An on-orbit disaster risk area lightweight extraction method and system

Abstract

The present application relates to a kind of on-orbit disaster risk area lightweight extraction method and system, belong to the cross technical field of satellite remote sensing data processing and emergency disaster response.First, satellite remote sensing image is received and preprocessed on-orbit, then disaster risk area is extracted on-orbit, geographic spatial data conversion;Data lightweight: after the data of conversion format are respectively simplified using boundary simplification function, centroid is extracted using centroid calculation function, and simplified vector data is generated;Data download based on beidou short message, ground data receiving and visual display steps.The present application can realize "on-orbit complete core processing, lightweight data adapt beidou, minute level download to ground", break through the timeliness bottleneck of disaster emergency response.

Description

TECHNICAL FIELD

[0001] The application relates to an on-board disaster risk area lightweight extraction method and system, and belongs to the technical field of satellite remote sensing data processing and emergency disaster response. BACKGROUND

[0002] Currently, the monitoring of some disasters, such as flood disasters, mainly relies on high-resolution remote sensing images of SAR satellites. The technical process is as follows: the SAR satellite acquires original images in orbit, the original images are transmitted to the ground station through the satellite-ground data transmission link, the ground center completes preprocessing such as radiation correction and geometric precision correction, the water area is extracted based on the SDWI (standardized difference water body index) algorithm, and the flood risk area result is generated and pushed to the relevant units.

[0003] The main defects of the prior art are as follows:

[0004] (1) Low data transmission efficiency, dependent on ground station coverage,

[0005] The original image data of the SAR satellite is extremely large, with a single scene data volume of about 5-10 GB. The satellite-ground data transmission link rate is limited by bandwidth, and the single scene data transmission takes 10-60 minutes. If the satellite is in the coverage blind area of the ground station (such as the ocean, remote mountainous areas), the data transmission delay can be extended to several hours or even several days, which cannot meet the "minute-level response" requirement of flood disaster emergency;

[0006] (2) Long ground processing flow, time efficiency bottleneck prominent,

[0007] After the original image is transmitted, multi-step preprocessing is completed on the ground by professional software (such as ENVI and ArcGIS), and the preprocessing alone takes 1-2 hours. Subsequent water extraction and risk area labeling require manual intervention of parameters. From data reception to result output, the whole process takes more than 4 hours on average, missing the golden time for rescue in the early stage of flood disaster;

[0008] (3) Not adapted to the emergency transmission capacity of Beidou short message,

[0009] Beidou short message has the advantages of global coverage, anti-interference and independence from ground stations, but the transmission capacity of a single message is limited to 14000 bits (about 1.75 KB). The existing technology does not design an integrated scheme of "on-board extraction and lightweight compression" for this capacity limit, and can only transmit text type warning information, which cannot transmit the spatial range data of the flood risk area through the Beidou short message, resulting in the emergency potential of the Beidou short message not being activated.

[0010] There are some related researches in the prior art, such as patent CN120214791, but there are still some deficiencies:

[0011] (1) Only the pre-processing on the satellite is realized, the risk extraction and risk area identification on the satellite are not involved, and the core processing still needs to be completed on the ground;

[0012] (2) The Beidou short message is only used for transmitting text early warning, and a lightweight scheme is not designed for spatial vector data, which cannot adapt to the current hanging map operation demand;

[0013] (3) The closed-loop process of "on-satellite processing-lightweight-Beidou downlink-ground display" is not formed, and the timeliness of emergency response is still restricted by the ground link. SUMMARY

[0014] The purpose of the present application is to overcome the above-mentioned deficiencies and provide a kind of on-satellite disaster risk area lightweight extraction method, realize "on-satellite complete core processing, lightweight data adapt to Beidou, minute level downlink to ground", break through the timeliness bottleneck of disaster emergency response.

[0015] The technical scheme adopted by the present application is:

[0016] An on-satellite disaster risk area lightweight extraction method, comprising the following steps:

[0017] S1. Receiving satellite remote sensing image and on-satellite pre-processing;

[0018] S2. On-satellite extraction of disaster risk area;

[0019] S3. Geospatial data conversion;

[0020] S4. Data lightweight: the converted data is respectively simplified by a boundary simplification function, and the centroid is extracted by a centroid calculation function, to generate simplified vector data;

[0021] S5. Data downlink based on Beidou short message.

[0022] In the above method, the satellite remote sensing image of step S1 is preferably a synthetic aperture radar (SAR) satellite image, and the pre-processing is radiation correction, radiation calibration and geometric correction.

[0023] In step S2, the on-satellite extraction of disaster risk area is water body change extraction for water disaster or temperature anomaly extraction for fire disaster or deformation change extraction for earthquake according to the disaster type.

