Computer system and method for a multi-sensor SAR integrated three-dimensional decomposition acquisition planner

Abstract

Systems and methods for 3D acquisition planning are provided. A system includes a receiving device, a data processing device, and a user device. The receiving device is configured to receive user input including area of interest (AOI) and timeframe. The data processing device is configured to generate a list of possible synthetic aperture radar (SAR) acquisitions for user-taskable satellites over AOI for timeframe, with associated ground footprints, spatial resolution, and geometry information; simulate deformation and expected noise over AOI; project deformation to SAR line-of-sight for each possible acquisition geometry; for different combinations of three or more acquisition geometries, perform 3D decomposition on simulated noisy data, and compute north / south, east / west, and vertical error; rank the combinations by minimized error across the three dimensions over the AOI; and generate an output including a report and order information for a set of ranked combinations. The user device is configured to display the output.

Description

COMPUTER SYSTEM AND METHOD FOR A MULTI-SENSOR SAR INTEGRATED THREE-DIMENSIONAL DECOMPOSITION ACQUISITION PLANNERTechnical Field

[0001] The following relates generally to Earth observation data acquisition planning, and more particularly to systems and methods for a Synthetic Aperture Radar (SAR) multi-sensor integrated three-dimensional decomposition acquisition planner.Introduction

[0002] Interferometric Synthetic Aperture Radar (InSAR) is a powerful technology that provides high-resolution, wide area, and high accuracy ground surface deformation measurements. It relies upon a set of images acquired over the same area at different times using nearly identical imaging geometry (a “stack”). As ground deformation is a 3- Dimensional (3D) process, the ability of InSAR to measure ground deformation depends on the available lines of sight supported by the satellite orbit and imaging geometry.

[0003] One of the limitations of InSAR is that a given stack can only measure a one-dimensional motion along a single radar line-of-sight (LOS). Such limitations around the information provided can lead to challenging interpretation of results, limited assessment of geo-hazards and ambiguous modelling processes. Further, entities may be interested in the full three-dimensional (3D) displacements.

[0004] By combining stacks with multiple viewing geometries, it is possible to retrieve 3D deformation. Currently, almost all operational spaceborne SAR sensors acquire image stacks from sun-synchronous orbits (SSO), mostly using right-looking modes. Over low to mid latitudes, available lines of sight (LOS) are suitable for measuring east-west and vertical deformation but are poor for measuring north-south deformation, even with multiple stacks acquired at different incidence angles, pass (ascending / descending) and look (left / right) directions. At higher latitudes north-south sensitivity improves and east-west sensitivity reduces enabling 3-D deformation over a narrow range of sites. Upcoming operational missions in mid-inclination orbits will provide an opportunity to improve the 3-D sensitivities at lower latitudes.

[0005] Accordingly, there is a need for an improved system and method for data acquisition planning to overcome at least some of the disadvantages of existing systems and methods.Summary

[0006] A system for 3D acquisition planning is provided. The system includes a receiving device, a data processing device, and a user device. The receiving device is configured to receive a user input including an area of interest (AOI) and a timeframe. The data processing device is configured to generate a list of possible synthetic aperture radar (SAR) acquisitions for user-taskable satellites over the AOI for the timeframe, with associated ground footprints, spatial resolution, and geometry information; simulate deformation and expected noise over the AOI; project deformation to a SAR line-of-sight for each possible acquisition geometry; for different combinations of three or more acquisition geometries, perform 3D decomposition on simulated noisy data, and compute north / south, east / west, and vertical error; rank the combinations by minimized error across the three dimensions over the AOI; and generate an output including a report and order information for a set of the ranked combinations. The user device is configured to display the output.

[0007] In an embodiment, the data processing device is further configured to generate a list of likely open SAR data acquisitions over the AOI for the timeframe, with an associated ground footprint, spatial resolution, and geometry information.

[0008] In an embodiment, user-taskable satellites includes at least one satellite in a mid-inclination orbit.

[0009] In an embodiment, the geometry information includes at least one of: a viewing geometry; a beam position; an incidence angle; a look direction; a pass direction; and a heading from orbit information.

[0010] In an embodiment, the report includes at least one of: simulation figures for the set of the ranked combinations; and tables of estimated error sensitivities for the set of the ranked combinations.

[0011] In an embodiment, order information includes at least one of: satellite information; incidence angle; metadata associated with acquisition of a data set; a swath ID; a downlink facility; and other information or files to facilitate easy ordering of data for a selected combination.

[0012] In an embodiment, the user input further includes a type of deformation to simulate.

[0013] A method of 3D acquisition planning is provided. The method includes receiving a user input including an area of interest (AOI), and a timeframe. The method further includes generating a list of possible synthetic aperture radar (SAR) acquisitions for user-taskable satellites over the AOI for the timeframe, with associated ground footprints, spatial resolution, and geometry information. The method further includes for different combinations of acquisition geometries, computing north / south, east / west, and vertical sensitivities based on orthogonality of viewing geometries. The method further includes ranking combinations by minimized error across three dimensions. The method further includes generating an output including a report and order information for a set of the ranked combinations. The method further includes displaying the output at a user device.

[0014] In an embodiment, the method further includes generating a list of likely open SAR data acquisitions over the AOI for the timeframe, with associated ground footprints, spatial resolution, and geometry information.

[0015] In an embodiment, user-taskable satellites includes at least one satellite in a mid-inclination orbit.

[0016] In an embodiment, the geometry information includes at least one of: a viewing geometry; a beam position; an incidence angle; a look direction; a pass direction; and a heading from orbit information.

[0017] In an embodiment, the report includes at least one of: simulation figures for the set of the ranked combinations; and tables of estimated error sensitivities for the set of the ranked combinations.

[0018] In an embodiment, the order information includes at least one of: satellite information; incidence angle; metadata associated with acquisition of a data set; a swath ID; a downlink facility; and other information or files to facilitate easy ordering of data for a selected combination.

[0019] A method of 3D acquisition planning is provided. The method includes receiving a user input including an area of interest (AOI), and a timeframe. The method further includes generating a list of possible synthetic aperture radar (SAR) acquisitions for user-taskable satellites over the AOI for the timeframe, with associated ground footprints, spatial resolution, and geometry information. The method further includes simulating deformation and expected noise over the AOI. The method further includes projecting deformation to the SAR line-of-sight for each possible acquisition geometry. The method further includes, for different combinations of three or more acquisition geometries, performing 3D decomposition on simulated noisy data, and computing north / south, east / west, and vertical error. The method further includes ranking the combinations by minimized error across the three dimensions over the AOI. The method further includes generating an output including a report and order information for a set of the ranked combinations. The method further includes displaying the output at a user device.

[0020] In an embodiment the method further includes generating a list of likely open SAR data acquisitions over the AOI for the timeframe, with associated ground footprints, spatial resolution, and geometry information.

[0021] In an embodiment, user-taskable satellites includes at least one satellite in a mid-inclination orbit.

