A method for computing regional coverage based on discrete point access information clustering

Abstract

The application discloses a kind of area covering calculation method based on discrete point access information clustering, belong to satellite remote sensing field.This method first discretizes the region to be observed into several equidistant discrete points, and calculates the access information (access time and access angle) of each satellite to each discrete point, access information is clustered according to constraint condition, and a series of access information list is obtained;According to the list with the most access information elements, the task planning is carried out, the region to be observed is divided into planned area and un-planned area, and each un-planned area is taken as a new region to be observed, and the method is iterated to continue processing until the region to be observed is completely covered or there is no available access information.The application can realize the maximum coverage calculation of the region to be observed in the shortest time, avoid the problem complexity caused by the satellite observation width and the different orbit directions, and has good adaptability to different region types and different satellite types, and has good expansibility.

Description

Technical Field

[0001] This invention relates to the field of satellite remote sensing, specifically to a method for calculating regional coverage based on discrete point access information clustering. This method is applicable to the problem of maximizing the coverage of a specified area by multiple remote sensing satellites within a certain time period in the field of satellite remote sensing mission planning. Background Technology

[0002] Regional coverage is an important application in the field of satellite remote sensing, such as estimating the area of ​​water bodies and forests. Since satellite remote sensing can only image along its orbital direction, the ground imaging area generated by a single satellite activation is a strip, with the strip's direction aligned with the satellite's orbital direction. The width of this strip is closely related to the field of view of the satellite's remote sensing payload; the higher the satellite resolution, the narrower the imaging strip. When the area to be observed is large and cannot be covered by a single satellite image, multiple satellites are often used in conjunction to quickly achieve coverage of the area, or to achieve maximum coverage of the area in the shortest possible time.

[0003] The conventional algorithm for regional coverage mission planning uses a strip coverage method, which divides the region into several strips along the satellite's orbital direction. Each time a satellite passes by, it observes one strip, and by stitching together the strips observed by multiple satellites, the coverage of the region is formed. However, due to the urgency of the mission, the satellites participating in the mission may be of different types, with different orbital directions and imaging swaths, making it difficult to use a single standard to divide the region into strips. In addition, remote sensing satellites often have strong lateral maneuvering capabilities. By lateral maneuvering, satellites can observe a large area to the left and right of the nadir point, and can observe many strips. However, a satellite can only use one lateral maneuver angle for each imaging operation, meaning that a satellite can only observe one strip when passing by the region. The strip selection by multiple satellites further increases the complexity of this type of algorithm, resulting in low efficiency and poor adaptability. Summary of the Invention

[0004] In view of this, this invention proposes a regional coverage calculation method based on discrete point access information clustering. This invention can achieve maximum coverage calculation of the area to be observed in the shortest time, while avoiding the complexity caused by different satellite imaging swath widths and orbital directions. It has good adaptability to different region types and different satellite types, and good scalability.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for calculating region coverage based on discrete point access information clustering includes the following steps:

[0007] Step 1: Create a planning scheme list. Each element in the planning scheme list includes the satellite code wxdh, the imaging side view βplan, the imaging start time t1plan, and the imaging end time t2plan.

[0008] Step 2: Calculate the nadir imaging swath of all satellites participating in the mission to obtain the minimum imaging swath, and calculate the discrete point interval based on the minimum imaging swath.

[0009] Step 3: Discretize the area to be observed into multiple equally spaced discrete points according to the discrete point interval, and add all discrete points to the discrete point list after numbering them.

[0010] Step 4: Cluster the access information of discrete points: Calculate the access information of each satellite to each discrete point, and then cluster the access information to form multiple access information lists;

[0011] Step 5: Perform task planning on the list with the most access information elements. According to the planning scheme, divide the area to be observed into planned areas and unplanned areas. Take all unplanned areas as new areas to be observed and continue to execute Step 3. Repeat this process until the area to be observed is completely covered or there is no available access information. This completes the area coverage based on discrete point access information clustering.

[0012] Furthermore, the specific method for step 2 is as follows:

[0013] (201) Calculate the nadir imaging swath W for each satellite:

[0014] W≈H×α

[0015] Where H is the satellite's orbital altitude, α is the satellite's payload field of view angle; the minimum imaging swath is the minimum value minW among all satellites' nadir imaging swaths;

[0016] (202) Calculate the discrete point interval space:

[0017] space = minW / 3.

