judgment of the subsequent steps in the evaluation of the mountain surface deformation on the millimeter scale with the data which is not processed in the S4 step and uses the conventional interpolation only according to the surface deformation occurrence trend under the natural condition as the data set reference standard.
As shown in fig. 4, the time sequence SBAS-InSAR surface deformation rate result data grid shows obvious data holes. By using the processing method disclosed by the invention, time sequence predictive model training is carried out on the preposed time sequence InSAR surface deformation data set to generate a cavity filling prediction set for the goodness-of-fit verification of the cavity filling data set; by using the processing method, a data set which is acquired by an unmanned aerial vehicle platform and used for filling the surface deformation hole is imported, the data set is subjected to operations such as projection coordinate system conversion, resampling, boundary extraction and the like, the surface deformation hole filling data set is obtained, the hole filling effectiveness is verified through boundary gray level matching inspection, and edge smoothing calculation is performed on a boundary neighborhood according to an inspection result. The calculation result shows that the data which is not used in the method is compared with the data of the result of edge smoothing processing by using the boundary gray matching test proposed by the method for the part of the research area with the fitted surface curvature value larger than 20 percent, the reflection of the surface deformation is closer to the real mountain landform condition, and the reflection details of the surface condition with the terrain mutation of special terrains at the edge of the cavity, such as trench cuts, valleys, ridges and the like, are more obvious.
As shown in fig. 5, the hole filling of the time sequence InSAR data implemented by the processing method of the present invention completes the hole processing of the time sequence InSAR surface deformation data after the goodness-of-fit test. The annual average rate of surface deformation in the hole filling data is in mm/year, and the data range is-contrast unprocessed data.
The implementation process of the invention is as follows: acquiring a preposed time sequence InSAR (interferometric synthetic Aperture Radar) surface deformation data grid of a target area; filling a data cavity in the surface deformation data of the InSAR with a post-positioned time sequence of the unmanned aerial vehicle working platform for data acquisition and processing; acquiring a post-timing InSAR (interferometric synthetic Aperture Radar) surface deformation data grid of a target area; filling and predicting training of a front time sequence InSAR surface deformation data hole; filling and processing a post-timing InSAR surface deformation data cavity; and verifying the goodness of fit of the hole filling data according to the prediction training model, and finishing the hole processing of the time sequence InSAR surface deformation data.
The present invention has been described in detail through the embodiments. It should be understood that the terms "Patch set", "validity set", "Raw set", "Fixed set", etc. refer to data set names for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the referred data set must be named as a specific name and thus should not be construed as limiting the present invention. Thus, the data sets defining the "Patch set", "valid set", "Raw set", "Fixed set" may explicitly or implicitly include one or more of the data processing sets provided by the present invention.
Although the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the details of the embodiments, and various equivalent modifications can be made within the technical spirit of the present invention, and the scope of the present invention is also within the scope of the present invention.
Claims (10)
1. A time sequence InSAR surface deformation data void processing method based on an unmanned aerial vehicle is characterized in that: the method comprises the following steps:
step 1, acquiring a preposed time sequence InSAR (interferometric synthetic Aperture Radar) surface deformation data set: setting a time sequence change point of hole processing of the InSAR surface deformation data, calling the InSAR surface deformation data before the time sequence change point as preposed time sequence InSAR surface deformation data, and calling the InSAR surface deformation data behind the time sequence change point as postposition time sequence InSAR surface deformation data; acquiring preposed time sequence InSAR surface deformation data once in each revisit period according to the determined InSAR data range, revisit period and time sequence change point, thereby obtaining a preposed time sequence InSAR surface deformation data set containing cavity position information;
step 2, acquiring a filling data set of a rear time sequence cavity of the unmanned aerial vehicle platform: the unmanned aerial vehicle sets a flight path according to the hole position information in the step 1, and acquires the post-positioned time sequence hole filling data of the unmanned aerial vehicle platform according to the set flight path when the time sequence change point of each revisit period starts by referring to the revisit period and the time sequence change point in the step 1 from the time sequence change point in the step 1, so that a post-positioned time sequence hole filling data set of the unmanned aerial vehicle platform is obtained;
step 3, acquiring a post-timing InSAR surface deformation data set: according to the data range of the unmanned aerial vehicle platform post-timing hole filling data set in the step 2, starting from the timing change point in the step 1, referring to the revisit period and the timing change point in the step 1, and acquiring post-timing InSAR surface deformation data once when the timing change point of each revisit period starts, so as to obtain a post-timing InSAR surface deformation data set containing hole position information;
step 4, acquiring a preposed time sequence hole filling prediction set: predicting and filling all the holes in the surface deformation data set of the preposed time sequence InSAR obtained in the step 1, thereby forming a preposed time sequence hole filling prediction set;
step 5, acquiring a post-timing hole filling data set: using the unmanned aerial vehicle platform post-timing-sequence hole filling data set obtained in the step 2, fusing and filling holes in the post-timing-sequence InSAR surface deformation data set obtained in the step 3, and calling the post-timing-sequence InSAR surface deformation data set after hole filling as a post-timing-sequence hole filling data set;
step 6, goodness of fit inspection: performing goodness-of-fit inspection on the post-sequence cavity filling data set obtained in the step 5 by adopting the pre-sequence cavity filling prediction set obtained in the step 4, and judging that the cavity filling is finished when goodness-of-fit inspection indexes meet set thresholds; otherwise, repeating the step 5 to the step 6 until the goodness-of-fit test index meets the requirement of the set threshold.