[0024] The geospatial data conversion of step S3 includes obtaining the extracted data, encoding adaptation, then extracting the coordinate system, converting the data format, and outputting and saving.

[0025] The data format conversion in step S3 is to convert the geographic feature data in the Shapefile into a data structure in the GeoJSON format specification, and embed the original coordinate system information extracted by the coordinate system into a metadata field of the GeoJSON data structure, so as to ensure the consistency of the spatial position information.

[0026] The boundary simplification process in step S4 is that, for an element of the geometry type "polygon", a topological maintenance algorithm is used to simplify the boundary with a tolerance as a threshold, the number of boundary vertices is reduced by removing the vertices on the boundary deviating from the simplified path by less than or equal to the tolerance, and the topological structure of the polygon is maintained.

[0027] The simplified polygon is denoted as Ps, and satisfies: Ps=Simplify(P,t),

[0028] Wherein, P is the original polygon, t is the tolerance, and Simplify(·) is a topological maintenance boundary simplification function.

[0029] The centroid extraction process in step S4 is to calculate the geometric centroid based on the original planar element, and for a polygon P, the coordinates of the centroid point C are calculated by a geometric center algorithm of the planar element:

[0030] C=Centroid(P),

[0031] Wherein, Centroid(·) is a centroid calculation function, and outputs the two-dimensional coordinates (x c , y c ) of the centroid, and the coordinate system is consistent with the original data.

[0032] In step S5, the data is first packaged: the vector data after boundary simplification and the extracted centroid point data are read by the spaceborne Beidou short message payload, and are packaged into a data frame according to the Beidou short message protocol;

[0033] Then, the data frame is transmitted to the ground system terminal through the Beidou satellite constellation.

[0034] The above method further includes a ground data receiving and visual display step.

[0035] Another object of the present application is to provide a lightweight disaster risk area extraction system on board a satellite, which comprises a spaceborne preprocessing module for receiving and preprocessing satellite remote sensing images;

[0036] A disaster risk area extraction module on board a satellite is used to extract water body changes of water disasters, extract temperature anomalies of fires, or extract deformation changes of earthquakes according to the types of disasters, so as to obtain a risk area.

[0037] A satellite-borne geographic space data conversion module is used for obtaining the extracted data, performing encoding adaptation, then performing coordinate system extraction, converting the data format, and outputting and saving;

[0038] A satellite-borne data lightening module is used for performing boundary simplification on the data in the converted format by using a boundary simplification function, and performing centroid extraction by using a centroid calculation function, to generate simplified vector data;

[0039] A data on-orbit downlink module is used for encapsulating the lightened data into a data frame according to a Beidou short message protocol, and then performing on-orbit transmission.

[0040] The present application has the following advantages:

[0041] 1. The present application is designed for the capacity limit of 1.75KB of the Beidou short message, and through the combined processing of "simplifying the boundary of the risk area + extracting the core center point + converting into light data format", the originally large risk area space data is compressed to the transmission range, so that the specific range information of the disaster risk area can be transmitted through the Beidou short message, and the emergency transmission potential of the Beidou global coverage and anti-interference is fully activated;

[0042] 2. The present application avoids the long downlink time of traditional raw images, and only transmits KB-level light data through the Beidou short message after the core processing on the satellite, so that the downlink time is reduced to several minutes, and is not limited by the coverage of the ground station. The blind area such as the ocean and remote mountainous area can also be transmitted in real time, so that the core pain point of data downlink delay is solved;

[0043] 3. The present application overcomes the problems of large data volume, slow transmission and long processing time in the prior art, and is applied to the on-satellite real-time data processing scene of remote sensing satellites, especially SAR satellites. It is suitable for real-time monitoring and rapid response of flood disasters in meteorological, water conservancy and emergency management departments. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 The present application is a method flowchart;

[0045] Figure 2 The present application is a schematic diagram of the remote sensing image after preprocessing on the satellite;

[0046] Figure 3 The present application is a schematic diagram of the water extraction result on the satellite;

[0047] Figure 4 The present application is a data conversion result and file attribute screenshot;

[0048] Figure 5Figures for lightweight results and file attributes in the embodiments of the present application are shown in the drawings.

[0049] Figure 6 Figures for ground system display in the embodiments of the present application are shown in the drawings. DETAILED DESCRIPTION

[0050] The present application is further described below in conjunction with specific embodiments.

[0051] Embodiment 1: A method for extracting disaster risk areas on a satellite, comprising the steps (see Figure 1 ) as follows:

[0052] S1. Receiving and preprocessing of satellite remote sensing images:

[0053] Taking a SAR satellite as an example, after completing the on-orbit observation task, the on-board preprocessing module directly reads the original image data, and completes the preprocessing work of radiation correction, radiation calibration, and geometric correction.

