CN116740306B - Remote sensing satellite earth observation task planning method and device based on road network guidance - Google Patents
Remote sensing satellite earth observation task planning method and device based on road network guidance Download PDFInfo
- Publication number
- CN116740306B CN116740306B CN202310993296.0A CN202310993296A CN116740306B CN 116740306 B CN116740306 B CN 116740306B CN 202310993296 A CN202310993296 A CN 202310993296A CN 116740306 B CN116740306 B CN 116740306B
- Authority
- CN
- China
- Prior art keywords
- grid
- shooting
- road network
- remote sensing
- sequence
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000012163 sequencing technique Methods 0.000 claims abstract description 9
- 238000003384 imaging method Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 125000003275 alpha amino acid group Chemical group 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/05—Geographic models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
- G06T17/205—Re-meshing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- Computer Graphics (AREA)
- Economics (AREA)
- Remote Sensing (AREA)
- Entrepreneurship & Innovation (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Radar, Positioning & Navigation (AREA)
- Game Theory and Decision Science (AREA)
- Multimedia (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a road network guiding-based remote sensing satellite earth observation task planning method and device, wherein the method comprises the following steps: performing grid division on known road network information and a target observation area; determining a second shooting priority of a grid to be shot in the target observation area; determining a plurality of candidate grids from the grids to be shot based on the satellite point track after 24 hours in the future, and sequencing the candidate grids based on the second shooting priority of the candidate grids and the flight direction of the remote sensing satellite to obtain a first grid sequence; and forming a second grid sequence based on the observation constraint condition of the remote sensing satellite, and generating a remote sensing satellite earth observation task planning scheme. The method can accurately and effectively acquire the road network target area, reduce the complexity of manual operation of task planning, and greatly improve the effectiveness and timeliness of task planning.
Description
Technical Field
The invention relates to the field of space high-resolution optical remote sensing satellite mission planning, in particular to a remote sensing satellite earth observation mission planning method and device based on road network guidance.
Background
The traditional remote sensing satellite earth observation task is usually initiated by the ground, and the ground operation and control department uploads the task meeting the satellite-ground interface convention to the satellite in the environment or by using a relay arc through links such as task demand collection, task planning, visibility calculation, instruction generation and the like. With the increase of the number of in-orbit satellites year by year, the increasing of the load types of remote sensing satellites, the gradual expansion of satellite application modes and the gradual improvement of task timeliness requirements, the conventional satellite-ground large loop task control mode can not meet the requirements, and the autonomous requirements on satellites are further improved.
Along with the improvement of the spatial resolution of the remote sensing satellite image, the application range of the remote sensing image is wider and wider, and the sub-meter-level high-resolution remote sensing image can be used for extracting the large-range road network information in the traffic industry. However, at present, a shooting plan mode for road network information is still established manually, so that urban road network information coverage and updating requirements are high, an optical remote sensing image is generally influenced by cloud shielding and the like, and partial region shooting in the image is shielded or unclear, so that interruption occurs in road network information extraction.
With the increase of the number of in-orbit satellites year by year, the increasing of the load types of remote sensing satellites, the gradual expansion of satellite application modes and the gradual improvement of task timeliness requirements, the task planning capability of the satellites is improved to a certain extent, but the task planning capability mainly aims at the task planning of a typical area or a typical target image of the satellites, and compared with the target area, the road network has larger extensibility and continuity and has higher requirements on the task planning of the satellites.
Disclosure of Invention
In view of the above, the invention provides a road network guiding-based remote sensing satellite earth observation task planning method and device, which can solve the technical problem of rapid road network acquisition requirements of users, and automatically complete imaging task planning of the remote sensing satellite on road network information needing updating and supplementing by comprehensively considering the observation tasks and key observation areas of the users by utilizing known road network information.
The present invention is so implemented as to solve the above-mentioned technical problems.