[0018] Furthermore, the specific method for step 3 is as follows:

[0019] (301) Traverse the coordinates of each vertex of the polygon in the region to be observed, and obtain the minimum x-coordinate minX, minimum y-coordinate minY, maximum x-coordinate maxX and maximum y-coordinate maxY in the coordinates of each vertex. Using (minX, minY) and (maxX, maxY) as the coordinates of the two vertices of a diagonal, generate the bounding rectangle of the region to be observed. Insert m columns × n rows of discrete points in the bounding rectangle, where m = [(maxX-minX) / space]+1, n = [(maxY-minY) / space]+1, and the coordinates of the discrete point P(i,j) in the i-th column and j-th row are (xi, yj), xi = minX + (i-1)*space, yj = minY + (j-1)*space, 1≤i≤m, 1≤j≤n, and i and j are integers;

[0020] (302) Establish a discrete point list PointList, calculate whether each discrete point is inside the polygon of the region to be observed, and add all discrete points inside the polygon of the region to be observed to the discrete point list PointList after numbering them.

[0021] Furthermore, the specific method for step 4 is as follows:

[0022] (401) Define the access information data structure Ai, which contains wxdh, mbbh, t and θ;

[0023] Where wxdh is the satellite code, mbbh is the target discrete point number, t is the time when the satellite visits the target discrete point, and θ is the side view when the satellite visits the target discrete point.

[0024] (402) Establish a total access information list AiList, calculate the access information of each satellite to each discrete point in the discrete point list PointList, that is, the time of satellite access to the target discrete point and the side view during the access; save all access information to the total access information list AiList;

[0025] (403) Generate multiple sets of first-level access information lists based on the total access information column AiList, including AiList1, AiList2, AiList3, etc. Each set of first-level access information lists stores the access information of all discrete points within the entire side view range of the same satellite in the same orbital circle.

[0026] (404) Sort all side views in the first-level access information list of each group from smallest to largest:

[0027] β1≤β2≤β3≤……≤βk;

[0028] Save all access information in the first-level access information list where the difference between the side viewpoint and βi is less than the field of view angle α to a second-level access information list, i = 1, 2, 3, ..., and so on, until a second-level access information list includes access information with a side viewpoint of βk.

[0029] Traverse each group of first-level access information lists to finally form a second-level access information list clustered by side view: AiList11, AiList12, ..., AiList21, AiList22, ...

[0030] Furthermore, the specific method for step 5 is as follows:

[0031] (501) Select the list AiListMost with the most access information elements from AiList11, AiList12, ..., AiList21, AiList22, ..., and obtain the minimum side view minβ, the maximum side view maxβ, the earliest access time t1 and the latest access time t2 from this list. Generate a planning scheme based on these four parameters. The relevant parameters of the planning scheme are: imaging side view βplan=(minβ+maxβ) / 2, imaging start time t1plan=t1-Δt, imaging end time t2plan=t2+Δt, where Δt is the time difference between two adjacent access information in the current secondary access information list;

[0032] (502) Add the imaging side view βplan, imaging start time t1plan, imaging end time t2plan, and satellite code wxdh to which the list AiListMost belongs to the planning scheme to the planning scheme list PlanList;

[0033] (503) The planning scheme decomposes the area to be observed into a planned area and an unplanned area. There are four possible spatial relationships between the planned area and the unplanned area: the planned area is on the left and the unplanned area is on the right; the planned area is in the middle and the unplanned areas are on both sides; the planned area is on the right and the unplanned area is on the left; the planned area completely covers the area to be observed and there is no area to be planned.

[0034] (504) Treat each unplanned area as a new observation area and continue to execute step 3. When executing step (402), the access information that conflicts with the planning scheme list PlanList needs to be removed from the total access information list AiList.

[0035] (505) Repeat the above process until the area to be observed is completely covered by the planned area, i.e., the fourth case in step (503), or there is no more available access information, i.e. after step (504) is completed, the total access information list AiList is empty, and the final planning scheme list PlanList is obtained, i.e. the area coverage based on discrete point access information clustering is completed.

[0036] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art:

[0037] 1. The present invention uses a regional discretization method to avoid the complexity of strip division caused by different types of satellites having different orbital directions and different observation swaths, and the logic is clear and visible.