2. The unmanned aerial vehicle-based time sequence InSAR surface deformation data void processing method according to claim 1, characterized in that: in step 5, the method for acquiring the post-timing-sequence hole filling data set Fixed set includes the following steps:
step 51, coordinate system unify: setting the unmanned aerial vehicle platform post-timing cavity filling data set obtained in the step 2 as a Patch set, and setting the post-timing InSAR earth surface deformation data set obtained in the step 3 as a Raw set; then, respectively projecting the Patch set and the Raw set and converting the Patch set and the Raw set to the same coordinate system;
step 52, raw set boundary extraction: extracting the boundary of each cavity in the Raw set;
step 53, cutting Patch set pixels: pixel clipping processing is carried out on the Patch set;
step 54, generate Fixed set: and (4) performing grid data fusion on the Raw set after the boundary is extracted in the step 52 and the Patch set after the pixel is cut in the step 53, so as to form a Fixed set.
3. The unmanned aerial vehicle-based time sequence InSAR surface deformation data void processing method according to claim 2, characterized in that: in step 54, the method for performing grid data fusion between Raw set and Patch set includes the following steps:
step 54-1, pixel gray matching: embedding and covering pixels in a Patch set according to a pixel gray matching method, wherein the objects are corresponding holes extracted from boundaries in a Raw set;
step 54-2, boundary smoothing: smoothing each hole boundary embedded with a pixel element to form a Fixed set.
4. The unmanned aerial vehicle-based time sequence InSAR earth surface deformation data hole processing method according to claim 3, characterized in that: in step 54-2, the method for smoothing the edge of each hole includes the following steps:
step 54-2A, calculating point location weight: respectively calculating point location weights of each pixel in the boundary field of each cavity; the calculation formula of the point location weight M is as follows:
in the formula, w 1 And w 2 All are weight coefficients, set values; a is the earth surface deformation data corresponding to the current pixel;
the average value of the surface deformation data corresponding to all pixels in the current cavity boundary field is obtained; b is the distance from the current pixel to the boundary of the corresponding cavity;
representing the average value of the distance from the center point of the corresponding cavity to the boundary of the current cavity;
step 54-2B, smoothing: and carrying out smooth calculation on the current pixel by adopting a Gaussian kernel function for the pixel with the point location weight higher than the set weight threshold, and assigning the calculation result to the current pixel to realize the smoothness of the current pixel.
5. The unmanned aerial vehicle-based time sequence InSAR earth surface deformation data hole processing method according to claim 1, characterized in that: in step 1, the method for acquiring the surface deformation data set of the preposed time sequence InSAR comprises the following steps:
step 11, determining the range of the target area: setting a preposed time sequence InSAR earth surface deformation data range and a geographical position which need to be collected;
step 12, determining a revisit period: determining the type of an InSAR data system according to the geographical position of the earth surface to be filled; determining a revisit period according to the satellite model corresponding to the InSAR data system;
step 13, positioning a time sequence change point: positioning a time sequence change point according to continuously measured preposed time sequence InSAR surface deformation data;
step 14, producing a preposed time sequence InSAR surface deformation data set: and in each revisit period, acquiring the surface deformation data of the preposed time sequence InSAR once by using a corresponding InSAR data system, thereby obtaining a preposed time sequence InSAR surface deformation data set containing the cavity position information.
6. The unmanned aerial vehicle-based time sequence InSAR earth surface deformation data hole processing method according to claim 5, characterized in that: in step 14, the preposed time sequence InSAR surface deformation data set comprises at least 15 preposed time sequence InSAR surface deformation data with uniform time sequence intervals; in step 12, the method for determining the type of the InSAR data system includes:
A. when the floor area of the artificial structure in the geographical position of the earth surface to be filled exceeds a set value or the relief degree of the terrain is smooth, the InSAR data system uses a PS-InSAR system;
B. and when the floor area of the artificial structure in the geographical position of the earth surface to be filled does not exceed a set value or the relief degree of the terrain is steep, the InSAR data system uses SBAS-InSAR.
7. The unmanned aerial vehicle-based time sequence InSAR surface deformation data void processing method according to claim 1, characterized in that:
in the step 2, a radar signal source and a wave band switcher are arranged in the unmanned aerial vehicle;
the radar signal source can generate a radar signal source of a specified wave band, and the coverage range of the radar wave band comprises a space wave radar with the wavelength range of 0.1 meter to 1 meter;
the waveband switcher can switch the wavelength range of the current working radar signal source.
8. The unmanned aerial vehicle-based time sequence InSAR earth surface deformation data hole processing method according to claim 1, characterized in that: and 4, predicting and filling all the holes in the pre-sequence InSAR surface deformation data set obtained in the step 1 by generating an ROC curve and adopting the ROC curve to perform interpolation prediction.
9. The unmanned aerial vehicle-based time sequence InSAR earth surface deformation data hole processing method according to claim 2, characterized in that: in step 6, the goodness-of-fit test index is a coefficient of certainty R 2 。
10. The unmanned aerial vehicle-based time sequence InSAR surface deformation data hole processing method according to claim 9, characterized in that: in step 6, if the pre-timing hole filling prediction set obtained in step 4 is deemed as valid set, then R is 2 The calculation formula of (c) is:
wherein RSS is the sum of squares of residuals of Validation set and Fixed set; TSS is the total deviation of the Validation set from the Fixed set.
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