A road network guidance-based remote sensing satellite earth observation task planning method comprises the following steps:
step S1: acquiring known road network information; dividing grids of known road network information and target observation areas, and setting road network shooting identifiers and road network shooting attributes of each grid, wherein the target observation areas are areas to be shot by a user by using remote sensing satellites, and the road network shooting attributes are used for indicating first shooting priorities and shooting conditions of the grids;
step S2: matching each grid in the target observation area with the grids of the known road network information, and determining a second shooting priority of the grid to be shot in the target observation area;
step S3: parameters of a remote sensing satellite are obtained, and a satellite orbit prediction model for predicting the satellite's satellite point orbit after 24 hours in the future is established;
step S4: determining a plurality of candidate grids from the grids to be shot based on the future satellite point track after 24 hours, and sequencing the candidate grids based on the second shooting priority of the candidate grids and the flight direction of the remote sensing satellite to obtain a first grid sequence;
step S5: and deleting the candidate grids in the first grid sequence based on the target model based on the observation constraint condition of the remote sensing satellite to form a second grid sequence, and generating a remote sensing satellite earth observation task planning scheme based on the second grid sequence.
Preferably, the step S1 includes: acquiring known road network information; performing grid division on the known road network information and a target observation area; numbering each grid in the target observation area, and setting a road network shooting identifier and a road network shooting attribute of each grid, wherein the target observation area is an area to be shot by a user by using satellites, the road network shooting identifier is used for indicating whether the grid has shooting data of remote sensing satellites in known road network information, and the road network shooting attribute is used for indicating a first shooting priority and shooting conditions of the grid.
Preferably, each grid in the target observation area is matched with the grid of the known road network information, and the second shooting priority of the grid to be shot in the target observation area is determined, wherein the first shooting priority of the grid to be shot in the shooting time limit designated by the user is determined according to the user requirement and is set to be high; under the condition that the user does not have requirements on shooting time limit, if the grid to be shot in the target observation area corresponds to the grid of the known road network information, and the road network shooting identification of the corresponding grid of the known road network information indicates that the grid does not have shooting data of remote sensing satellites in the known road network information, the second shooting priority of the grid to be shot is a medium priority; if the grid to be shot in the target observation area corresponds to the grid of the known road network information, and the road network shooting identification of the corresponding grid of the known road network information indicates that the grid has shooting data of remote sensing satellites in the known road network information, the second shooting priority of the grid to be shot is low; wherein a shooting time limit is determined by the first shooting priority.
Preferably, the step S4 includes:
step S41: based on the satellite point track after 24 hours in the future, acquiring a satellite observable time window of the grid to be shot, and marking road network shooting asAnd->As candidate grids:
the road network shooting identification is expressed as follows:
wherein,representing existing road network information without photographing an update anymore,/->Representing existing road network information but requiring re-shooting of updates,/->Representing road network-free information;
step S42: sequencing each candidate grid according to the second shooting priority of the candidate grids and the flight direction of the remote sensing satellite to obtain a first grid sequenceT 1 。
Preferably, the step S5 includes:
step S51: obtaining the observation constraint conditions of the remote sensing satellite, wherein the constraint conditions are as follows:
whereint mn In order to take the time required for shooting two adjacent candidate grids in the first grid sequence when the remote sensing satellite runs along the track of the point under the satellite,tthe time required for the remote sensing satellite to roll to image,mandnare all numbers of candidate grids in the first grid sequence,/the number of candidate grids in the first grid sequence is equal to the number of candidate grids in the first grid sequence>，NThe method comprises the steps of obtaining the maximum number of grids which can be observed by a remote sensing satellite in the first grid sequence at one time;
step S52: constructing a target model, deleting candidate grids in the first grid sequence based on the constraint condition to form an initial sequence of a second grid sequenceT 2 ；
Step S53: ordering the initial sequence of the second grid sequence according to the imaging time of the remote sensing satellite corresponding to each grid in the initial sequence of the second grid sequence from low to high to obtain the second grid sequence;
step S54: generating a remote sensing satellite earth observation task planning scheme based on the second grid sequence, wherein the remote sensing satellite earth observation task planning scheme is used for generating a task sequence comprising a plurality of tasks according to imaging time of each grid in the second grid sequence, each task corresponds to one grid, and each task comprises a network number, shooting start time, shooting end time and maneuvering parameters of the remote sensing satellite; wherein the shooting start time ist 0 The shooting end time is，LFor the length of the grid,v 0 is satellite flying speed.
Preferably, candidate grids in the first grid sequence are pruned to form an initial sequence of a second grid sequenceT 2 Comprising:
taking the grid set which meets the constraint condition and has the largest candidate grid in the first grid sequence as an initial sequence of a second grid sequence:
T 2 obtained from the following formula:
i,j,…,nnumbering candidate grids contained in the initial sequence of the second grid sequence, each number uniquely corresponding to one grid, whereint i,j For shooting the first grid sequence with the numberiTo number ofjIs a candidate grid of the grid.