[0038] 2. This invention calculates and generates a planned area based on the access information list, and recalculates the remaining unplanned areas iteratively, without having to consider the splicing problem of different strips, thus further reducing the complexity of the problem.

[0039] 3. This invention features a simple software architecture, ease of implementation and debugging, ease of expansion, and strong compatibility. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the remote sensing satellite orbit in an embodiment of the present invention.

[0041] Figure 2 This is a schematic diagram of remote sensing satellite parameters in an embodiment of the present invention.

[0042] Figure 3 This is a schematic diagram of the process in an embodiment of the present invention.

[0043] Figure 4 yes Figure 3 A schematic diagram of the discretization of the region to be observed.

[0044] Figure 5 yes Figure 3 A schematic diagram of information access based on side-view clustering.

[0045] Figure 6 yes Figure 3 A schematic diagram of the observable region is planned based on the clustering results of access information.

[0046] Figure 7 yes Figure 3 The planning results divide the area to be observed into planned and unplanned areas. Detailed Implementation

[0047] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0048] A method for calculating region coverage based on discrete point access information clustering, such as Figure 3 As shown, it includes the following steps:

[0049] Step 1: Create a planning scheme list. Each element in the planning scheme list includes the satellite code wxdh, the imaging side view βplan, the imaging start time t1plan, and the imaging end time t2plan.

[0050] Step 2: Calculate the nadir imaging swath of all satellites participating in the mission to obtain the minimum imaging swath, and calculate the discrete point interval based on the minimum imaging swath.

[0051] Step 3, as follows Figure 4 As shown, the area to be observed is discretized into multiple equally spaced discrete points according to the discrete point interval, and all discrete points are numbered and added to the discrete point list.

[0052] Step 4: Cluster the access information of discrete points: Calculate the access information of each satellite to each discrete point, and then cluster the access information to form multiple access information lists;

[0053] Step 5, as follows Figure 6 , Figure 7 As shown, task planning is performed on the list with the most access information elements. According to the planning scheme, the area to be observed is divided into planned areas and unplanned areas. All unplanned areas are taken as new areas to be observed, and step 3 is continued. This process is repeated until the area to be observed is completely covered or there is no available access information. This completes the area coverage based on discrete point access information clustering.

[0054] Furthermore, the specific method for step 2 is as follows:

[0055] (201) Calculate the nadir imaging swath W for each satellite:

[0056] W≈H×α

[0057] Where H is the satellite's orbital altitude, α is the satellite's payload field of view angle; the minimum imaging swath is the minimum value minW among all satellites' nadir imaging swaths;

[0058] Specifically, such as Figure 1 , Figure 2 The image shows a schematic diagram of relevant parameters for remote sensing satellites.

[0059] (202) Calculate the discrete point interval space:

[0060] space = minW / 3.

[0061] Furthermore, the specific method for step 3 is as follows:

[0062] (301) Traverse the coordinates of each vertex of the polygon in the region to be observed, and obtain the minimum x-coordinate minX, minimum y-coordinate minY, maximum x-coordinate maxX and maximum y-coordinate maxY in the coordinates of each vertex. Using (minX, minY) and (maxX, maxY) as the coordinates of the two vertices of a diagonal, generate the bounding rectangle of the region to be observed. Insert m columns × n rows of discrete points in the bounding rectangle, where m = [(maxX-minX) / space]+1, n = [(maxY-minY) / space]+1, and the coordinates of the discrete point P(i,j) in the i-th column and j-th row are (xi, yj), xi = minX + (i-1)*space, yj = minY + (j-1)*space, 1≤i≤m, 1≤j≤n, and i and j are integers;

[0063] (302) Establish a discrete point list PointList, calculate whether each discrete point is inside the polygon of the region to be observed, and add all discrete points inside the polygon of the region to be observed to the discrete point list PointList after numbering them.

[0064] Furthermore, the specific method for step 4 is as follows:

[0065] (401) Define the access information data structure Ai, which contains wxdh, mbbh, t and θ;

[0066] Where wxdh is the satellite code, mbbh is the target discrete point number, t is the time when the satellite visits the target discrete point, and θ is the side view when the satellite visits the target discrete point.