[0022] In an embodiment, geometry information includes at least one of: a viewing geometry; a beam position; an incidence angle; a look direction; a pass direction; and a heading from orbit information.

[0023] In an embodiment, the report includes at least one of: simulation figures for the set of the ranked combinations; and tables of estimated error sensitivities for the set of the ranked combinations.

[0024] In an embodiment, order information includes at least one of: satellite information; incidence angle; metadata associated with acquisition of a data set; a swath ID; a downlink facility; and other information or files to facilitate easy ordering of data for a selected combination.

[0025] In an embodiment, the user input further includes a type of deformation to simulate.

[0026] A method of an archive search for 2D / 3D analysis is provided. The method includes receiving a user input including an area of interest (AOI), and a timeframe. The method further includes searching satellite archives over the AOI for timeframe, returning associated ground footprints, spatial resolution, and geometry information. The method further includes for available acquisition geometries, computing north / south, east / west, and vertical sensitivities based on orthogonality of viewing geometries. The method further includes generating an output including a report and order / download information for archive results. The method further includes displaying the output at a user device.

[0027] In an embodiment, the method includes restricting computing to a set of satellite archives.

[0028] In an embodiment, geometry information includes at least one of: a viewing geometry; a beam position; an incidence angle; a look direction; a pass direction; and a heading from orbit information.

[0029] In an embodiment, the report includes at least one of: simulation figures for the set of the ranked combinations; and tables of estimated error sensitivities for the set of the ranked combinations.

[0030] In an embodiment, order / download information includes at least one of: satellite information, incidence angle, metadata associated with acquisition of a data set, a swath ID, a downlink facility, and other information or files to facilitate easy ordering of data for a selected combination.

[0031] In an embodiment, the user input further includes a deformation to simulate.

[0032] Other aspects and features will become apparent, to those ordinarily skilled in the art, upon review of the following description of some exemplary embodiments.Brief Description of the Drawings

[0033] The drawings included herewith are for illustrating various examples of articles, methods, and apparatuses of the present specification. In the drawings:

[0034] Figure 1 is a schematic diagram of a system for a multi-sensor SAR integrated three-dimensional decomposition acquisition planner, according to an embodiment;

[0035] Figure 2 is a block diagram of components of the system of Figure 1 in further detail, according to an embodiment;

[0036] Figure 3 is a flowchart of a method of 2D / 3D acquisition planning, according to an embodiment;

[0037] Figure 4 is a flowchart of a method of 2D / 3D acquisition planning, according to another embodiment;

[0038] Figure 5 is a flowchart of a method of an archive search for 2D / 3D analysis, according to an embodiment; and

[0039] Figure 6 is a block diagram of a computer system for multi-sensor SAR integrated three-dimensional decomposition acquisition planning, according to an embodiment.Detailed Description

[0040] Various apparatuses or processes will be described below to provide an example of each claimed embodiment. No embodiment described below limits any claimed embodiment and any claimed embodiment may cover processes or apparatuses that differ from those described below. The claimed embodiments are not limited to apparatuses or processes having all of the features of any one apparatus or process described below or to features common to multiple or all of the apparatuses described below.

[0041] One or more systems described herein may be implemented in computer programs executing on programmable computers, each comprising at least one processor, a data storage system (including volatile and non-volatile memory and / orstorage elements), at least one input device, and at least one output device. For example, and without limitation, the programmable computer may be a programmable logic unit, a mainframe computer, server, and personal computer, cloud-based program or system, laptop, personal data assistance, cellular telephone, smartphone, or tablet device.

[0042] Each program is preferably implemented in a high-level procedural or object-oriented programming and / or scripting language to communicate with a computer system. However, the programs can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Each such computer program is preferably stored on a storage media or a device readable by a general or special purpose programmable computer for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein.

[0043] A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the present invention.

[0044] Further, although process steps, method steps, algorithms or the like may be described (in the disclosure and I or in the claims) in a sequential order, such processes, methods and algorithms may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps be performed in that order. The steps of processes described herein may be performed in any order that is practical. Further, some steps may be performed simultaneously.

[0045] When a single device or article is described herein, it will be readily apparent that more than one device I article (whether or not they cooperate) may be used in place of a single device I article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be readily apparent that a single device I article may be used in place of the more than one device or article.

[0046] As used herein, image stack refers to images acquired in a consistent beam mode.

[0047] As used herein, SAR beam mode refers to a specific SAR payload configuration that uses one or more beams and pulses to acquire data with a particular swath width, swath position, noise level, polarization, and a particular range of incidence angles. This term describes the operating mode of a satellite, defined by swath width and resolution, e.g. Standard, Fine, ScanSAR, etc. The term “Beam Mode” is sometimes referred to as “Sensor Mode” and is sometimes shortened to just “Mode”.

[0048] As used herein beam position refers to the image strip on the ground corresponding to the illumination of the RADAR beam. It is characterized by a particular inner and outer incidence angle. There may be several Beam Positions available to choose from in each Beam Mode. The term “Beam Position” is sometimes shortened to just “Beam”.

[0049] The following relates generally to Earth observation data acquisition planning, and more particularly to systems and methods for a multi-sensor SAR integrated three-dimensional decomposition acquisition planner.

[0050] Interferometric Synthetic Aperture Radar (InSAR) is a powerful technology that provides high-resolution, wide area, and high accuracy ground surface deformation measurements. One of its limitations is that it only measures one-dimensional motion along a single radar line-of-sight (LOS). The lack of 3D information can lead to challenging interpretation of results, limited assessment of geo-hazards and ambiguous modelling processes. To retrieve the three motion components of deformation, at least three non- coplanar LOS vectors are required. This can be achieved by utilizing multiple InSAR stacks acquired from different pass directions (ascending and descending orbits), different incidence angles, and left and right looking observations.

[0051] Currently, almost all operational spaceborne SAR sensors acquire image stacks from sun-synchronous orbits (SSO), mostly using right-looking modes. The InSAR measurements derived from these geometrical configurations provide accurate vertical and east-west deformation measurements, however the north-south component remains poorly resolved. When the left-looking geometry is incorporated, some improvement in the accuracy of the north-south component can be achieved, depending on latitude.Unfortunately, few missions have the capability to acquire image stacks in left and right looking modes.

[0052] Some techniques have successfully used multiple geometries from right looking modes to retrieve 3D displacement, but this is limited to regions at high latitudes where angular diversity increases. Other techniques have utilized the pixel offset method and multi-aperture interferometry to overcome this problem; but these methods have their own sensitivity limitations.