Preferably, candidate grids in the first grid sequence are pruned to form an initial sequence of a second grid sequenceT 2 Comprising:
taking a grid set which meets the constraint condition and corresponds to the candidate grid and has the largest second shooting priority value in the first grid sequence as an initial sequence of the second grid sequence, wherein the high priority value is set to 3, the medium priority value is set to 2, and the low priority value is set to 1;
T 2 obtained from the following formula:
wherein the method comprises the steps ofP i Is numbered asiIs associated with a second shot priority of the grid of (c),t i,j for shooting the first grid sequence with the numberiTo number ofjIs a candidate grid of the grid.
Preferably, in the step S3, the parameters of the remote sensing satellite include a position, a velocity, and an orbit number of the remote sensing satellite.
The invention provides a road network guiding-based remote sensing satellite earth observation task planning device, which comprises:
an initialization module: configured to obtain known road network information; dividing grids of known road network information and target observation areas, and setting road network shooting identifiers and road network shooting attributes of each grid, wherein the target observation areas are areas to be shot by a user by using remote sensing satellites, and the road network shooting attributes are used for indicating first shooting priorities and shooting conditions of the grids;
a priority determining module: the method comprises the steps of configuring to match each grid in a target observation area with grids of known road network information, and determining a second shooting priority of grids to be shot in the target observation area;
the track determination module: the method comprises the steps of configuring parameters of a remote sensing satellite, and establishing a satellite orbit prediction model for predicting the satellite's satellite point orbit after 24 hours in the future;
a first grid sequence module: the method comprises the steps of determining a plurality of candidate grids from grids to be shot based on the satellite point tracks after 24 hours in the future, and sequencing the candidate grids based on the second shooting priority of the candidate grids and the flight direction of a remote sensing satellite to obtain a first grid sequence;
and a task module: and the remote sensing satellite earth observation task planning scheme is configured to prune candidate grids in the first grid sequence based on the observation constraint condition of the remote sensing satellite and a target model to form a second grid sequence, and generate the remote sensing satellite earth observation task planning scheme based on the second grid sequence.
The beneficial effects are that:
(1) Compared with the existing task planning method, the method can accurately and effectively acquire the road network target area, reduce the complexity of manual operation of task planning, and greatly improve the effectiveness and timeliness of task planning;
(2) The method is simple and easy to implement;
(3) The invention can dynamically adjust the task planning mode according to the parameters of the remote sensing satellite;
(4) The method effectively improves the satellite task planning efficiency and satellite observation capacity;
(5) The invention supports editing and is flexible to use.
Drawings
FIG. 1 is a schematic flow chart of a road network guidance-based remote sensing satellite earth observation task planning method;
FIG. 2 is a schematic diagram of an embodiment of a road network guidance-based remote sensing satellite earth observation mission planning method provided by the invention;
fig. 3 is a schematic structural diagram of a road network guidance-based remote sensing satellite earth observation task planning device.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples.
As shown in fig. 1, the invention provides a road network guiding-based remote sensing satellite earth observation task planning method, which comprises the following steps:
step S1: acquiring known road network information; dividing grids of known road network information and target observation areas, and setting road network shooting identifiers and road network shooting attributes of each grid, wherein the target observation areas are areas to be shot by a user by using remote sensing satellites, and the road network shooting attributes are used for indicating first shooting priorities and shooting conditions of the grids;
step S2: matching each grid in the target observation area with the grids of the known road network information, and determining a second shooting priority of the grid to be shot in the target observation area;
step S3: parameters of a remote sensing satellite are obtained, and a satellite orbit prediction model for predicting the satellite's satellite point orbit after 24 hours in the future is established;
step S4: determining a plurality of candidate grids from the grids to be shot based on the future satellite point track after 24 hours, and sequencing the candidate grids based on the second shooting priority of the candidate grids and the flight direction of the remote sensing satellite to obtain a first grid sequence;
step S5: and deleting the candidate grids in the first grid sequence based on the target model based on the observation constraint condition of the remote sensing satellite to form a second grid sequence, and generating a remote sensing satellite earth observation task planning scheme based on the second grid sequence.
Further, the grid to be shot comprises a grid which is not shot and a grid which needs to be shot again.