[0067] (402) Establish a total access information list AiList, calculate the access information of each satellite to each discrete point in the discrete point list PointList, that is, the time of satellite access to the target discrete point and the side view during the access; save all access information to the total access information list AiList;

[0068] (403) Generate multiple sets of first-level access information lists based on the total access information column AiList, including AiList1, AiList2, AiList3, etc. Each set of first-level access information lists stores the access information of all discrete points within the entire side view range of the same satellite in the same orbital circle.

[0069] (404) such as Figure 5 As shown, all side views in each group's first-level access information list are sorted from smallest to largest:

[0070] β1≤β2≤β3≤……≤βk;

[0071] Save all access information in the first-level access information list where the difference between the side viewpoint and βi is less than the field of view angle α to a second-level access information list, i = 1, 2, 3, ..., and so on, until a second-level access information list includes access information with a side viewpoint of βk.

[0072] Traverse each group of first-level access information lists to finally form a second-level access information list clustered by side view: AiList11, AiList12, ..., AiList21, AiList22, ...

[0073] Furthermore, the specific method for step 5 is as follows:

[0074] (501) Select the list AiListMost with the most access information elements from AiList11, AiList12, ..., AiList21, AiList22, ..., and obtain the minimum side view minβ, the maximum side view maxβ, the earliest access time t1 and the latest access time t2 from this list. Generate a planning scheme based on these four parameters. The relevant parameters of the planning scheme are: imaging side view βplan=(minβ+maxβ) / 2, imaging start time t1plan=t1-Δt, imaging end time t2plan=t2+Δt, where Δt is the time difference between two adjacent access information in the current secondary access information list;

[0075] (502) Add the imaging side view βplan, imaging start time t1plan, imaging end time t2plan, and satellite code wxdh to which the list AiListMost belongs to the planning scheme to the planning scheme list PlanList;

[0076] (503) The planning scheme decomposes the area to be observed into a planned area and an unplanned area. There are four possible spatial relationships between the planned area and the unplanned area: the planned area is on the left and the unplanned area is on the right; the planned area is in the middle and the unplanned areas are on both sides; the planned area is on the right and the unplanned area is on the left; the planned area completely covers the area to be observed and there is no area to be planned.

[0077] (504) Treat each unplanned area as a new observation area and continue to execute step 3. When executing step (402), the access information that conflicts with the planning scheme list PlanList needs to be removed from the total access information list AiList.

[0078] (505) Repeat the above process until the area to be observed is completely covered by the planned area, i.e., the fourth case in step (503), or there is no more available access information, i.e. after step (504) is completed, the total access information list AiList is empty, and the final planning scheme list PlanList is obtained, i.e. the area coverage based on discrete point access information clustering is completed.

[0079] Therefore, this method can achieve maximum coverage calculation of the area to be observed in the shortest time, while avoiding the complexity caused by different satellite observation swaths and orbital directions. It has good adaptability to different area types and different satellite types, and good scalability. It is suitable for the problem of maximizing coverage calculation of a specified area by multiple remote sensing satellites within a certain time in the field of satellite remote sensing mission planning.

[0080] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and does not limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the present invention as defined in the appended claims should be included within the protection scope of the present invention.

Claims

1. A method for calculating region coverage based on discrete point access information clustering, characterized in that, Includes the following steps: Step 1: Create a planning scheme list. Each element in the planning scheme list includes the satellite code wxdh, the imaging side view βplan, the imaging start time t1plan, and the imaging end time t2plan. Step 2: Calculate the nadir imaging swath of all satellites participating in the mission to obtain the minimum imaging swath, and calculate the discrete point interval based on the minimum imaging swath. Step 3: Discretize the area to be observed into multiple equally spaced discrete points according to the discrete point interval, and add all discrete points to the discrete point list after numbering them. Step 4: Cluster the discrete point access information: Calculate the access information of each satellite to each discrete point, and then cluster the access information to form multiple access information lists; specifically: (401) Define the access information data structure Ai, which contains wxdh, mbbh, t and θ; Where wxdh is the satellite code, mbbh is the target discrete point number, t is the time when the satellite visits the target discrete point, and θ is the side angle when the satellite visits the target discrete point. (402) Establish a total access information list AiList, calculate the access information of each satellite to each discrete point in the discrete point list PointList, that is, the time of satellite access to the target discrete point and the side view during the access; save all access information to the total access information list AiList; (403) Generate multiple sets of first-level access information lists based on the total access information list AiList, including AiList1, AiList2, AiList3, etc. Each set of first-level access information lists stores the access information of all discrete points within the entire side view range of the same satellite in the same orbital orbit. (404) Sort all side views in the first-level access information list of each group from smallest to largest: β1≤β2≤β3≤……≤βk； Save all access information in the first-level access information list where the difference between the side viewpoint and βi is less than the field of view angle α to a second-level access information list, i=1, 2, 3..., and so on, until a second-level access information list includes access information with a side viewpoint of βk. Traverse each group of first-level access information lists to finally form a second-level access information list clustered by side view: AiList11, AiList12, ..., AiList21, AiList22, ...; Step 5: Perform task planning on the list with the most access information elements. According to the planning scheme, divide the area to be observed into planned areas and unplanned areas. Take all unplanned areas as new areas to be observed and continue to execute Step 3. Repeat this process until the area to be observed is completely covered or there is no available access information. This completes the area coverage based on discrete point access information clustering.

2. The method for calculating regional coverage based on discrete point access information clustering according to claim 1, characterized in that, The specific method for step 2 is as follows: (201) Calculate the nadir imaging swath W for each satellite: W≈H×α Where H is the satellite's orbital altitude, α is the satellite's payload field of view angle; the minimum imaging swath is the minimum value minW among all satellites' nadir imaging swaths; (202) Calculate the discrete point interval space: space = minW / 3.

3. The method for calculating regional coverage based on discrete point access information clustering according to claim 2, characterized in that, The specific method for step 3 is as follows: (301) Traverse the coordinates of each vertex of the polygon to be observed, and obtain the minimum x-coordinate minX, minimum y-coordinate minY, maximum x-coordinate maxX and maximum y-coordinate maxY among the coordinates of each vertex. Use (minX, minY) and (maxX, maxY) as the coordinates of the two vertices of a diagonal to generate the bounding rectangle of the region to be observed. Insert m columns × n rows of discrete points in the bounding rectangle, where m = [(maxX - minX) / space] + 1, n = [(maxY - minY) / space] + 1, and the coordinates of the discrete point P(i,j) in the i-th column and j-th row are (xi, yj), xi = minX + (i-1) * space, yj = minY + (j-1) * space, 1 ≤ i ≤ m, 1 ≤ j ≤ n, and i and j are integers; (302) Establish a discrete point list PointList, calculate whether each discrete point is inside the polygon of the region to be observed, and add all discrete points inside the polygon of the region to be observed to the discrete point list PointList after numbering them.

4. The method for calculating regional coverage based on discrete point access information clustering according to claim 3, characterized in that, The specific method for step 5 is as follows: (501) Select the list AiListMost with the most access information elements from AiList11, AiList12, ..., AiList21, AiList22, ..., and obtain the minimum side view minβ, the maximum side view maxβ, the earliest access time t1 and the latest access time t2 from this list. Generate a planning scheme based on these four parameters. The relevant parameters of the planning scheme are: imaging side view βplan=(minβ+maxβ) / 2, imaging start time t1plan=t1-Δt, imaging end time t2plan=t2+Δt, where Δt is the time difference between two adjacent access information in the current secondary access information list; (502) Add the imaging side view βplan, imaging start time t1plan, imaging end time t2plan, and satellite code wxdh to which the list AiListMost belongs to the planning scheme to the planning scheme list PlanList; (503) The planning scheme decomposes the area to be observed into a planned area and an unplanned area. There are four possible spatial relationships between the planned area and the unplanned area: the planned area is on the left and the unplanned area is on the right; the planned area is in the middle and the unplanned areas are on both sides; the planned area is on the right and the unplanned area is on the left; the planned area completely covers the area to be observed and there is no area to be planned. (504) Take each unplanned area as a new observation area and continue to execute step 3. When executing step (402), it is necessary to remove the access information that conflicts with the planning scheme list PlanList from the total access information list AiList. (505) Repeat the above process until the area to be observed is completely covered by the planned area, i.e., the fourth case in step (503), or there is no more available access information, i.e. after step (504) is completed, the total access information list AiList is empty, and the final planning scheme list PlanList is obtained, i.e. the area coverage based on discrete point access information clustering is completed.

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