[0054] S2. On-board extraction of disaster risk areas:

[0055] Taking water disaster as an example, water body extraction: the on-board embedded algorithm calls the standardized image generated in step S1, extracts the water body area in the image through the method of standardized water body index (SDWI), and the formula is as follows:

[0056] ,

[0057] SDWI: standardized differential water body index,

[0058] : VV polarization backscattering coefficient,

[0059] : VH polarization backscattering coefficient,

[0060] Data output and storage: through the water body extraction algorithm, the water body vector data in Shapefile format is output, and stored to the specified storage location on the satellite, and recorded as A2;

[0061] Extraction of flood disaster risk areas: the water body area range of rivers, lakes, reservoirs and the like in the current regional normal state is made in advance to form water body vector data in Shapefile format recorded as A1, and transmitted to the specified storage area of the satellite; the risk area is ΔA=A2-A1, and the output ΔA is stored to the specified area.

[0062] S3. Conversion of geospatial data:

[0063] Input acquisition: Get the storage path of the Shapefile data of ΔA, identify the target file with the extension ".shp" in the path; if the target file does not exist, trigger a file does not exist exception;

[0064] Encoding adaptation: For the character encoding characteristics of Shapefile data, use the preset encoding set (utf-8, gbk, gb2312, cp936, latin-1) to try encoding analysis in turn until the file content is successfully parsed, ensuring the correct identification of special characters such as Chinese; if all encodings cannot be parsed, trigger an encoding adaptation exception;

[0065] Coordinate system extraction: Extract the original geographic coordinate system information from the successfully parsed Shapefile data, and record the parameters of the coordinate system (including spatial reference identifier, projection parameters);

[0066] Format conversion: Convert the geographic feature data in Shapefile to GeoJSON format specification data structure, and embed the original coordinate system information extracted by the coordinate system into the metadata field of the GeoJSON data structure, ensuring the consistency of spatial location information;

[0067] Output saving: Write the converted GeoJSON data in utf-8 encoding to the target storage path to generate an output file with the extension ".json"; if the target path already exists a file with the same name, delete the original file first and then write new data to complete the conversion.

[0068] S4. Data lightweight:

[0069] Input data acquisition: Get the face-shaped vector data ΔA to be processed, read the geometric elements and associated attribute information (non-spatial attribute fields) in the data;

[0070] Boundary simplification: For the filtered face-shaped elements, perform boundary simplification based on the preset tolerance (tolerance), which includes:

[0071] For elements with geometry type "polygon", use the topological preservation algorithm to simplify the boundary with tolerance as the threshold - by removing the vertices on the boundary that deviate from the simplified path by less than or equal to the tolerance, the number of boundary vertices is reduced while the topological structure of the polygon is maintained.

[0072] Simplify the polygon after simplification as Ps, which satisfies: Ps = Simplify (P, t),

[0073] Where P is the original polygon, t is the tolerance (t > 0, unit consistent with the data coordinate system), and Simplify (·) is a topological preservation boundary simplification function.

[0074] Centroid extraction: the geometric centroid of the original planar feature is calculated, and for a polygon P, the coordinates of the centroid point C are calculated by the geometric center algorithm of the planar feature: C = Centroid (P) where Centroid (·) is a centroid calculation function, and the two-dimensional coordinates (x c , y c ) of the centroid are output, and the coordinate system is consistent with the original data.

[0075] Result output: generate boundary simplified vector data, associate the obtained simplified planar feature with the original attribute information, output in the specified format.json, and keep the original coordinate system information. Generate centroid point data: associate the obtained centroid point with the original attribute information, output in the specified format.json, and keep the original coordinate system information.

[0076] S5. Data download based on Beidou short message:

[0077] Data encapsulation: the on-board Beidou short message payload reads the lightweight vector data and centroid point data, and encapsulates them into data frames according to the Beidou short message protocol;

[0078] On-orbit delivery: deliver the data frames to the ground system terminal through the Beidou satellite constellation.

[0079] S6. Ground data reception and visual display:

[0080] Data analysis: after receiving the data frame, the ground system device automatically analyzes the risk area vector data and centroid point information;

[0081] Map overlay: the emergency display system overlays the analyzed vector risk area to the electronic map;

[0082] Early warning push: generate a visual warning interface and push it to the emergency person through SMS and APP.

[0083] After the satellite remote sensing image of a certain area is preprocessed on board, the processed image can be obtained, such as Figure 2 , the water body of the image is extracted, and the extraction result is as follows Figure 3 , the Shapefile data is converted to GeoJSON data, and the conversion result is as follows Figure 4 , the converted GeoJSON data is lightened, and the result is as follows Figure 5 , after the data download based on Beidou short message, the ground data reception and visual display are as follows Figure 6 .

[0084] Embodiment 2: an on-board disaster risk area lightweight extraction system, comprising an on-board preprocessing module for receiving and preprocessing satellite remote sensing images;

[0085] A disaster risk area on-board extraction module is configured to extract water body changes of water disasters, extract temperature anomalies of fires, or extract deformation changes of earthquakes according to disaster types, and obtain a risk area;

[0086] A space-borne geospatial data conversion module is configured to obtain the extracted data, perform encoding adaptation, then perform coordinate system extraction, convert data formats, and output and save;

[0087] A space-borne data lightweight module is configured to perform boundary simplification on the converted data by using a boundary simplification function, and extract centroids by using a centroid calculation function, to generate simplified vector data;

[0088] A data on-orbit downlink module is configured to encapsulate the lightweight data into data frames according to a Beidou short message protocol, and then perform on-orbit downlink.

[0089] The above is a further description of the present application in combination with specific embodiments, and the protection scope of the present application is not limited thereto.

Claims

1. An on-board disaster risk zone lightweight extraction method, characterized by, The steps include the following: S1. Receiving and on-satellite preprocessing of satellite remote sensing images; S2. On-satellite extraction of disaster risk areas; S3. Conversion of geographic spatial data; converting geographic feature data in Shapefile into a data structure in GeoJSON format specification, and embedding original coordinate system information extracted from the coordinate system into the metadata field of the GeoJSON data structure, to ensure consistency of spatial position information; S4. Data lightweight; simplifying the boundary of the data converted into the format by using a boundary simplification function, and extracting the centroid by using a centroid calculation function, to generate simplified vector data; The boundary simplification process is as follows: for an element of the geometry type "polygon", a topological preservation algorithm is used to simplify the boundary with a tolerance as the threshold, the number of boundary vertices is reduced by removing the vertices on the boundary deviating from the simplified path by less than or equal to the tolerance, while the topological structure of the polygon is maintained, The simplified polygon is denoted as Ps, and it satisfies: Ps=Simplify(P,t), where P is the original polygon, t is the tolerance, and Simplify(·) is a boundary simplification function for topological preservation; The centroid extraction process is as follows: the geometric centroid of the original planar element is calculated, and for a polygon P, the coordinates of the centroid point C are calculated by the geometric center algorithm of the planar element, C=Centroid(P), Where Centroid() is the centroid calculation function, which outputs the two-dimensional coordinates (x, y) of the centroid. c y c The coordinate system is consistent with the original data; S5. Data download based on Beidou short message; the data is first packaged, the vector data simplified and the centroid point data extracted are read by the on-satellite Beidou short message payload, and are packaged into a data frame according to the Beidou short message protocol; Then, the data is transmitted on-orbit to the ground system terminal through the Beidou satellite constellation.

2. The method of claim 1, wherein the method is characterized by: The satellite remote sensing images in step S1 are preferably synthetic aperture radar satellite images, and the preprocessing is radiation correction, radiation calibration and geometric correction.

3. The method of claim 1, wherein the method is characterized by: In step S2, the on-satellite extraction of disaster risk areas is performed according to the type of disaster, such as water body change extraction for water disasters, temperature anomaly extraction for fires, or deformation change extraction for earthquakes.

4. The method of claim 1, wherein the method is characterized by: The geographic spatial data conversion in step S3 includes obtaining the extracted data, performing encoding adaptation, then extracting the coordinate system, converting the data format, and outputting and saving.

5. The method of claim 1, wherein the method is characterized by: It also includes a ground data receiving and visual display step.

6. A system for implementing the on-board disaster risk zone lightweight extraction method according to claim 1, characterized in that, The system includes an on-satellite preprocessing module for receiving and on-satellite preprocessing of satellite remote sensing images; An on-satellite disaster risk area extraction module for extracting disaster risk areas according to the type of disaster, such as water body change extraction for water disasters, temperature anomaly extraction for fires, or deformation change extraction for earthquakes; An on-satellite geographic spatial data conversion module for obtaining the extracted data, performing encoding adaptation, then extracting the coordinate system, converting the data format, and outputting and saving; An on-satellite data lightweight module for simplifying the boundary of the data converted into the format by using a boundary simplification function, and extracting the centroid by using a centroid calculation function, to generate simplified vector data; A data on-orbit transmission module for packaging the lightweight data into a data frame according to the Beidou short message protocol, and then transmitting the data on-orbit.

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