The step S1 includes: acquiring known road network information; performing grid division on the known road network information and a target observation area; numbering each grid in the target observation area, and setting a road network shooting identifier and a road network shooting attribute of each grid, wherein the target observation area is an area to be shot by a user by using satellites, the road network shooting identifier is used for indicating whether the grid has shooting data of remote sensing satellites in known road network information, and the road network shooting attribute is used for indicating a first shooting priority and shooting conditions of the grid.
The step S2: matching each grid in the target observation area with grids of known road network information, and determining a second shooting priority of the grid to be shot in the target observation area, wherein the second shooting priority of the grid to be shot in the user-specified shooting time limit is firstly determined according to user requirements and is set to be high priority; under the condition that the user does not have requirements on shooting time limit, if the grid to be shot in the target observation area corresponds to the grid of the known road network information, and the road network shooting identification of the corresponding grid of the known road network information indicates that the grid does not have shooting data of remote sensing satellites in the known road network information, the second shooting priority of the grid to be shot is a medium priority; if the grid to be shot in the target observation area corresponds to the grid of the known road network information, and the road network shooting identification of the corresponding grid of the known road network information indicates that the grid has shooting data of remote sensing satellites in the known road network information, the second shooting priority of the grid to be shot is low; wherein a shooting time limit is determined by the first shooting priority.
Further, in the step S3, the parameters of the remote sensing satellite include a position, a velocity, and an orbit number of the remote sensing satellite.
The remote sensing satellite orbit prediction model is a remote sensing satellite orbit prediction model which is universal in the field.
The step S4 includes:
step S41: based on the satellite point track after 24 hours in the future, acquiring a satellite observable time window of the grid to be shot, and marking road network shooting asAnd->As candidate grids:
the road network shooting identification is expressed as follows:
wherein,representing existing road network information without photographing an update anymore,/->Representing existing road network information but requiring re-shooting of updates,/->Indicating no road network information.
Step S42: sequencing each candidate grid according to the second shooting priority of the candidate grids and the flight direction of the remote sensing satellite to obtain a first grid sequenceT 1 。
Further, the candidate grids are ranked based on the second shooting priority of the candidate grids and the flying direction of the remote sensing satellite, so as to obtain a first grid sequence, wherein the grids to be shot in the target observation area are ranked from high to low based on the second shooting priority, and the grids to be shot with the same priority are ranked based on the flying direction of the remote sensing satellite.
In this embodiment, the remote sensing satellite flight direction characterizes the satellite shooting sequence. The high priority is not delayed when shooting.
The step S5 includes:
step S51: obtaining the observation constraint conditions of the remote sensing satellite, wherein the constraint conditions are as follows:
whereint mn In order to take the time required for shooting two adjacent candidate grids in the first grid sequence when the remote sensing satellite runs along the track of the point under the satellite,tthe time required for the remote sensing satellite to roll to image,mandnare all numbers of candidate grids in the first grid sequence,/the number of candidate grids in the first grid sequence is equal to the number of candidate grids in the first grid sequence>，NThe method comprises the steps of obtaining the maximum number of grids which can be observed by a remote sensing satellite in the first grid sequence at one time;
step S52: constructing a target model, deleting candidate grids in the first grid sequence based on the constraint condition to form an initial sequence of a second grid sequenceT 2 ；
Optionally, taking the grid set which satisfies the constraint condition and has the largest candidate grid in the first grid sequence as an initial sequence of a second grid sequence:
T 2 obtained from the following formula:
i,j,…,nnumbering candidate grids contained in the initial sequence of the second grid sequence, each number uniquely corresponding to one grid, whereint i,j For shooting the first grid sequence with the numberiTo number ofjIs a candidate grid of the grid.
Optionally, taking a grid set which meets the constraint condition and corresponds to the candidate grid and has the largest second shooting priority value in the first grid sequence as an initial sequence of the second grid sequence, wherein the high priority value is set to 3, the medium priority value is set to 2, and the low priority value is set to 1;
T 2 obtained from the following formula:
wherein the method comprises the steps ofP i Is numbered asiIs associated with a second shot priority of the grid of (c),t i,j for shooting the first grid sequence with the numberiTo number ofjIs a candidate grid of the grid.
