A satellite remote sensing-based all-weather sea ice disaster monitoring method, device, equipment and medium

Abstract

The application discloses a kind of all-weather sea ice disaster monitoring method, device, equipment and medium based on satellite remote sensing, related to disaster detection field, the method comprises: the optical satellite image and synthetic aperture radar image of sea ice monitoring area are acquired;Optical satellite image is obtained by HY-1 C / D CZI acquisition, synthetic aperture radar image is obtained by Sentinel-1 satellite acquisition;According to the cloud amount of optical satellite image to determine the sea ice monitoring area;If cloud amount is less than preset cloud amount threshold, then according to optical satellite image to carry out sea ice pixel recognition, obtain first sea ice distribution result image, determine sea ice distribution area in combination with the spatial resolution of optical satellite image;Otherwise, according to synthetic aperture radar image to carry out sea ice pixel recognition, obtain second sea ice distribution result image, determine sea ice distribution area in combination with the spatial resolution of synthetic aperture radar image.The application realizes the all-weather accurate monitoring of sea ice disaster.

Description

Technical Field

[0001] This application relates to the field of disaster detection, and in particular to a method, device, equipment and medium for all-weather sea ice disaster monitoring based on satellite remote sensing. Background Technology

[0002] Sea ice disasters are natural disasters caused by saltwater ice formed from frozen seawater and land-based ice entering the ocean, impacting coastal zones and maritime activities. Winter sea ice poses a serious threat to offshore oil extraction and shipping, damaging ships and offshore structures, and hindering fisheries production. Continuous and effective monitoring of sea ice and timely acquisition of sea ice information are crucial for marine disaster prevention and mitigation, ensuring the safety of people's lives and property, and minimizing economic losses. Traditional on-site sea ice observation and aerial photography methods have limited coverage and are insufficient to meet monitoring needs. Satellite remote sensing technology can conduct large-scale, continuous observation of widely distributed and complex sea ice, making it a primary technical means for sea ice disaster early warning, tracking, and prevention.

[0003] Currently, there are two main problems with using satellite remote sensing technology for sea ice disaster monitoring. First, existing remote sensing methods for sea ice monitoring typically use a single satellite data source. These data sources have low temporal resolution, cannot image the same area daily, and cannot form continuous observations. Alternatively, the satellite data source may have high temporal resolution but low spatial resolution, significantly reducing the effectiveness of sea ice monitoring and hindering detailed monitoring. Second, because optical remote sensing satellites are passive imaging systems, they are easily affected by lighting conditions and cloud cover, causing sea ice in satellite images to be obscured by clouds, preventing effective sea ice monitoring. Using optical remote sensing data alone cannot achieve continuous observation. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, equipment and medium for all-weather sea ice disaster monitoring based on satellite remote sensing, which can realize accurate all-weather monitoring of sea ice disasters.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for all-weather sea ice disaster monitoring based on satellite remote sensing, including: Optical satellite imagery and synthetic aperture radar imagery of the sea ice monitoring area were acquired; the optical satellite imagery was acquired via HY-1 C / D CZI, and the synthetic aperture radar imagery was acquired via Sentinel-1 satellite. Cloud pixel identification is performed based on the optical satellite imagery to determine the cloud cover in the sea ice monitoring area; If the cloud cover in the sea ice monitoring area is less than a preset cloud cover threshold, then sea ice pixel identification is performed based on the optical satellite image to obtain a first sea ice distribution result image, and the sea ice distribution area of ​​the sea ice monitoring area is determined based on the spatial resolution of the first sea ice distribution result image and the optical satellite image. If the cloud cover in the sea ice monitoring area is greater than or equal to a preset cloud cover threshold, sea ice pixel identification is performed based on the synthetic aperture radar image to obtain a second sea ice distribution result image, and the sea ice distribution area of ​​the sea ice monitoring area is determined based on the spatial resolution of the second sea ice distribution result image and the synthetic aperture radar image.