[0053] Utilizing at least one non-coplanar LOS vector (SAR images stack) acquired from a mid-inclination orbit enables 3D deformation monitoring at low to mid latitudes since it widens / increases the angular diversity compared to current missions. This may be implemented by a single sensor in the mid-inclination orbit in order to increase the angular diversity relative to using only sun-synchronous orbits missions. However, a multi-sensor (e.g., two sensor) SAR constellation may also be used. Both satellites may provide repeat-pass InSAR capability with various interferometric modes supported. Examples of supported imaging modes may include Stripmap and Spotlight modes. The satellites may operate in a mid-inclination orbit (e.g., 53.5°) that provides more frequent coverage over mid to low latitudes (e.g., ± 62.5° latitude). The mid-inclination orbit may provide a wider diversity of lines of sight and enable better monitoring of 3D deformation.

[0054] Current missions (sun-synchronous orbits (SSO)) do not provide enough angular diversity (i.e. , poor north-south). Three or more viewing geometries are required to estimate 3D deformation using InSAR. At low to mid latitudes, at least one midinclination orbit satellite provides broader angular diversity to better retrieve north-south deformation. At higher latitudes, north-south precision is also highly improved. There may be benefits provided by having the three non-coplanar LOS vectors include at least one SAR stack from an SSO satellite and at least one SAR stack from a mid-inclination orbit (MIO) satellite.

[0055] The present disclosure provides methods and systems for selecting optimal operationally feasible imaging geometries from RADARSAT-2, CHORUS and / or other satellites for 2D or 3D Interferometric Synthetic Aperture Radar (InSAR) deformation monitoring of an area of interest. In an embodiment, the system considers constraintssuch as available viewing geometries, incidence angles, orbits, spatial resolution, and temporal sampling in addition to the location and topography of the area of interest. Viewing geometries are selected to minimize the uncertainty inherent to the imaging geometry in vertical, east-west, and north-south directions. The techniques for data acquisition planning disclosed herein overcome disadvantages of known techniques by selecting appropriate combinations of InSAR image stacks to use.

[0056] Beam mode selection is highly tied to the application desired to be solved (for example, for bridge monitoring a viewing geometry of high resolution is needed, while for tectonic deformation a beam mode of coarser resolution may be used). While technically any three or more non-coplanar viewing geometries over a given area could solve 3D deformation, the associated spatial coverage, as well as error and sensitivity to noise, will vary a lot depending on the selection.

[0057] Inputs include a user-defined area and timeframe of interest, decomposition type (2D or 3D), ancillary data (e.g., digital elevation model (open global, or user-supplied if available)), and satellite configuration information (orbits, viewing geometry and position parameters etc.). The system may include a mechanism for specifying constraints on the imaging modes, if they exist for the region.

[0058] Outputs include a summary of the selected imaging geometries, an assessment of the associated decomposition uncertainty, plus any files needed to place the order.

[0059] InSAR-based deformation monitoring using a single imaging geometry measures the deformation along the sensor line-of-sight. For practical purposes, end users often need to know how this breaks down into vertical, east-west, and north-south deformation.

[0060] In order to do this breakdown, either some assumptions are applied based on knowledge of the area, or additional information is brought in from another source or using other techniques such as speckle tracking. Currently, many providers offer 2D vertical and east-west decomposition information based on a combination of ascending and descending passes, but 3D decomposition has been much more limited becausecommonly available imaging geometries are sun-synchronous orbits, leading to large uncertainty in the estimation of north-south motion.

[0061] In an embodiment, at least three viewing geometries are used.

[0062] In an embodiment, at least one of the viewing geometries is in a midinclination orbit.

[0063] In an embodiment, the viewing geometries may be from only mid-inclination satellites. In an embodiment, the viewing geometries may be from only sun-synchronous (SSO) satellites. In an embodiment, the viewing geometries may be from a combination of SSO satellites and mid-inclination satellites (e.g., one SSO viewing geometry, two midinclination viewing geometries; two SSO viewing geometries, one mid-inclination viewing geometry).

[0064] At or near Earth’s poles, three SSO viewing geometries may be used. In some embodiments, at least one mid-inclination orbit is used for non-polar regions.

[0065] For a SAR constellation in a mid-inclination orbit, this provides an opportunity to resolve north-south motion through the use of multiple (e.g., three) imaging geometries that include at least one image stack acquired from a mid-inclination orbit mission.

[0066] The systems and methods of the present disclosure include the use of satellite configuration information to plan optimal operationally feasible acquisitions for 2D or 3D InSAR monitoring of a site, based on some combination of sun-synchronous orbit and mid-inclination orbit satellites.

[0067] The systems and methods of the present disclosure may be designed and implemented such that they may be used either standalone or within existing acquisition planning tools.

[0068] The present disclosure may have various potential applications. The ability to plan acquisition modes suitable for 3D InSAR deformation decomposition may provide a significant advantage for mining projects and other deformation monitoring projects where a breakdown of deformation into vertical / east-west / north-south components is required, provided the cost of acquiring multiple stacks of data is not prohibitive.

[0069] The planning tool of the present disclosure provides advantages in selecting tasking configurations for SAR satellites in sun-synchronous orbits and is particularly advantageous for selecting these configurations for satellites in mid-inclination orbits, for purposes of 3D deformation monitoring. Advantageously, the user will not need to determine the selection of appropriate acquisitions for 3D decompositions.

[0070] The 3D planning tool is particularly advantageous for mid-inclination orbits (MIOs) versus sun-synchronous orbits (SSOs), as combinations of MIOs, or MIOs plus SSOs provide more opportunities for 3D decomposition (acquisitions from SSOs alone cannot capture the north-south component with small enough error at most latitudes). However, the same general approach could be used for 2D decomposition into vertical / east-west deformation for the SSO-only case (that decomposition ignores northsouth motion), so could still be advantageous there for cases where the north-south motion is not of concern.

[0071] The systems and methods of the present disclosure may enable 3D deformation monitoring with a SAR Constellation.

[0072] Referring now to Figure 1 , shown therein is a system 100 for a multi-sensor SAR integrated three-dimensional decomposition acquisition planner, according to an embodiment.

[0073] The system 100 includes a receiving device 102, a data processing device 112, and a user device 124. The system 100 also includes a plurality of commercial satellites 101 having sensors 103 that may be tasked based on an output generated by the data processing device 112.

[0074] As used herein, the term “commercial satellites” includes satellites for which a user can place an order with configuration information to request an acquisition with desired geometry (i.e. , a user-taskable satellite).

[0075] The user device 124 and the data processing device 112 communicate via a network 150. The network 150 may be a wide area network, such as the Internet. Communication in this context may include sending and receiving data.