Step S53: ordering the initial sequence of the second grid sequence according to the imaging time of the remote sensing satellite corresponding to each grid in the initial sequence of the second grid sequence from low to high to obtain the second grid sequence;
step S54: generating a remote sensing satellite earth observation task planning scheme based on the second grid sequence, wherein the remote sensing satellite earth observation task planning scheme is used for generating a task sequence comprising a plurality of tasks according to imaging time of each grid in the second grid sequence, each task corresponds to one grid, and each task comprises a network number, shooting start time, shooting end time and maneuvering parameters of the remote sensing satellite; wherein the shooting start time ist 0 The shooting end time is，LFor the length of the grid,v 0 is satellite flying speed.
Referring to fig. 2, the road network guiding-based remote sensing satellite earth observation task planning method of the invention is described with reference to a specific embodiment.
S1, acquiring known road network information, wherein a specific storage format is a road network layer based on geographic information, and the layer comprises geographic plate attributes and administrative attribution information.
The road network layer is meshed in a mode of equal longitude and latitude, the range of the meshing is from the equator to the two poles, from the longitude to the east, from the west to the east, the weft difference is 10-angle seconds, and the warp difference is 10-angle seconds.
The grid number adopts a direct sequence numbering mode and uses 30-bit binary number identification.
S2, setting a road network shooting identifier and a road network shooting attribute for the grids with the numbers.
Specifically, at the time t, the grid with the number n is expressed as a road network shooting identifier:
（1）
in the formula (1), the amino acid sequence of the formula (1),representing existing road network information without photographing an update anymore,/->Representing existing road network information but requiring re-shooting of updates,/->Indicating no road network information.
At time t, the grid with n number shows the following observation attribute:
（2）
in the formula (2), L is a level that needs to be re-photographed (the level is 0 when no road information exists in the area, the level is 1 when road information exists in the area, the level is 2 when road information exists in the area), the value is a positive integer, and a higher level indicates a stronger requirement for photographing the area, that is, a higher photographing priority, and the level definition of L can be enhanced or reduced as required.
The judgment of the level of the area L is based on the earth surface type or the known road network information, and the scene without road information or road network information extraction such as water area, desert and the like can be identified as level 0; the adjacent boundary of the adjacent area of the area is provided with road network information, and the road information has continuity, and the level of the area can be judged to be 2; the area is not a single scene such as a water area or a desert, the adjacent boundary of the adjacent area has no road network information, whether the area has road network information cannot be determined, and the level of the area can be judged to be 1; for areas that have not been photographed (no remote sensing image) and have no known road network information, the level may be defined as 0 or 1 or 2 according to the user's needs.
The Requirement is a shooting condition, and the shooting condition is that the solar altitude is more than 20 degrees and the cloud coverage proportion is less than 50%.
All grids support modification in the road network shooting identification and the road network shooting attribute.
Acquiring all the current timeThe value is +.>And->As a set of grids to be photographed.
S3, carrying out orbit prediction and calculation by using the current satellite position, speed and orbit number, and obtaining the satellite point track of 24 hours in the future.
S4, calculating the visible time window of each grid in the grid set according to the future 24-hour star point track and the grid set with the shooting level L not being 0.
S5, determining task planning constraint conditions according to the remote sensing satellite use constraint and the constraint. These basic constraints support modifications. And according to the constraint conditions, combining shooting attributes and shooting benefits of the grids to make a decision so as to obtain the grids to be shot at the next moment.
Assuming that the shooting gain of the ith grid is Ui, the calculation method of Ui is as follows:
if the grid is in the area where the user is uploading the observation task and the priority of the observation task is high, adding 3 to Ui;
if the grid is in the area where the user is uploading the observation task and the priority of the observation task is low, adding 2 to Ui;
if this grid is not in the area where the user is uploading the observation task, but is in the range where the satellite is observable through the maneuver, then Ui is incremented by 1.
And ordering the observation benefits Ui of all unobserved grids, and selecting the grids from large to small according to the sequence of Ui values as the grids to be observed at the next moment under the condition that the basic constraint condition of task planning is met.
And S6, generating an action sequence and starting execution time of the action sequence for the task of the grid to be observed.