[0006] Secondly, this application provides an all-weather sea ice disaster monitoring device based on satellite remote sensing, comprising: The data acquisition module is used to acquire optical satellite images and synthetic aperture radar images of the sea ice monitoring area; the optical satellite images are acquired by HY-1 C / D CZI, and the synthetic aperture radar images are acquired by Sentinel-1 satellite; The cloud cover identification module is used to identify cloud pixels based on the optical satellite imagery to determine the cloud cover in the sea ice monitoring area. An optical image detection module is used to identify sea ice pixels based on the optical satellite image when the cloud cover in the sea ice monitoring area is less than a preset cloud cover threshold, to obtain a first sea ice distribution result image, and to determine the sea ice distribution area in the sea ice monitoring area based on the spatial resolution of the first sea ice distribution result image and the optical satellite image. The radar image detection module is used to identify sea ice pixels based on the synthetic aperture radar image when the cloud cover in the sea ice monitoring area is greater than or equal to a preset cloud cover threshold, to obtain a second sea ice distribution result image, and to determine the sea ice distribution area of ​​the sea ice monitoring area based on the spatial resolution of the second sea ice distribution result image and the synthetic aperture radar image.

[0007] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described all-weather sea ice disaster monitoring method based on satellite remote sensing.

[0008] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described all-weather sea ice disaster monitoring method based on satellite remote sensing.

[0009] According to the specific embodiments provided in this application, this application achieves the following technical effects: By leveraging the technical advantages of fusing HY-1 C / DCZI optical satellite imagery and Sentinel-1 synthetic aperture radar imagery, it effectively overcomes the technical bottlenecks of single optical satellite monitoring being susceptible to cloud cover interference and unable to provide refined continuous monitoring. Based on the comparison results between the cloud cover in the monitoring area and a preset threshold, the monitoring data source is flexibly switched: when cloud cover is low, refined continuous sea ice monitoring and distribution area calculation are carried out by relying on the high temporal resolution (two consecutive imaging sessions every three days) and medium-to-high spatial resolution (50-meter resolution) advantages of HY-1 C / DCZI optical satellite imagery; when cloud cover is high, monitoring is carried out by utilizing the characteristic that radar imagery is not obstructed by clouds, ensuring uninterrupted monitoring. This method achieves all-weather, uninterrupted monitoring of sea ice disasters, significantly improving the timeliness and reliability of sea ice distribution data. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is an application environment diagram of an all-weather sea ice disaster monitoring method based on satellite remote sensing, according to one embodiment of this application.

[0012] Figure 2 This is a schematic diagram of the overall process of an all-weather sea ice disaster monitoring method based on satellite remote sensing, provided as an embodiment of this application.

[0013] Figure 3 A detailed flowchart illustrating an all-weather sea ice disaster monitoring method based on satellite remote sensing, provided as an embodiment of this application.

[0014] Figure 4 This is a schematic diagram of the functional modules of an all-weather sea ice disaster monitoring device based on satellite remote sensing, provided as an embodiment of this application.

[0015] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] This application addresses the problem that current sea ice disaster monitoring methods cannot meet the needs of operational use. It proposes an operational sea ice disaster monitoring method that combines optical remote sensing images from the HY-1C / D Coastal Zone Imager (CZI) with images from the Sentinel-1 Synthetic Aperture Radar (SAR). This method enables effective and continuous observation of sea ice disasters while overcoming the influence of cloud cover.

[0018] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] The all-weather sea ice disaster monitoring method based on satellite remote sensing provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be set up independently, integrated into server 102, or placed in the cloud or on another server. Terminal 101 can send optical satellite images and SAR images of the sea ice monitoring area to server 102. After receiving the optical satellite images and synthetic aperture radar images of the sea ice monitoring area, server 102 performs all-weather sea ice disaster monitoring to determine the sea ice distribution area of ​​the monitoring area. Server 102 can then feed back the obtained sea ice distribution area of ​​the monitoring area to terminal 101.

[0020] Among them, terminal 101 can be, but is not limited to, various desktop computers, laptops, smartphones and tablets, and server 102 can be implemented by independent servers or server clusters composed of multiple servers, or it can be a cloud server.

[0021] In one exemplary embodiment, such as Figure 2 and Figure 3 As shown, a method for all-weather sea ice disaster monitoring based on satellite remote sensing is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1Taking server 102 as an example, the explanation includes the following steps 201 to 204.

[0022] Step 201: Acquire optical satellite images and synthetic aperture radar images of the sea ice monitoring area.