[0076] The system 100 includes an acquisition planning software application 105. The acquisition planning software application 105 includes software components executing on data processing device 112 and / or software components executing on user device 124. The acquisition planning software application 105 is configured to determine or select optimal operationally feasible beam modes from SSO and mid-inclination satellites for 2D or 3D InSAR deformation monitoring of an area of interest (“AOI”). The acquisition planning software application 105 receives multi-sensor characteristics data, target requirements data, and optionally ancillary datasets and generates an observation plan. Generating the observation plan may include determining or selecting one or more appropriate combinations of InSAR image stacks to acquire. The application 105 may consider constraints such as available beam modes, orbits, and spatial resolution in addition to location and topography of the area of interest. The application 105 may select beam modes that minimize the uncertainty inherent to the imaging geometry in vertical, east-west, and north-south directions. Inputs to the application 105 may include a user- defined area and timeframe of interest, a decomposition type (2D or 3D), satellite configuration information (e.g., orbits, beam mode and position parameters, etc.). Optionally, the inputs may also include ancillary data (e.g., digital elevation model, open global or user-supplied). The application 105 may may be configured to specify constraints on the imaging modes, if they exist for the region (e.g., if a region is known to always be imaged in a particular mode by a particular satellite). Outputs of the application 105 may include a summary of selected beam modes, an assessment of associated decomposition uncertainty, and files or data needed to place the associated orders for the SSO or mid-inclination satellites.

[0077] In some embodiments, software components on data processing device 112 and user device 124 may operate in a server-client relationship.

[0078] The software application 105 operating at the user device 124 may include a graphical user interface (“GUI”) for receiving input data from a user and displaying an output generated by the data processing device 112. For example, the acquisition planning software application 105 may be configured to receive user inputs to the acquisition planning process via the GUI, such as a time period or timeframe and a geographic location (e.g., an AOI). The acquisition planning software application 105 maydisplay an output of the acquisition planning software application 105 via the GUI, such as a report and order information for a set of the ranked combinations. A report may include, without limitation, simulation figures for the set of the ranked combinations, and tables of estimated error sensitivities for the set of the ranked combinations. Order information may include, without limitation, satellite information, incidence angle, metadata associated with acquisition of a data set, a swath ID, a downlink facility, and other information or files to facilitate easy ordering of data for a selected combination.

[0079] In an embodiment, input data may be posted through a non-graphical web API as part of a script. This may be used, for example, when doing some sort of a bulk search.

[0080] Referring now to Figure 2, shown therein are components of the system 100 of Figure 1 in further detail, according to an embodiment.

[0081] The receiving device 102 receives a user input 104 including an Area of Interest (AOI) 106, and a timeframe 108. The receiving device 102 provides the user input 104 to the data processing device 112.

[0082] The data processing device 112 generates a list of possible synthetic aperture radar (SAR) acquisitions 114 for commercial satellites 101 over the AOI 106 for the timeframe 108. This may include an associated ground footprint, spatial resolution, imaging times, and geometry information.

[0083] In various embodiments, commercial satellites may include satellites such as RADARSAT-2, CHORUS, or the like.

[0084] In various embodiments, commercial satellites may include at least one satellite in a mid-inclination orbit.

[0085] In various embodiments, for 3D deformation, a minimum of three image stacks may be required, with at least one image stack being from mid-inclination orbit for most latitudes.

[0086] Optionally, the data processing device 112 generates a list of likely open SAR data acquisitions over AOI 106 for timeframe 108, with associated ground footprint, spatial resolution, imaging times, and geometry information.

[0087] The data processing device 112 then simulates deformation 116 and expected noise over the AOI 106.

[0088] Optionally, ancillary data (e.g., a digital elevation model (DEM), a noise modeler, and / or General Circulation Model (GCM)Zweather station data) may be provided as input to the data processing device 112 to address one or more sources of noise in InSAR. In an embodiment, the noise modeler is a land cover map.

[0089] In various embodiments, the DEM is used to compute layover / shadow masks and more accurate local incidence angles. These affect the achievable spatial coverage of a set of imaging geometries (in areas that fall in shadow for one or more imaging geometries, the 3D solution will be degraded if four or more viewing geometries are used, or not available if only three are used).

[0090] A land cover mask may be used as a consideration in expected noise for the simulation (e.g., vegetated areas tend to be noisier than landscapes that do not change a lot on the spatial scale of a few to tens of centimeters). Other implementations may not use a land cover mask, however. For example, DEM may be used more frequently.

[0091] In various embodiments, a land cover map may be an input to modelling one of multiple sources of noise that exist in InSAR. However, even without a land cover map it is still possible to simulate noise.

[0092] The data processing device 112 then projects deformation to the SAR line- of-sight 118 for each possible acquisition geometry.

[0093] Optionally, a digital elevation model (DEM) and / or a land cover map or other ancillary information for noise model may be provided to the data processing device 112 for inclusion at this stage as well.

[0094] In various embodiments, a land cover map is only input into the simulated deformation 408, if used at all.

[0095] In various embodiments, the DEM is used in the SAR line-of-sight projection 412 (determining areas of shadow and layover), but may also be useful in some casesfor simulated deformation 408 depending on the application (e.g., if a simulated deformation model includes local slope as a parameter).

[0096] For different combinations of three or more acquisition geometries, the data processing device then performs 3D decomposition 120 on simulated noisy data, and computes the north / south, east / west, and vertical error.

[0097] The data processing device 112 then ranks the combinations 122 by minimized error across the three dimensions over the AOI 106.

[0098] The data processing device 112 then generates an output 126. The output 126 includes a report and order information for a set of the ranked combinations.

[0099] In various embodiments, a selection may be performed by a computer or otherwise to determine the set of the ranked combinations. For example, a computer may be used to determine the set of the ranked combinations, to which the report and order information then pertain.

[0100] In various embodiments, order information includes information required to order a satellite acquisition. In an example case (e.g., for RADARSAT-2), the order information may include an acquisition plan file, product generation parameters (e.g., product type- single look complex, and other configurable processing parameters), polarization, and delivery location.

[0101] Information within an acquisition plan file may vary. For example, an acquisition plan file may include, without limitation, satellite information, incidence angle information, metadata associated with the acquisition of a data set, a swath ID, a downlink facility, and other information to place an order.

[0102] In other embodiments, the contents of the order information may vary (e.g., depending on different missions). However, such order information is likely similar to the foregoing example order information.

[0103] The data processing device 112 sends the output 126 to user device 124.

[0104] The user device 124 is configured to receive output 126 from the data processing device 112. The user device 124 is configured to display a graphical user interface that allows a user to interact with the output 126 generated by the dataprocessing device 112. The user interface may include a series of user interface screens for receiving user input and display output 126 generated by the data processing device 112.

[0105] In various embodiments, the user input 104 further includes a type of deformation to simulate.

[0106] In an embodiment, the deformation to simulate refers to the type of 2D / 3D monitoring to be performed, or the type of deformation to simulate, if any. For example, deformation seen with oil and gas extraction has different temporal and spatial characteristics than deformation seen with mining. The deformation to simulate may only impact the simulated deformation, for implementations that create that. It may not be strictly necessary for simpler implementations which only consider orthogonality of viewing geometries and do not consider the temporal and spatial resolutions needed to capture the expected signal of interest.

[0107] Referring now to Figure 3, shown therein is a method 300 of 2D / 3D acquisition planning, according to an embodiment.