And S7, updating the observation identification of the land area grid after the observation task is executed. Specifically, according to the grid covered by the current observation, the observation mark of the corresponding grid is obtained "X n Assigned asX 1 Or (b)。
The invention also provides a remote sensing satellite earth observation task planning device based on road network guidance, as shown in fig. 3, the device comprises:
an initialization module: configured to obtain known road network information; dividing grids of known road network information and target observation areas, and setting road network shooting identifiers and road network shooting attributes of each grid, wherein the target observation areas are areas to be shot by a user by using remote sensing satellites, and the road network shooting attributes are used for indicating first shooting priorities and shooting conditions of the grids;
a priority determining module: the method comprises the steps of configuring to match each grid in a target observation area with grids of known road network information, and determining a second shooting priority of grids to be shot in the target observation area;
the track determination module: the method comprises the steps of configuring parameters of a remote sensing satellite, and establishing a satellite orbit prediction model for predicting the satellite's satellite point orbit after 24 hours in the future;
a first grid sequence module: the method comprises the steps of determining a plurality of candidate grids from grids to be shot based on the satellite point tracks after 24 hours in the future, and sequencing the candidate grids based on the second shooting priority of the candidate grids and the flight direction of a remote sensing satellite to obtain a first grid sequence;
and a task module: and the remote sensing satellite earth observation task planning scheme is configured to prune candidate grids in the first grid sequence based on the observation constraint condition of the remote sensing satellite and a target model to form a second grid sequence, and generate the remote sensing satellite earth observation task planning scheme based on the second grid sequence.
The above specific embodiments merely describe the design principle of the present invention, and the shapes of the components in the description may be different, and the names are not limited. Therefore, the technical scheme described in the foregoing embodiments can be modified or replaced equivalently by those skilled in the art; such modifications and substitutions do not depart from the spirit and technical scope of the invention, and all of them should be considered to fall within the scope of the invention.
Claims (9)
1. The remote sensing satellite earth observation task planning method based on road network guidance is characterized by comprising the following steps of:
step S1: acquiring known road network information; dividing grids of known road network information and target observation areas, and setting road network shooting identifiers and road network shooting attributes of each grid, wherein the target observation areas are areas to be shot by a user by using remote sensing satellites, and the road network shooting attributes are used for indicating first shooting priorities and shooting conditions of the grids;
step S2: matching each grid in the target observation area with the grids of the known road network information, and determining a second shooting priority of the grid to be shot in the target observation area;
step S3: acquiring parameters of a remote sensing satellite, and establishing a remote sensing satellite orbit prediction model for predicting a satellite point orbit of the remote sensing satellite 24 hours later in the future;
step S4: determining a plurality of candidate grids from the grids to be shot based on the future satellite point track after 24 hours, and sequencing the candidate grids based on the second shooting priority of the candidate grids and the flight direction of the remote sensing satellite to obtain a first grid sequence;
step S5: and deleting the candidate grids in the first grid sequence based on the target model based on the observation constraint condition of the remote sensing satellite to form a second grid sequence, and generating a remote sensing satellite earth observation task planning scheme based on the second grid sequence.
2. The method according to claim 1, wherein the step S1 includes: acquiring known road network information; performing grid division on the known road network information and a target observation area; numbering each grid in the target observation area, and setting a road network shooting identifier and a road network shooting attribute of each grid, wherein the target observation area is an area to be shot by a user by using satellites, the road network shooting identifier is used for indicating whether the grid has shooting data of remote sensing satellites in known road network information, and the road network shooting attribute is used for indicating a first shooting priority and shooting conditions of the grid.
3. The method of claim 2, wherein each grid in the target observation area is matched with a grid of known road network information, and a second shooting priority of the grid to be shot in the target observation area is determined, wherein the second shooting priority of the grid to be shot for which the user designates shooting time limit is firstly determined according to user requirements and is set to be high priority; under the condition that the user does not have requirements on shooting time limit, if the grid to be shot in the target observation area corresponds to the grid of the known road network information, and the road network shooting identification of the corresponding grid of the known road network information indicates that the grid does not have shooting data of remote sensing satellites in the known road network information, the second shooting priority of the grid to be shot is a medium priority; if the grid to be shot in the target observation area corresponds to the grid of the known road network information, and the road network shooting identification of the corresponding grid of the known road network information indicates that the grid has shooting data of remote sensing satellites in the known road network information, the second shooting priority of the grid to be shot is low; wherein a shooting time limit is determined by the first shooting priority.