[0023] The optical satellite images were acquired by the Ocean-1 C / D optical remote sensing satellite (HY-1 C / D) CZI, and the synthetic aperture radar images were acquired by the Sentinel-1 satellite.

[0024] In a specific application example, step 201 includes steps 11 to 14.

[0025] Step 11: Obtain optical satellite imagery data acquired by HY-1 C / D CZI, synthetic aperture radar imagery data acquired by Sentinel-1 satellite, and vector file of sea ice monitoring area.

[0026] Specifically, download the HY-1 C / D CZI optical satellite imagery data from the China Ocean Satellite Data Service System. Download the Sentinel-1 IW GRDH SAR imagery from the European Space Agency's official data distribution website. Then, read the downloaded HY-1 C / D CZI optical satellite imagery data, Sentinel-1 IW GRDH SAR imagery, and sea ice monitoring area vector file using a computer.

[0027] Step 12: The optical satellite image data is cropped using the vector file of the sea ice monitoring area to obtain the optical satellite image of the sea ice monitoring area, so as to remove the influence of image data outside the sea ice monitoring area on subsequent steps.

[0028] Step 13: Perform trajectory correction, thermal noise removal, radiometric calibration, image filtering, and terrain correction operations on the synthetic aperture radar image data in sequence to obtain preprocessed synthetic aperture radar image.

[0029] Specifically, SNAP software was used to preprocess Sentinel-1 IW GRDH SAR images. SNAP software is a professional data processing software for the Sentinel series satellites provided by the European Space Agency. The images to be processed were loaded into SNAP software, and the toolboxes provided by the software were used to perform orbit correction, thermal noise removal, radiometric calibration, image filtering, and terrain correction operations on the Sentinel-1 IW GRDH SAR images to complete the image preprocessing.

[0030] Step 14: The preprocessed synthetic aperture radar image is cropped using the vector file of the sea ice monitoring area to obtain the synthetic aperture radar image of the sea ice monitoring area, so as to remove the influence of image data outside the sea ice monitoring area on subsequent steps.

[0031] Step 202: Perform cloud pixel identification based on the optical satellite imagery to determine the cloud cover in the sea ice monitoring area.

[0032] In a specific application example, step 202 includes steps 21 to 23.

[0033] Step 21: Calculate the normalized difference water index based on the green band data and near-infrared band data of the optical satellite image to obtain the normalized difference water index image.

[0034] The HY-1 C / D CZI optical satellite imagery data is a four-band satellite imagery, including blue band, green band, red band, and near-infrared band. This application uses the pixel values ​​of the green band and near-infrared band for band calculation, and the formula is Image1=(band(G)-band(NIR)) / (band(G)+band(NIR)). Wherein, Image1 is the normalized difference water index image, band(G) is the green band data of the optical satellite imagery, and band(NIR) is the near-infrared band data of the optical satellite imagery.

[0035] Step 22: Classify the pixels in the normalized differential water index image using a preset cloud pixel threshold to obtain a cloud mask binary image.

[0036] In this application, the preset cloud pixel threshold is set to 0.075, that is, pixels with a value less than or equal to 0.075 in the normalized difference water index image are classified as cloud pixels, and pixels with a value greater than 0.075 are classified as background, thus obtaining a cloud mask binary image.

[0037] Step 23: Calculate the cloud cover in the sea ice monitoring area based on the total number of pixels and the number of cloud pixels in the binary image with the cloud mask. ;in, n This represents the number of cloud pixels in the binary image with the cloud mask. m This represents the total number of pixels in the binary image with the cloud mask.

[0038] Furthermore, this application sets a preset cloud cover threshold of 50%, compares the cloud cover in the sea ice monitoring area with the preset cloud cover threshold, and if the cloud cover is less than 50%, proceed to step 203; if the cloud cover is greater than or equal to 50%, proceed to step 204.

[0039] Step 203: If the cloud cover in the sea ice monitoring area is less than a preset cloud cover threshold, then sea ice pixel identification is performed based on the optical satellite image to obtain a first sea ice distribution result image, and the sea ice distribution area of ​​the sea ice monitoring area is determined based on the spatial resolution of the first sea ice distribution result image and the optical satellite image.

[0040] In a specific application example, step 203 includes steps 31 to 34.

[0041] Step 31: Remove cloud pixels from the normalized differential water index image to obtain the cloud-free index image.