[0108] The method 300 may be encoded as computer-executable instructions and executed by one or more computing devices comprising one or more processors. In an embodiment, the method 300 may be executed by the data processing device 112 and user device 124 of Figure 1 or the computer system 600 of Figure 6.

[0109] Method 300 may be considered a “simpler” version of 3D acquisition planning (e.g., as compared to method 400). The method 300 considers errors / sensitivities based on orthogonality of viewing geometries. Layover, shadow, and changing sensitivities based on local slopes are not considered.

[0110] At 302, the method 300 includes receiving a user input. The user input includes an area of interest (AOI), and a timeframe.

[0111] In various embodiments, the user input further includes a type of deformation to simulate.

[0112] Generally, an AOI is provided as an outline of the area as a polygon. In various embodiments, an AOI may be provided as an ESRI polygon shapefile, a geojsonfile, a text top-left / bottom-right latitude / longitude, a text latitude / longitude center point plus a radius, or another polygon representation.

[0113] In various embodiments, the timeframe may be a year-month-day start date and a year-month-day end date.

[0114] In various embodiments, the deformation to simulate may map to one of a list of allowable simulated deformation types that may be appropriate for different applications.

[0115] In some cases, however, the deformation to simulate may not be needed, especially in simpler cases without deformation simulation.

[0116] As an example, a simulated deformation type may not be used for method 300, while it may be used for method 400 of Figure 4 (discussed below).

[0117] At 304, the method 300 includes generating a list of possible SAR acquisitions for commercial satellites over the AOI for the timeframe. This may include an associated ground footprint, spatial resolution, and geometry information.

[0118] In various embodiments, the list of possible SAR acquisitions is determined based on anticipated orbit information and known satellite constraints. The system described herein may interact with other systems to get these lists, or it may implement some of this functionality internally.

[0119] For instance, for RADARSAT-2, an Acquisition Planning Tool (APT) (or a successor tool) may be used to obtain this list. Other missions may require interaction with other tools.

[0120] One example implementation of the 3D planner may have a tool fit in alongside APT (or in place of it, but using its functions). The 3D planner may then request acquisition coverage plan (.acp) files for possible RADARSAT-2 acquisitions over the area of interest, analyze them in the context of similar information from CHORUS and other missions, and pass forward the .acp and analogous other-mission files for submission to the order desk(s) alongside the report and simulated deformation result for each combination.

[0121] Optionally, at 306, the method 300 includes generating a list of likely open SAR data acquisitions over AOI for timeframe, with associated ground footprint, spatial resolution, and geometry information.

[0122] As an example, some space agencies control the observation plan of satellites and so those satellites are not commercial. However, any data acquired may be downloaded and used for some of these satellites, such as Sentinel-1 (as data is open). A space agency may publish its observation plan, and in general may keep the imaging geometry consistent over a location. Therefore, by searching for what has been acquired in past over an area, it is possible to make an educated guess at what will be available in future, despite a degree of uncertainty remaining. For this reason, it is optional whether or not to consider it in the analysis, because it may be useful if it is acquired, yet it cannot be completely relied upon.

[0123] Again, as used herein, the term “commercial satellites” includes satellites for which a user can place an order with configuration information to request an acquisition with desired geometry (i.e. , a user-taskable satellite).

[0124] In various embodiments, the way the list of likely open SAR data acquisitions is generated will depend on the mission. For example, in the case of Sentinel- 1 , web APIs such as those provided by ESA or the Alaska SAR facility may be used to conduct a recent historical search over the area, and assume that any consistent ongoing acquisitions will be continued in the future. There may be no associated order - just a link or script to help check for, and download the data, if / when it arrives.

[0125] At 308, the method 300 includes, for different combinations of acquisition geometries, computing north / south, east / west, and vertical sensitivities based on orthogonality of viewing geometries.

[0126] In various embodiments, the sensitivities may be obtained using, for example, an error propagation law. An error propagation law may be applied to understand how sensitivities / errors reflect in metric units, which are easier to understand to end users.

[0127] At 310, the method 300 includes ranking combinations. Combinations are ranked based on minimizing error across the three dimensions.

[0128] In various embodiments, the minimum norm for 3D error components is evaluated, after filtering past a suitable threshold so that none of the error components are too high.

[0129] Each sensitivity may be converted for each component to a metric unit (e.g., millimeters, centimeters, millimeters / year, etc.).

[0130] Then, the solution with minimum error is searched for. Either the overall sensitivity for all components (e.g. use the viewing geometries that on average give the smallest error or highest sensitivity) could be optimized, or the solution that minimizes the maximum error among all individual components could be used.

[0131] Other variations could exist based on certain constraints (for example, if a customer only wants to use one mid-inclination orbit and two sun-synchronous orbits, or a customer wants to use only sun-synchronous orbits, etc.). Other constraints could be related to technical aspects (e.g., a situation where one cannot use the best possible geometry for sun-synchronous orbits because a left looking image stack is needed and is not feasible with the best possible geometry).

[0132] As multiple ways may exist for determining the solution with the minimum error, other ways to find the optimal geometries may be reasonably contemplated.

[0133] At 312, the method 300 includes generating an output. The output includes a report and order information for best combinations.

[0134] In various embodiments, a selection may be performed by a computer or otherwise to determine the best combinations (e.g., a set of the ranked combinations). For example, a computer may be used to determine the best combinations, to which the report and order information then pertain.

[0135] In various embodiments, order information includes information required to order a satellite acquisition. In an example case (e.g., for RADARSAT-2), this information may be an acquisition plan file, plus product generation parameters (e.g., product type-single look complex, and other configurable processing parameters), polarization, delivery location.

[0136] In various embodiments, details may differ depending on missions, but may likely require similar sorts of information.

[0137] In various embodiments, reports may show the expected measurement error in north-south, east-west, and vertical directions for a number of different combinations of satellite acquisitions.

[0138] At 314, the method 300 includes displaying the output from 312 at a user device.

[0139] In various embodiments, a user may submit the order information for the selected combination of acquisitions to the relevant order desks.

[0140] Once data is available, a user may download and analyze it to suit their needs.

[0141] Referring now to Figure 4, shown therein is a method 400 of 2D / 3D acquisition planning, according to another embodiment.

[0142] The method 400 may be encoded as computer-executable instructions and executed by one or more computing devices comprising one or more processors. In an embodiment, the method 400 may be executed by the data processing device 112 and user device 124 of Figure 1 or the computer system 600 of Figure 6.

[0143] Method 400 represents may be considered a more in-depth version of 3D decomposition (e.g., compared to method 300 of Figure 3). In the method 400, the optimization also considers layover, shadow, etc.

[0144] At 402, the method 400 includes receiving a user input. The user input includes an area of interest (AOI), and a timeframe.

[0145] In various embodiments, an AOI may be provided as an ESRI polygon shapefile, a geojson file, a text top-left / bottom-right latitude / longitude, or a text latitude / longitude center point plus a radius.

[0146] Essentially, an AOI may be provided as any convenient way of providing an outline of the area as a polygon.

[0147] In various embodiments, the timeframe may be a year-month-day start date and a year-month-day end date.

[0148] In various embodiments, a deformation to simulate may map to one of a list of allowable simulated deformation types that may be appropriate for different applications.

[0149] In some cases however, the deformation to simulate may not be needed, especially in simpler cases without deformation simulation.

[0150] As an example, a simulated deformation type may also be used for method 400.

[0151] At 404, the method 400 includes generating a list of possible SAR acquisitions for commercial satellites over AOI for timeframe. This may include an associated ground footprint, spatial resolution, and geometry information.

[0152] In various embodiments, commercial satellites may include satellites such as RADARSAT-2, CHORUS, or the like.

[0153] In various embodiments, commercial satellites may include at least one satellite in a mid-inclination orbit.

[0154] In an embodiment, a commercial satellite provides a means of ordering a specific configuration, and is thus taskable by users.

[0155] In various embodiments, for 3D deformation, a minimum of three image stacks may be required, with at least one image stack being from mid-inclination orbit.

[0156] Optionally, at 406, the method 400 includes generating a list of likely open SAR data acquisitions over AOI for timeframe. This may include an associated ground footprint, spatial resolution, and geometry information.

[0157] As an example, some space agencies control the observation plan of satellites and so those satellites are not commercial. However, any data acquired may be downloaded and used for some of these satellites, such as Sentinel-1 (as data is open).A space agency may publish its observation plan, and in general may keep the imaging geometry consistent over a location. Therefore, by searching for what has been acquired in past over an area, it is possible to make an educated guess at what will be available in future, despite a degree of uncertainty remaining. For this reason, it is optional whether or not to consider it in the analysis, because it may be useful if it is acquired, yet it cannot be completely relied upon.

[0158] In various embodiments, the way the list of likely open SAR data acquisitions is generated will depend on the mission. For example, in the case of Sentinel- 1 , web APIs such as those provided by ESA or the Alaska SAR facility may be used to conduct a recent historical search over the area, and assume that any consistent ongoing acquisitions will be continued in the future. There may be no associated order - just a link or script to help check for, and download the data, if / when it arrives.

[0159] In various embodiments, geometry information may include a viewing geometry (impacts spatial resolution, noise levels), a beam position (e.g., an incidence angle), a look direction (e.g., left / right), a pass direction (e.g., ascending / descending), or heading from orbit information. As an orbital track repeats after some number of days, the simulation only needs to be for one cycle and then duplicated to cover the timeframe.

[0160] At 408, the method 400 includes simulating deformation and expected noise over the AO I.

[0161] Optionally, at 410, the method 400 includes providing / applying ancillary data (e.g., a land cover map for the noise model) as input to address one or more sources of noise.

[0162] In various embodiments, a land cover map may be useful for defining the noise model. With SAR, vegetated areas tend to have a lot more decorrelation between acquisitions than some other land cover types. However, it may not be used in many implementations.

[0163] In various embodiments, it is also possible to simulate other sources of noise that do not require the land cover map. Those other sources of noise may also be simulated within the method 400, but do not require any external data.

[0164] In some embodiments, additional parameters or ancillary data may also need to be incorporated.

[0165] At 412, the method 400 includes projecting deformation to the SAR line-of- sight for each possible acquisition geometry. Optionally, the land cover map for the noise model of step 410 may be provided at step 412.

[0166] In various embodiments, projecting to SAR line-of-sight simulates what may be measured by each individual satellite. Then, the reverse operation of reconstructing the 3D deformation from the 3+ individual satellite measurements can be performed to compute expected error for a given noise level of simulated data. Projecting to SAR line of sight (LOS) is essentially a dot product of the deformation vector onto the SAR line of sight vector.

[0167] In various embodiments, ancillary data (e.g., a digital elevation model (DEM), a noise modeler, or General Circulation Model (GCM)Zweather station data) is provided as input to address one or more sources of noise in InSAR. Noise may be modelled in different ways (e.g., a land cover map), and the noise modeler is not necessary to identify all source of noise. Moreover, multiple sources of noise exist in the InSAR solution described herein. The noise modeler may optionally assist us to infer signal loss over some areas. However, in the absence of a noise modeler, it is still possible to simulate other sources of noise.

[0168] At 414, the method 400 includes, for different combinations of three or more acquisition geometries, performing 3D decomposition on the simulated noisy data, and computing north / south, east / west, and vertical error.

[0169] In various embodiments, the sensitivities may be obtained using, for example, an error propagation law.

[0170] At 416, the method 400 includes ranking combinations. Combinations may be ranked based on minimizing error across the three dimensions over the AOI.

[0171] In various embodiments, a “combination” is a set of planned satellite acquisition geometries for computing 3D decomposition from.

[0172] There may be different ways of ranking (e.g., different weightings of average error, and percent coverage in the case of one or more acquisitions having some parts of the AOI in shadow, spatial resolution).

[0173] An example ranking may be: 1 . Combination 1 (“best”) - satellite geometries A, B, and C, giving average north-south error X, average vertical error Y, average eastwest error Z, covering N% of the AOI. An electronic / computer folder may be populated with deformation results and ordering info for A, B, C.

[0174] 2. Combination 2 (“second best”) - satellite geometries D, E, and F, etc.

[0175] At 418, the method 400 includes generating an output. The output includes a report and order information for best combinations.

[0176] In various embodiments, a selection may be performed by a computer or otherwise to determine the best combinations (e.g., a set of the ranked combinations). For example, a computer may be used to determine the best combinations, to which the report and order information then pertain.

[0177] In various embodiments, order information includes information required to order a satellite acquisition. In an example case (e.g., for RADARSAT-2), this information may be an acquisition plan file, plus product generation parameters (e.g., product typesingle look complex, and other configurable processing parameters), polarization, delivery location.

[0178] In various embodiments, details may differ depending on missions, but may likely require similar sorts of information.

[0179] In various embodiments, reports may show the expected measurement error in north-south, east-west, and vertical directions for a number of different combinations of satellite acquisitions.

[0180] At 420, the method 400 includes displaying the output from 418 at a user device.

[0181] In various embodiments, the report may include simulation figures and tables of estimated error sensitivities for the best combinations.

[0182] Order information may include satellite information, incidence angle, metadata associated with acquisition of a data set, a swath ID, a downlink facility, and other information or files or information to facilitate easy ordering of the data for the selected combination. This allows for the user to not need to do any intense work to configure and put in the order, as they may just forward the information from the selected combination.

[0183] In an embodiment, forwarded information may be, for example, the .acp files required to order a particular set of RADARSAT-2 acquisitions (or the CHORUS equivalent).

[0184] Otherwise, the user could go in to the acquisition planning tool (APT) to construct the required order info (acquisition coverage plan, .acp file) themselves based on the selected output imaging geometries.

[0185] Referring now to Figure 5, shown therein is a method 500 of an archive search for 2D / 3D analysis, according to an embodiment.

[0186] The method 500 may be encoded as computer-executable instructions and executed by one or more computing devices comprising one or more processors. In an embodiment, the method 500 may be executed by the data processing device 112 and user device 124 of Figure 1 or the computer system 600 of Figure 6.

[0187] The method 500 provides an archive search assessment of suitability of available past acquisitions for 2D / 3D analysis over an area of interest (AOI).

[0188] This information could be useful for a historical analysis after an unanticipated event (e.g., an earthquake, dam collapse, etc.), if there happened to be enough data acquired incidentally over the site.

[0189] This may be similar to methods 300 and 400 above, but on a reduced subset of image acquisition options (i.e. , just on whatever was acquired).

[0190] At 502, the method 500 includes receiving a user input. The user input includes an area of interest (AOI), and a timeframe.

[0191] At 504, the method 500 includes searching satellite archives over the AOI for the timeframe. The search returns associated ground footprints, spatial resolution, and geometry information.

[0192] In various embodiments, the satellite archives are online catalogs / APIs for finding past data, where location is mission-dependent.

[0193] Optionally, at 506, the method 500 includes restricting which results to include in analysis. If not restricted, all results will be considered since archive results are much more limited than planning results.

[0194] In various embodiments, restrictions may need to be applied if someone else has already ordered data that conflicts with some of the otherwise-possible acquisitions for a satellite over a given timeframe. Restrictions may be to cut out the no- longer-possible acquisition options from the input to the analysis.

[0195] At 508, the method 500 includes, for available acquisition geometries, computing north / south, east / west, and vertical sensitivities based on orthogonality of viewing geometries.

[0196] In various embodiments, the sensitivities may be obtained using, for example, an error propagation law.

[0197] Optionally, at 510, the method 500 includes simulating deformation, projecting SAR to line of sight, performing 2D or 3D decomposition, and estimating residual error for a more detailed analysis.

[0198] Optionally, at 512, the method 500 includes providing / applying ancillary data (e.g., a land cover map for the noise model) as input to address one or more sources of noise.

[0199] At 514, the method 500 includes generating an output. The output includes a report and order / download information for archive results.

[0200] In various embodiments, order information includes information required to order a past satellite acquisition. In an example case (e.g., for RADARSAT-2), this information may be an acquisition plan file, plus product generation parameters (e.g., product type- single look complex, and other configurable processing parameters),polarization, delivery location. Those having skill in the art will appreciate that in the archive search case, the order information will be for a past acquisition, rather than a planned future one.

[0201] In various embodiments, details may differ depending on missions, but may likely require similar sorts of information.

[0202] In various embodiments, reports may show the expected measurement error in north-south, east-west, and vertical directions for a number of different combinations of satellite acquisitions.

[0203] At 516, the method 500 includes displaying the output from 514 at a user device.

[0204] Referring now to Figure 6, shown therein is a computer system 600 for multi-sensor SAR integrated three-dimensional decomposition acquisition planning, according to an embodiment.

[0205] The system 600 may be configured to implement any one or more of the methods described herein, or portions thereof.

[0206] Components of the computer system 600 may be implemented at one or more devices, such as data processing device 112 and user device 124 of Figure 1 .

[0207] The system 600 includes a memory 602 and a processor 604 in communication with the memory 602.

[0208] The processor 604 is configured to execute various software modules and components. In some embodiments, modules or components executed by the processor 604 may include server-side software components and client-side software components that communicate with each other in order to provide various features and functionalities of the system 600. In some cases, server-side components may be executed at a server computer and client-side components may be executed at a user device.

[0209] The system 600 includes a communication interface device 606 for transmitting and receiving data to and from other computing devices. The communication interface device 606 may include a network interface device for transmitting and receiving data via a network connection (e.g., local area network, wide area network, etc.).

[0210] The communication interface device 606 is configured to receive a user input 601 including an area of interest (AOI) 603, and a timeframe 605.

[0211] In some embodiments, the user input 601 further includes a type of deformation to simulate.

[0212] The system 600 includes a display device 608 for displaying data generated by the system 600. The display device 608 may be located at a user device, such as user device 124 of Figure 1 .

[0213] The system 600 includes an input device 610 for receiving input data from a user interacting with the system 600. For example, a user may use input device 610 to interact with the system 600 through a graphical user interface generated by the processor 604 and displayed via the display device 608. The input device 610 may be located at the user device, such as user device 124 of Figure 1. A user may input information requests about three-dimensional decomposition acquisition planning through input device 610 (e.g., provide area of interest (AOI), timeframe, or deformation to simulate information).

[0214] The processor 604 includes a SAR acquisitions list generation module 612 configured to generate a list of possible synthetic aperture radar (SAR) acquisitions 614 for commercial satellites over the AOI for the timeframe, with an associated ground footprint, spatial resolution, and geometry information.

[0215] The processor 604 further includes a simulation deformation module 616 configured to simulate deformation (e.g., simulated deformation 618) and expected noise over the AOI.

[0216] The processor 604 further includes a projection module 620 configured to project to SAR a line-of-sight 622 for each possible acquisition geometry.

[0217] The processor 604 further includes a 3D decomposition module 624 configured to, for different combinations of acquisition geometries, perform 3D decomposition (e.g., 3D decomposition 626) on simulated noisy data, and computing north / south, east / west, and vertical error.

[0218] The processor 604 further includes a ranking module 628 configured to rank the combinations (e.g. ranked combinations 630) by minimized error across the three dimensions over the AOI.

[0219] The processor 604 further includes an output generation module 632 configured to generate an output 634. The output 634 including a report and order information for a set of the ranked combinations.

[0220] Specific systems may utilize all of the components shown or only a subset of the components, and levels of integration may vary from system to system. Furthermore, a system may contain additional components which may be reasonably contemplated by those of skill in the art, such as additional input devices, or additional modules within the processor 604 configured to perform specific functions. Similarly, a system may contain multiple instances of a component, such as multiple processors 604, memories 602, communication interfaces 606, etc.

[0221] Referring now to Figure 7, shown therein is a method 700 of 3D deformation monitoring, according to an embodiment.

[0222] The method 700 may be encoded as computer-executable instructions and executed by one or more computing devices comprising one or more processors. In an embodiment, the method 700 may be executed by the data processing device 112 and user device 124 of Figure 1 or the computer system 600 of Figure 6.

[0223] At 702, the method 700 includes acquiring at least three Interferometric Synthetic Aperture Radar (InSAR) image stacks, wherein each InSAR image stack is acquired from a respective satellite having a SAR payload configured for InSAR, and wherein at least one (e.g., a first) InSAR image stack is acquired from a first satellite in a mid-inclination orbit.

[0224] At 704, the method further includes downlinking the acquired image stacks as input to a 3D deformation monitoring software application.