4. A method according to claim 3, wherein said step S4 comprises:
step S41: based on the satellite point track after 24 hours in the future, acquiring a satellite observable time window of the grid to be shot, and marking road network shooting asAnd->As candidate grids:
the road network shooting identification is expressed as follows:
wherein,representing existing road network information without photographing an update anymore,/->Representing existing road network information but requiring re-shooting of updates,/->Representing road network-free information;
step S42: ordering each candidate grid based on the second shooting priority of the candidate grid and the flight direction of the remote sensing satellite to obtain a first grid sequenceT 1 。
5. The method of claim 4, wherein the step S5 includes:
step S51: obtaining the observation constraint conditions of the remote sensing satellite, wherein the constraint conditions are as follows:
whereint mn In order to take the time required for shooting two adjacent candidate grids in the first grid sequence when the remote sensing satellite runs along the track of the point under the satellite,tthe time required for the remote sensing satellite to roll to image,mandnare all numbers of candidate grids in the first grid sequence,/the number of candidate grids in the first grid sequence is equal to the number of candidate grids in the first grid sequence>，NThe method comprises the steps of obtaining the maximum number of grids which can be observed by a remote sensing satellite in the first grid sequence at one time;
step S52: constructing a target model, deleting candidate grids in the first grid sequence based on the constraint condition to form an initial sequence of a second grid sequenceT 2 ；
Step S53: ordering the initial sequence of the second grid sequence according to the imaging time of the remote sensing satellite corresponding to each grid in the initial sequence of the second grid sequence from low to high to obtain the second grid sequence;
step S54: generating a remote sensing satellite earth observation task planning scheme based on the second grid sequence, wherein the remote sensing satellite earth observation task planning scheme is used for generating a task sequence comprising a plurality of tasks according to imaging time of each grid in the second grid sequence, each task corresponds to one grid, and each task comprises a network number, shooting start time, shooting end time and maneuvering parameters of the remote sensing satellite; wherein the shooting start time ist 0 The shooting end time is，LFor the length of the grid,v 0 is satellite flying speed.
6. The method of claim 5, wherein candidate grids in the first grid sequence are pruned to form an initial sequence of a second grid sequenceT 2 Comprising:
taking the grid set which meets the constraint condition and has the largest candidate grid in the first grid sequence as an initial sequence of a second grid sequence:
T 2 obtained from the following formula:
i,j,…,nnumbering candidate grids contained in the initial sequence of the second grid sequence, each number uniquely corresponding to one grid, whereint i,j For shooting the first grid sequence with the numberiTo number ofjIs a candidate grid of the grid.
7. The method of claim 5, wherein candidate grids in the first grid sequence are pruned to form an initial sequence of a second grid sequenceT 2 Comprising:
taking a grid set which meets the constraint condition and corresponds to the candidate grid and has the largest second shooting priority value in the first grid sequence as an initial sequence of the second grid sequence, wherein the high priority value is set to 3, the medium priority value is set to 2, and the low priority value is set to 1;
T 2 obtained from the following formula:
wherein the method comprises the steps ofP i Is numbered asiIs associated with a second shot priority of the grid of (c),t i,j for shooting the first grid sequence with the numberiTo number ofjIs a candidate grid of the grid.
8. The method according to any one of claims 1-7, wherein the parameters of the remote sensing satellite include the position, velocity and orbit number of the remote sensing satellite in step S3.
9. A road network guidance-based remote sensing satellite earth observation task planning device, the device comprising:
an initialization module: configured to obtain known road network information; dividing grids of known road network information and target observation areas, and setting road network shooting identifiers and road network shooting attributes of each grid, wherein the target observation areas are areas to be shot by a user by using remote sensing satellites, and the road network shooting attributes are used for indicating first shooting priorities and shooting conditions of the grids;
a priority determining module: the method comprises the steps of configuring to match each grid in a target observation area with grids of known road network information, and determining a second shooting priority of grids to be shot in the target observation area;
the track determination module: the method comprises the steps of configuring parameters of a remote sensing satellite, and establishing a remote sensing satellite orbit prediction model for predicting a satellite point orbit of the remote sensing satellite after 24 hours in the future;