[0042] Specifically, cloud masking is performed on the Normalized Differential Water Index (NDI) image using a cloud-masked binary image. This involves removing cloud pixels from the NDI image based on their identifiers in the cloud-masked binary image, while retaining other non-cloud pixels, resulting in a cloud-removed NDI image. After cloud masking, the actual land cover types in the cloud-removed NDI image only include sea ice and seawater.

[0043] Step 32: Based on the cloud-removed exponential image, determine the first image segmentation threshold α using the Otsu's method (OTSU). The OTSU algorithm is an image thresholding segmentation method that automatically determines the optimal segmentation threshold by analyzing the gray-level histogram characteristics of the image, thus segmenting the image into target and background.

[0044] Step 33: Segment the cloud-removed exponential image according to the first image segmentation threshold α, classify pixels with pixel values ​​less than the first image segmentation threshold α as sea ice (classify pixels with pixel values ​​greater than or equal to the first image segmentation threshold α as background) to obtain the first sea ice distribution result image.

[0045] Step 34, use the formula S=b 2 Determine the sea ice distribution area of ​​the sea ice monitoring area. Where S is the sea ice distribution area of ​​the sea ice monitoring area, a is the number of sea ice pixels in the first sea ice distribution result image, and b is the spatial resolution of the optical satellite image (50 meters).

[0046] Step 204: If the cloud cover in the sea ice monitoring area is greater than or equal to a preset cloud cover threshold, then sea ice pixel identification is performed based on the synthetic aperture radar image to obtain a second sea ice distribution result image, and the sea ice distribution area of ​​the sea ice monitoring area is determined based on the spatial resolution of the second sea ice distribution result image and the synthetic aperture radar image.

[0047] In a specific application example, step 204 includes steps 41 to 44.

[0048] Step 41: Calculate the Standardized Dual-polarization Water Index (SDWI) based on the polarization band of the synthetic aperture radar image to obtain a standardized dual-polarization water index image.

[0049] Specifically, using the formula Calculate the Standardized Bipolar Water Index (SDWI). SDWI is the Standardized Bipolar Water Index. For the vertical transmission and vertical reception polarization band of synthetic aperture radar imagery. This is the vertical transmission and horizontal reception polarization band for synthetic aperture radar imagery.

[0050] Step 42: Based on the standardized dual-polarization water index image, determine the second image segmentation threshold β using the maximum inter-class variance method.

[0051] Step 43: Segment the standardized dual-polarization water index image according to the second image segmentation threshold β, classify pixels with pixel values ​​greater than the second image segmentation threshold β as sea ice (classify pixels with pixel values ​​less than or equal to the second image segmentation threshold β as background) to obtain the second sea ice distribution result image.

[0052] Step 44, using the formula S=d 2 Determine the sea ice distribution area of ​​the sea ice monitoring area; where S is the sea ice distribution area of ​​the sea ice monitoring area, c is the number of sea ice pixels in the second sea ice distribution result image, and d is the spatial resolution of the synthetic aperture radar image (10 meters).

[0053] This application proposes a collaborative method for operational monitoring of sea ice hazards in winter, utilizing optical and SAR remote sensing imagery. This method leverages remote sensing technology to enable large-scale, continuous observation of sea ice hazards. The selected HY-1 C / D sensor allows for continuous observation of the same area twice every three days, ensuring data continuity for operational sea ice hazard monitoring. Simultaneously, Sentinel-1 SAR data is chosen to overcome the limitations of cloud and rain weather on optical satellite imaging, enabling the acquisition of sea ice monitoring images even under cloud and rain conditions. By combining optical remote sensing data and SAR data, the distribution range and area of ​​sea ice are extracted, meeting the requirements for all-weather operational sea ice hazard monitoring.

[0054] In summary, compared with the prior art, the beneficial effects of this application include at least the following: (1) High frequency of satellite data acquisition. HY-1 C / D CZI data has high temporal resolution and medium-high spatial resolution of 50 meters, which can realize fine and continuous monitoring of sea ice.

[0055] (2) Unaffected by clouds in sea ice identification. Sentinel-1 SAR is an active microwave imaging system that can penetrate clouds and has the advantages of all-weather operation and 10-meter high resolution. It is an effective supplement to optical data in cloud-covered conditions.

[0056] (3) Compared with a single data source, this application combines HY-1 C / D optical data and Sentinel-1 SAR data to meet the operational monitoring needs of sea ice disasters with high frequency, wide range and all weather conditions.

[0057] Based on the same inventive concept, this application also provides an apparatus for implementing the method described above. The solution provided by this apparatus is similar to the solution described in the above method; therefore, specific limitations in one or more apparatus embodiments provided below can be found in the limitations of the method described above, and will not be repeated here.

[0058] In one exemplary embodiment, such as Figure 4 As shown, a satellite remote sensing-based all-weather sea ice disaster monitoring device is provided, which includes the following functional modules.

[0059] The data acquisition module 401 is used to acquire optical satellite images and synthetic aperture radar images of the sea ice monitoring area. The optical satellite images are acquired by HY-1 C / D CZI, and the synthetic aperture radar images are acquired by Sentinel-1 satellite.

[0060] The cloud cover identification module 402 is used to identify cloud pixels based on the optical satellite imagery to determine the cloud cover in the sea ice monitoring area.

[0061] The optical image detection module 403 is used to identify sea ice pixels based on the optical satellite image when the cloud cover in the sea ice monitoring area is less than a preset cloud cover threshold, obtain a first sea ice distribution result image, and determine the sea ice distribution area of ​​the sea ice monitoring area based on the spatial resolution of the first sea ice distribution result image and the optical satellite image.

[0062] The radar image detection module 404 is used to identify sea ice pixels based on the synthetic aperture radar image when the cloud cover in the sea ice monitoring area is greater than or equal to a preset cloud cover threshold, obtain a second sea ice distribution result image, and determine the sea ice distribution area of ​​the sea ice monitoring area based on the spatial resolution of the second sea ice distribution result image and the synthetic aperture radar image.

[0063] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 5As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores optical satellite imagery and synthetic aperture radar imagery of the sea ice monitoring area. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a satellite remote sensing-based all-weather sea ice disaster monitoring method.

[0064] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0065] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0066] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0067] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0068] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0069] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.

[0070] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0071] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for all-weather sea ice disaster monitoring based on satellite remote sensing, characterized in that, The all-weather sea ice disaster monitoring method based on satellite remote sensing includes: Optical satellite imagery and synthetic aperture radar imagery of the sea ice monitoring area were acquired; the optical satellite imagery was acquired via HY-1C / D CZI, and the synthetic aperture radar imagery was acquired via Sentinel-1 satellite. Cloud pixel identification is performed based on the optical satellite imagery to determine the cloud cover in the sea ice monitoring area; If the cloud cover in the sea ice monitoring area is less than a preset cloud cover threshold, then sea ice pixel identification is performed based on the optical satellite image to obtain a first sea ice distribution result image, and the sea ice distribution area of ​​the sea ice monitoring area is determined based on the spatial resolution of the first sea ice distribution result image and the optical satellite image. If the cloud cover in the sea ice monitoring area is greater than or equal to a preset cloud cover threshold, sea ice pixel identification is performed based on the synthetic aperture radar image to obtain a second sea ice distribution result image, and the sea ice distribution area of ​​the sea ice monitoring area is determined based on the spatial resolution of the second sea ice distribution result image and the synthetic aperture radar image.

2. The all-weather sea ice disaster monitoring method based on satellite remote sensing according to claim 1, characterized in that, Acquire optical satellite imagery and synthetic aperture radar imagery of the sea ice monitoring area, including: Acquire optical satellite imagery data from HY-1 C / D CZI, synthetic aperture radar imagery data from Sentinel-1 satellite, and vector files of the sea ice monitoring area. The optical satellite image data is cropped using the vector file of the sea ice monitoring area to obtain the optical satellite image of the sea ice monitoring area; The synthetic aperture radar image data is sequentially subjected to orbit correction, thermal noise removal, radiometric calibration, image filtering, and terrain correction operations to obtain a preprocessed synthetic aperture radar image. The preprocessed synthetic aperture radar image is cropped using the vector file of the sea ice monitoring area to obtain the synthetic aperture radar image of the sea ice monitoring area.

3. The all-weather sea ice disaster monitoring method based on satellite remote sensing according to claim 1, characterized in that, Cloud pixel identification is performed based on the optical satellite imagery to determine cloud cover in the sea ice monitoring area, including: The normalized differential water index is calculated based on the green band data and near-infrared band data of the optical satellite imagery to obtain the normalized differential water index image. The pixels in the normalized differential water index image are classified using a preset cloud pixel threshold to obtain a cloud mask binary image. The cloud cover in the sea ice monitoring area is calculated based on the total number of pixels and the number of cloud pixels in the binary image of the cloud mask.

4. The all-weather sea ice disaster monitoring method based on satellite remote sensing according to claim 3, characterized in that, Based on the optical satellite imagery, sea ice pixel identification is performed to obtain a first sea ice distribution result image, including: The cloud pixels in the normalized differential water index image are removed to obtain the cloud-free index image. Based on the cloud-removed exponential image, the first image segmentation threshold is determined using the maximum inter-class variance method; The cloud-free exponential image is segmented according to the first image segmentation threshold, and pixels with pixel values ​​less than the first image segmentation threshold are classified as sea ice to obtain the first sea ice distribution result image.

5. The all-weather sea ice disaster monitoring method based on satellite remote sensing according to claim 1, characterized in that, Based on the synthetic aperture radar imagery, sea ice pixel identification is performed to obtain a second sea ice distribution result image, including: Based on the polarization band of the synthetic aperture radar image, the standardized dual-polarization water index is calculated to obtain a standardized dual-polarization water index image. Based on the standardized dual-polarized water index image, the second image segmentation threshold is determined using the maximum inter-class variance method. The standardized dual-polarization water index image is segmented according to the second image segmentation threshold, and pixels with pixel values ​​greater than the second image segmentation threshold are classified as sea ice to obtain a second sea ice distribution result image.

6. The all-weather sea ice disaster monitoring method based on satellite remote sensing according to claim 5, characterized in that, Using formula Calculate the Standardized Bipolar Water Index; where SDWI is the Standardized Bipolar Water Index. For the vertical transmission and vertical reception polarization band of synthetic aperture radar imagery. This is the vertical transmission and horizontal reception polarization band for synthetic aperture radar imagery.

7. The all-weather sea ice disaster monitoring method based on satellite remote sensing according to claim 1, characterized in that, Using the formula S=b 2 Or S=d 2 Determine the sea ice distribution area of ​​the sea ice monitoring area; where S is the sea ice distribution area of ​​the sea ice monitoring area, a is the number of sea ice pixels in the first sea ice distribution result image, b is the spatial resolution of the optical satellite image, c is the number of sea ice pixels in the second sea ice distribution result image, and d is the spatial resolution of the synthetic aperture radar image.

8. A satellite remote sensing-based all-weather sea ice disaster monitoring device, characterized in that, The satellite remote sensing-based all-weather sea ice disaster monitoring device performs the satellite remote sensing-based all-weather sea ice disaster monitoring method according to any one of claims 1-7, wherein the satellite remote sensing-based all-weather sea ice disaster monitoring device comprises: The data acquisition module is used to acquire optical satellite images and synthetic aperture radar images of the sea ice monitoring area; the optical satellite images are acquired by HY-1 C / D CZI, and the synthetic aperture radar images are acquired by Sentinel-1 satellite; The cloud cover identification module is used to identify cloud pixels based on the optical satellite imagery to determine the cloud cover in the sea ice monitoring area. An optical image detection module is used to identify sea ice pixels based on the optical satellite image when the cloud cover in the sea ice monitoring area is less than a preset cloud cover threshold, to obtain a first sea ice distribution result image, and to determine the sea ice distribution area in the sea ice monitoring area based on the spatial resolution of the first sea ice distribution result image and the optical satellite image. The radar image detection module is used to identify sea ice pixels based on the synthetic aperture radar image when the cloud cover in the sea ice monitoring area is greater than or equal to a preset cloud cover threshold, to obtain a second sea ice distribution result image, and to determine the sea ice distribution area of ​​the sea ice monitoring area based on the spatial resolution of the second sea ice distribution result image and the synthetic aperture radar image.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the all-weather sea ice disaster monitoring method based on satellite remote sensing as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the all-weather sea ice disaster monitoring method based on satellite remote sensing as described in any one of claims 1-7.

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