[0225] In an embodiment, at least one (e.g., a second) InSAR image stack is acquired from a satellite in sun-synchronous orbit.

[0226] In various embodiments, various combinations of InSAR image stacks may be contemplated. For example, at least one image stack may originate from a satellite in a mid-inclination orbit, while the remainder of the image stacks may originate from one or more satellites in sun-synchronous orbits.

[0227] In an embodiment, all of the InSAR image stacks may originate from one or more satellites in mid-inclination orbits.

[0228] The acquired image stacks may thus all be acquired from at least one satellite in mid-inclination orbit.

[0229] While the above description provides examples of one or more apparatus, methods, or systems, it will be appreciated that other apparatus, methods, or systems may be within the scope of the claims as interpreted by one of skill in the art. Elements of each embodiment may be incorporated into other embodiments, for example, configurations discussed in relation to one embodiment, may be applied to other embodiments disclosed herein. Further, it is evident that various modifications and combinations can be made without departing from the invention. The specification and drawings are, accordingly, to be regarded simply as an illustration of the invention as defined by the claims, and are contemplated to cover any and all modifications, variations, combinations or equivalents that fall within the scope of the present disclosure.

Claims

Claims:1 . A system for 3D acquisition planning, the system comprising: a receiving device configured to receive a user input including an area of interest (AOI) and a timeframe; a data processing device configured to: generate a list of possible synthetic aperture radar (SAR) acquisitions for user-taskable satellites over the AOI for the timeframe, with associated ground footprints, spatial resolution, and geometry information; simulate deformation and expected noise over the AOI; project deformation to a SAR line-of-sight for each possible acquisition geometry; for different combinations of three or more acquisition geometries, perform 3D decomposition on simulated noisy data, and compute north / south, east / west, and vertical error; rank the combinations by minimized error across the three dimensions over the AOI; generate an output including a report and order information for a set of the ranked combinations; and a user device configured to display the output.

2. The system of claim 1 , wherein the data processing device is further configured to generate a list of likely open SAR data acquisitions over the AOI for the timeframe, with an associated ground footprint, spatial resolution, and geometry information.

3. The system of claim 1 , wherein user-taskable satellites includes at least one satellite in a mid-inclination orbit.

4. The system of claim 1 , wherein the geometry information includes at least one of: a viewing geometry; a beam position; an incidence angle; a look direction; a pass direction; and a heading from orbit information.

5. The system of claim 1 , wherein the report includes at least one of: simulation figures for the set of the ranked combinations; and tables of estimated error sensitivities for the set of the ranked combinations.

6. The system of claim 1 , wherein order information includes at least one of: satellite information; incidence angle; metadata associated with acquisition of a data set; a swath ID; a downlink facility; and other information or files to facilitate easy ordering of data for a selected combination.

7. The system of claim 1 , wherein the user input further includes a type of deformation to simulate.

8. A method of 3D acquisition planning, the method comprising: receiving a user input including an area of interest (AOI), and a timeframe; generating a list of possible synthetic aperture radar (SAR) acquisitions for user- taskable satellites over the AOI for the timeframe, with associated ground footprints, spatial resolution, and geometry information;for different combinations of acquisition geometries, computing north / south, east / west, and vertical sensitivities based on orthogonality of viewing geometries; ranking combinations by minimized error across three dimensions; generating an output including a report and order information for a set of the ranked combinations; displaying the output at a user device.

9. The method of claim 8, further comprising generating a list of likely open SAR data acquisitions over the AOI for the timeframe, with associated ground footprints, spatial resolution, and geometry information.

10. The method of claim 8, wherein user-taskable satellites includes at least one satellite in a mid-inclination orbit.11 . The method of claim 8, wherein the geometry information includes at least one of: a viewing geometry; a beam position; an incidence angle; a look direction; a pass direction; and a heading from orbit information.

12. The method of claim 8, wherein the report includes at least one of: simulation figures for the set of the ranked combinations; and tables of estimated error sensitivities for the set of the ranked combinations.

13. The method of claim 8, wherein the order information includes at least one of: satellite information; incidence angle; metadata associated with acquisition of a data set; a swath ID; a downlink facility; and other information or files to facilitate easy ordering of data for a selected combination.

14. A method of 3D acquisition planning, the method comprising:receiving a user input including an area of interest (AOI), and a timeframe; generating a list of possible synthetic aperture radar (SAR) acquisitions for user- taskable satellites over the AOI for the timeframe, with associated ground footprints, spatial resolution, and geometry information; simulating deformation and expected noise over the AOI; projecting deformation to the SAR line-of-sight for each possible acquisition geometry; for different combinations of three or more acquisition geometries, performing 3D decomposition on simulated noisy data, and computing north / south, east / west, and vertical error; ranking the combinations by minimized error across the three dimensions over the AOI; generating an output including a report and order information for a set of the ranked combinations; displaying the output at a user device.

15. The method of claim 14, further comprising generating a list of likely open SAR data acquisitions over the AOI for the timeframe, with associated ground footprints, spatial resolution, and geometry information.

16. The method of claim 14, wherein user-taskable satellites includes at least one satellite in a mid-inclination orbit.

17. The method of claim 14, wherein geometry information includes at least one of: a viewing geometry; a beam position; an incidence angle; a look direction; a pass direction; and a heading from orbit information.

18. The method of claim 14, wherein the report includes at least one of: simulation figures for the set of the ranked combinations; and tables of estimated error sensitivities for the set of the ranked combinations.

19. The method of claim 14, wherein order information includes at least one of: satellite information; incidence angle; metadata associated with acquisition of a data set; a swath ID; a downlink facility; and other information or files to facilitate easy ordering of data for a selected combination.

20. The method of claim 14, wherein the user input further includes a type of deformation to simulate.21 . A method of an archive search for 2D / 3D analysis, the method comprising: receiving a user input including an area of interest (AOI), and a timeframe; searching satellite archives over the AOI for timeframe, returning associated ground footprints, spatial resolution, and geometry information; for available acquisition geometries, computing north / south, east / west, and vertical sensitivities based on orthogonality of viewing geometries; generating an output including a report and order / download information for archive results; displaying the output at a user device.

22. The method of claim 21 , restricting computing to a set of satellite archives.

23. The method of claim 21 , wherein geometry information includes at least one of: a viewing geometry; a beam position; an incidence angle; a look direction; a pass direction; and a heading from orbit information.

24. The method of claim 21 , wherein the report includes at least one of: simulation figures for the set of the ranked combinations; and tables of estimated error sensitivities for the set of the ranked combinations.

25. The method of claim 21 , wherein order / download information includes at least one of: satellite information, incidence angle, metadata associated with acquisition of a data set, a swath ID, a downlink facility, and other information or files to facilitate easy ordering of data for a selected combination.

26. The method of claim 21 , wherein the user input further includes a deformation to simulate.

Images