a first grid sequence module: the method comprises the steps of determining a plurality of candidate grids from grids to be shot based on the satellite point tracks after 24 hours in the future, and sequencing the candidate grids based on the second shooting priority of the candidate grids and the flight direction of a remote sensing satellite to obtain a first grid sequence;
and a task module: and the remote sensing satellite earth observation task planning scheme is configured to prune candidate grids in the first grid sequence based on the observation constraint condition of the remote sensing satellite and a target model to form a second grid sequence, and generate the remote sensing satellite earth observation task planning scheme based on the second grid sequence.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310993296.0A CN116740306B (en) | 2023-08-09 | 2023-08-09 | Remote sensing satellite earth observation task planning method and device based on road network guidance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310993296.0A CN116740306B (en) | 2023-08-09 | 2023-08-09 | Remote sensing satellite earth observation task planning method and device based on road network guidance |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116740306A CN116740306A (en) | 2023-09-12 |
| CN116740306B true CN116740306B (en) | 2023-11-07 |
Family
ID=87915382
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310993296.0A Active CN116740306B (en) | 2023-08-09 | 2023-08-09 | Remote sensing satellite earth observation task planning method and device based on road network guidance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116740306B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114330856A (en) * | 2021-12-23 | 2022-04-12 | 中科天智运控(深圳)科技有限公司 | Long-time-period large-area remote sensing satellite rapid covering method and system |
| CN114596503A (en) * | 2022-03-03 | 2022-06-07 | 大连理工大学 | Road extraction method based on remote sensing satellite image |
| CN115759598A (en) * | 2022-11-07 | 2023-03-07 | 二十一世纪空间技术应用股份有限公司 | Remote sensing satellite task planning method based on multi-source cloud amount |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10482655B2 (en) * | 2017-11-20 | 2019-11-19 | Here Global B.V. | Method and apparatus for providing a tile-based digital elevation model |
-
2023
- 2023-08-09 CN CN202310993296.0A patent/CN116740306B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114330856A (en) * | 2021-12-23 | 2022-04-12 | 中科天智运控(深圳)科技有限公司 | Long-time-period large-area remote sensing satellite rapid covering method and system |
| CN114596503A (en) * | 2022-03-03 | 2022-06-07 | 大连理工大学 | Road extraction method based on remote sensing satellite image |
| CN115759598A (en) * | 2022-11-07 | 2023-03-07 | 二十一世纪空间技术应用股份有限公司 | Remote sensing satellite task planning method based on multi-source cloud amount |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116740306A (en) | 2023-09-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108109437B (en) | Unmanned aerial vehicle autonomous route extraction and generation method based on map features | |
| US9201426B1 (en) | Reverse iteration of planning data for system control | |
| KR101793509B1 (en) | Remote observation method and system by calculating automatic route of unmanned aerial vehicle for monitoring crops | |
| JP2018146546A (en) | Information processing system, information processing device, and information processing method | |
| CN112393714B (en) | Image correction method based on unmanned aerial vehicle aerial photography and satellite remote sensing fusion | |
| CN113361499B (en) | Local object extraction method and device based on two-dimensional texture and three-dimensional attitude fusion | |
| KR101737438B1 (en) | Method and Apparatus for Constructing Reference Layer, Method and System for Constructing 3D GIS Data using Reference Layer | |
| CN115657706A (en) | Landform measuring method and system based on unmanned aerial vehicle | |
| CN104331859A (en) | Inclined strip splicing imaging method of narrow and long object area of remote sensing satellite | |
| CN116740306B (en) | Remote sensing satellite earth observation task planning method and device based on road network guidance | |
| KR102567800B1 (en) | Drone used 3d mapping method | |
| Starek et al. | Application of unmanned aircraft systems for coastal mapping and resiliency | |
| KR102488553B1 (en) | Drone used 3d mapping method | |
| WO2022138182A1 (en) | Artificial satellite and ground system | |
| CN115824215A (en) | Three-dimensional flight path planning method for unmanned aerial vehicle for water channel and river course routing inspection | |
| KR102567799B1 (en) | Drone used 3d mapping method | |
| Chaudhry et al. | A comparative study of modern UAV platform for topographic mapping | |
| KR20200031853A (en) | A method for analyzing the thermal picture information based on gps | |
| CN112414375B (en) | Unmanned aerial vehicle image posture recovery method for flood disaster emergency quick jigsaw making | |
| CN112904894A (en) | Slope live-action image acquisition method based on unmanned aerial vehicle oblique photography | |
| Greenwood | Inside the world’s largest drone archaeology program | |
| Gao et al. | High-precision and light-small oblique photogrammetry UAV landscape restoration monitoring | |
| CN112197762B (en) | Outdoor maneuvering target position estimation method based on o' clock direction | |
| KR102557775B1 (en) | Drone used 3d mapping method | |
| Mi et al. | Declassified Historical Satellite Imagery from 1960s and Geometric Positioning Evaluation in Shanghai, China |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |