122 results about "Sea ice" patented technology

The invention discloses a ship route planning method and system in a polar region sea ice environment and a program product. The ship route planning method comprises the steps of preprocessing satellite remote sensing radiation intensity data, performing image reconstruction of inter-ice water channel detection through an improved Vision Transform model, planning and visualizing a ship route and the like. The deep features of the image blocks are extracted through a Transformer encoder which comprises a multi-head self-attention mechanism and is processed by a multi-layer perceptron; and the Transform encoder firstly performs layer normalization on the input, and then performs multi-head attention mechanism and multiple linear transformation and regularization processing. And realizing path planning in an inter-ice water channel environment by fusing a dynamic planning cost function through a fast marching method. Compared with a traditional algorithm, the method has the advantages that the calculation precision is higher in the same scene, the calculation efficiency is greatly improved, and the planning result obtained after multiple costs are comprehensively considered is given instead of pursuing the shortest distance.

The invention discloses a fan site selection method and system based on different-mode nested grids, and belongs to the technical field of fan site selection, and the method comprises the steps: firstly constructing a large-region sea ice background field by using global data to preliminarily screen candidate regions, then constructing a small-region high-resolution background field through a grid nesting technology, and carrying out the dynamic simulation, according to the method, a sea ice evolution field is obtained, an initial field is corrected by fusing field and satellite data, errors are quantized, comprehensive scoring is carried out to screen target site selection, finally, simulation parameters are dynamically optimized according to actually measured data, and a final site selection is determined through iterative correction. According to the method, multi-scale evaluation from macroscopic preliminary screening to microcosmic fine screening is realized, and through data fusion and dynamic optimization, the precision and reliability of fan site selection in a complex sea ice environment are remarkably improved.

The invention relates to the technical field of meteorological model optimization and polar region ocean data processing, in particular to a method and system for optimizing a COARE model based on sea ice density, and the method comprises the steps: obtaining the historical actual measurement data of the polar region ocean, calculating the real roughness and the model roughness outputted by the COARE model, and determining the roughness difference value; dividing a wind speed interval, and fitting and establishing a correction function of a difference value between the sea ice concentration and the roughness; and introducing the sea ice concentration into the COARE model, judging the interval according to the wind speed, calling a corresponding function to correct the roughness of the model, and outputting the ocean-atmosphere flux conforming to the polar region sea ice condition to complete the model optimization. According to the method, the sea ice concentration is introduced, and the wind speed partition correction relation is established, so that the roughness of the polar region under the sea ice coverage condition is dynamically corrected by the COARE model, the limitation of the original model in the aspects of wind speed applicability and polar region adaptability is broken through, and the accuracy and practicability of polar region ocean-atmosphere flux calculation are remarkably improved.

The invention relates to the technical field of intelligent transportation, in particular to a state space modeling method for ship ice region navigation decision model training, which comprises the following steps of: constructing a sea ice dynamic model, and estimating an unobservable sea ice state by using unscented Kalman filtering to obtain a hidden variable vector; inputting the hidden variable vector, the observable state information and the external force field code into a Transform encoder, and outputting future sea ice center position distribution; constructing a confidence collision avoidance domain of the time index according to future sea ice center position distribution, wherein the confidence collision domain is updated in a rolling manner along with time; taking observable state information, hidden variable vectors and transformation embedding of a confidence collision avoidance domain as reinforcement learning states, and adopting near-end strategy optimization to train a sea ice dynamic model; and obtaining a ship ice region navigation decision model based on a joint termination criterion of the verification set collision rate, the arrival rate and the strategy divergence. The method improves the strategy convergence stability and the ice region safety passing rate, and is suitable for autonomous navigation and simulation training of polar regions and high-latitude sea areas.

The invention relates to the technical field of remote sensing image processing, and provides a satellite remote sensing sea ice detection method and device. The method comprises the following steps: processing a multispectral remote sensing image of a target sea area to obtain image data; extracting multi-dimensional features including an edge feature, a first spectral feature and a second spectral feature from the image data; wherein the edge feature is obtained by performing adaptive gradient enhancement and edge probability smoothing processing on the image data, the first spectral feature comprises a sea ice index after correction of suspended sediment influence, and the second spectral feature comprises a water body index; and performing sea ice distribution judgment on each pixel in the image data according to a classification threshold value corresponding to each feature in the multi-dimensional features, and generating a sea ice distribution diagram. According to the satellite remote sensing sea ice detection method and device provided by the invention, the high-precision identification of the sea ice can be realized based on the multi-dimensional features including the edge features, the first spectral features and the second spectral features extracted from the image data.

The invention discloses a passive microwave and SAR data fused sea ice concentration inversion method and device, and relates to the field of remote sensing image processing, and the method comprises the steps: carrying out the geographic projection and resampling of preprocessed SAR data; performing calculation to obtain sea ice concentration inversion parameters, aligning the re-sampled AMSR2 data with the re-sampled SAR data, and cutting an image overlapping region to obtain an image pair; training a Pix2PixGAN network, and inputting a part of sea ice concentration inversion parameters to the Pix2PixGAN network to obtain an ice water classification result; and removing a seawater region in the remaining sea ice concentration inversion parameters according to the obtained ice water classification result, inputting the remaining sea ice concentration inversion parameters after the removal operation into the Pix2PixGAN network, and calculating an output result by adopting an ASI algorithm to obtain the sea ice concentration. According to the invention, high-precision sea ice concentration inversion can be realized.

This invention provides an unmanned aerial vehicle (UAV) for intelligent identification of polar ice rinks, comprising a main body, a solar panel, control wings, a propeller, and an observation cabin. The UAV is powered by solar energy, enabling a long endurance. The control wings are symmetrically mounted on both sides of the main body, enabling ascent, descent, and steering. The propeller provides propulsion, working in conjunction with the control wings to complete ascent, descent, and steering movements. The observation cabin incorporates multiple functional modules for ice rink identification, UAV and transport ship positioning, data transmission, and status monitoring. Illumination warning lights are installed on the leading edges of the wings to facilitate ice rink identification and landing during polar night conditions. A method for intelligent identification of polar ice rinks using this UAV is also provided. This invention enables detailed and accurate observation of sea ice morphology around shipping lanes, obtaining precise sea ice data to aid polar transport ships in navigation, assist captains in decision-making, and reduce the risk of distress.

This application discloses a multi-scale forecasting method and system for polar sea ice, relating to the field of climate forecasting technology. The method includes: acquiring raw sea ice concentration observation data; preprocessing the raw sea ice concentration observation data to obtain preprocessed data; extracting multi-scale spatial features from the preprocessed data to obtain feature tokens at multiple scales; aligning the feature tokens at multiple scales by feature dimensions to obtain aligned features; performing cross-scale attention fusion on the aligned features to obtain cross-scale fused features; performing intra-scale modeling optimization on the cross-scale fused features to obtain intra-scale optimized features; and performing linear mapping restoration and spatial decoding on the intra-scale optimized features to obtain sea ice concentration forecast results. The sea ice concentration forecast results include future daily, weekly, and monthly sea ice concentration forecasts. This application can solve the problems of single-scale modeling and information silos in existing technologies.

The invention provides a sea ice thickness extraction method and device based on unmanned aerial vehicle laser point cloud rapid classification. The method comprises the steps of obtaining laser point cloud data collected by an unmanned aerial vehicle platform; determining a local sea surface elevation reference based on the laser point cloud data; based on the local sea surface elevation reference, ship point cloud is identified through relative height screening and spatial clustering, and geometric feature verification is carried out on a clustering result; after sea water points and ship point clouds are excluded from the laser point cloud data, spatial clustering is carried out on the remaining point clouds to identify sea freezing point clouds; and based on the local sea surface elevation datum and the sea ice point cloud, extracting the sea ice thickness. According to the method, the advantages of high spatial resolution and high cost performance of the unmanned aerial vehicle are utilized, a refined point cloud processing technology is matched, classification is rapid and accurate, thickness extraction precision is high, the method adapts to a polar region complex observation environment, reliable data support can be provided for polar region navigation channel development, navigation safety investigation and climate change research in real time, and the method has remarkable practical value.

This invention belongs to the field of marine photovoltaic anti-icing technology, and provides a detachable anti-icing structure and system for pile-based marine photovoltaic systems. It solves the technical problems existing in current anti-icing cone structures. The technical solution includes two semi-circular hoop assembly structures connected to the pile foundation by clamps. Each semi-circular hoop assembly structure includes an upper semi-circular hoop, a middle semi-circular hoop, and a lower semi-circular hoop. The upper, middle, and lower semi-circular hoops of each assembly structure are sequentially connected from top to bottom and fixed to the pile foundation. Each layer of semi-circular hoop is circumferentially spaced with several support plates. One end of each support plate is fixed to the surface of the semi-circular hoop, and the other end is fixed to an anti-icing vertical plate. All anti-icing vertical plates are fixedly connected by anti-icing horizontal plates. It utilizes the thickness and cross-section of the anti-icing vertical plates to contact the incoming ice, generating contact pressure through a small contact area that exceeds the compressive strength of the sea ice, crushing the sea ice at the contact point and causing brittle splitting.

The invention discloses a sea ice coverage on-satellite detection and updating method and device for a synthetic aperture radar (SAR) image, and belongs to the technical field of satellite remote sensing marine monitoring, and the method comprises the steps: carrying out the preprocessing of a dual-polarization SAR image on the ground, training a super-resolution model, and carrying out the knowledge distillation and lightweight processing, and deploying the super-resolution model to a spaceborne computer. After a satellite obtains a real-time SAR image in an on-orbit mode, preprocessing can be completed on the satellite, and a sea ice coverage binary image is directly output. In order to adapt to different satellite wave band data, the ground only needs to finely adjust part of parameters of the model and upload and update the parameters in a differential packet form, so that the whole packet is prevented from being retransmitted. According to the method, a closed loop of ground pre-training-on-satellite reasoning-differential updating is realized, the method has the characteristics of fast response, light weight, easy expansion and the like, and the sailing safety of the arctic navigation channel can be remarkably improved.

The application discloses a sea ice density inversion method and system based on physical information guidance and a medium. The method comprises the following steps: obtaining a corresponding spatial sea ice density map by using a pre-trained sea ice density inversion model; performing feature extraction of microwave scattering maps, microwave radiation maps and meteorological element maps in multiple scales; for each scale of microwave scattering features, microwave radiation features and meteorological element features, guiding microwave radiation feature correction by using meteorological element features and microwave scattering features to realize physical information guided cross-modal feature fusion; extracting spatial information maps and decoding the fused latent feature maps step by step to obtain corresponding spatial sea ice density maps. The application aims to fully model the physical dependence relationship among radiation, scattering and environmental driving factors, realize the spatio-temporal collaboration among features, improve the ability of learning the continuity and structural properties of sea ice distribution and the physical consistency and spatial generalization ability of sea ice density inversion results.

The present application relates to a kind of sea ice surface snow depth combined estimation method, based on the characteristics that microwave radiometer brightness temperature decreases with the increase of sea ice surface snow depth, FY brightness temperature data is used to calculate the brightness temperature of open water at different frequencies, then the brightness temperature of sea ice surface of 10 sub-channels and 20 polarization gradient rate characteristics are calculated using open water brightness temperature and FY brightness temperature data.The OIB airborne data is gridded, and the daily average OIB snow depth of 12.5 km resolution is obtained.The correlation between 30 brightness temperature characteristics and OIB snow depth is calculated, and the linear regression model and machine learning model of one-year ice and multi-year ice surface snow depth estimation are established by selecting 5 sub-features with high correlation, thereby developing the combined estimation model of snow depth.The above is the sea ice surface snow depth combined estimation method proposed by the present application, which improves the estimation accuracy of polar sea ice surface snow depth.

The application provides a UAV and unmanned ship cooperative operation system and method, and relates to the technical field of UAV and unmanned ship cooperative operation.The system comprises a UAV, an unmanned ship and a ground station;the UAV comprises blades for providing power for the UAV, which are fixed on the UAV arms through screws;the UAV is provided with a wireless communication module;the UAV is provided with a depth vision camera for collecting obstacle information in a scene;the unmanned ship comprises a wireless communication module;the unmanned ship is provided with a propeller for providing power for the unmanned ship;the ground station comprises a wireless communication module;and the ground station is provided with a display screen module for displaying a digital map transmitted by the UAV.Through the cooperative control of the UAV, the unmanned ship and the ground station, and in combination with deep learning and intelligent algorithms, the application realizes efficient cooperative operation of polar sea ice monitoring and unmanned platforms.

The invention discloses a sea ice concentration identification method based on a DeepLab v3 + model vision technology, and relates to the technical field of ship navigation, and the method comprises the steps: S1, obtaining video data of a sea area around a ship in real time, processing the video data into an original frame, and preprocessing the original frame to obtain a to-be-identified frame; s2, inputting the to-be-identified frame into the DeepLab v3 + network to obtain the sea ice concentration C; compared with the prior art, the method is high in recognition precision and short in training period.

The application discloses a sea ice observation arm device, which comprises an observation arm structure, a joint structure and a counterweight structure. The observation arm structure comprises a main arm and a cantilever, the front end of the main arm is hingedly provided with the cantilever, the cantilever and the main arm have a folding and coinciding storage state and a controllable angle suspension state; the joint structure comprises a base joint and a cantilever joint, the base joint is arranged between the main arm and a base part and is used for controlling the angle of the main arm lifting, the cantilever joint is arranged at the hinged part of the main arm and the cantilever and is used for controlling the folding storage state or the controllable angle suspension state of the main arm and the cantilever; the counterweight structure is provided with an extension arm at the end of the main arm away from the cantilever, and the extension arm is provided with a counterweight mechanism. The sea ice observation arm device can meet the safe storage and fixation of the observation arm of a survey ship under various conditions. Meanwhile, the observation arm structure is compact, simple to operate and convenient to maintain.

The application discloses a kind of multi-bubble ice breaking experimental device and experimental method under simulated polar environment, it is related to bubble ice breaking technical field, lack of multi-bubble coupling effect on ice breaking effect research problem is solved.The natural sea ice preparation device is set in controllable refrigeration laboratory;The vacuum controllable low-temperature freezing device of natural sea ice preparation device is set on the upper end of transparent vacuum wall surface freezing box;Vacuum controllable low-temperature freezing device includes rapid controllable freezing plate;The box body bottom of transparent vacuum wall surface freezing box is provided with spark device, the box body left plate, box body right plate and box body back plate of box body are all provided with rapid controllable freezing plate, cooling is generated ice layer to box body by rapid controllable freezing plate, and bubble ice breaking is generated by spark device.The present application realizes ice breaking by the bending moment superposition effect under the coupling effect of multiple bubbles, so as to explore the load characteristics of bending moment superposition under the coupling effect of multiple bubbles, and reveal the action mechanism of bending moment superposition under the coupling effect of multiple bubbles.

This invention belongs to the field of marine remote sensing information processing technology and discloses a sea ice thickness inversion method based on multi-source heterogeneous fusion and physical constraints. The invention acquires multi-source heterogeneous GNSS-R data and performs hierarchical grouping and quality control; performs systematic bias and geometric correction on the data to generate a spatiotemporally continuous normalized reflectivity field; constructs a three-layer coherent reflectivity physical model of air-sea ice-seawater; constructs a two-stream physical sensing neural network, embedding the physical model as a differentiable constraint module into the network loss function, and uses the reflectivity field as input to invert sea ice thickness. This invention establishes a refined quality control system for the characteristics of semi-enclosed sea areas, overcoming the limitations of long revisit cycles and low coverage of single satellites. This invention also constructs a high spatiotemporal resolution sea ice monitoring system, solving the problem of instability in inversion of pure data-driven models in thin ice areas, and significantly improving the physical robustness of sea ice thickness inversion.

This invention discloses a method for rapidly extracting and classifying interglacial lakes in the Antarctic Cosmonaut Sea. The steps include: quality control of sea ice concentration data, identification and extraction of interglacial lakes, classification of interglacial lakes, and data storage. This method improves the speed and accuracy of extracting spatial distribution, spatial morphology, and area parameters of interglacial lakes. It enables the extraction of interglacial lakes by category, facilitating research on the formation mechanisms of different types of interglacial lakes. It also improves the degree of automated data processing, reduces manual intervention and workload, standardizes data processing criteria, improves data quality, and obtains long-term data sequences. Furthermore, it helps researchers to promptly grasp the near-real-time status of interglacial lakes during the sea ice growth season (May-August) in the Antarctic Cosmonaut Sea, enhancing understanding of the formation and dissipation processes of interglacial lakes, further strengthening scientific understanding of the Cosmonaut Sea, providing data support for future scientific expeditions, and serving my country's national strategic interests in Antarctica.

This invention discloses an adaptive adjustment method for observing solar irradiance within ice, which measures the initial thickness of the high scattering layer and sea ice layer; sets 5 observation layers within the high scattering layer, and uniformly sets 10 observation layers vertically within the sea ice layer and subglacial water; sets the corresponding observation layer adjustment period to N days, and measures the irradiance spectral intensity set {F} within the t-th observation layer adjustment period. t}; Based on the irradiance spectral intensity set {F t The invention updates the position of the first observation layer within the high-scattering layer and calculates the new thickness of the high-scattering layer. Based on the calculated new thickness, the first observation layer is reset, and then the process awaits the next observation layer adjustment cycle. This invention can track the changes in the thickness of the high-scattering layer caused by seasonal sea ice variations in real time, dynamically adjust the position of the observation layer within the high-scattering layer, and ensure sufficient measurement data within the high-scattering layer, thereby accurately obtaining the optical parameters of each layer within the ice. This provides strong technical support for researchers to conduct long-term in-situ observations of the polar environment.

The application discloses a wide-area sea ice dynamic damage model generation method, and belongs to the technical field of polar engineering, which comprises the following steps: determining the geometric size and physical parameters of the sea ice area; establishing a fracture area calculation model; establishing a sea ice non-fracture area calculation model; step-by-step solving to determine the time step of different calculation domains; iterative calculation, interactive transmission of physical information of different calculation domains; judging the contact between sea ice and structures; calculating ice force pulsation, sea ice deformation and damage field; judging whether the crack extends to the fracture area; judging whether the time iteration step reaches the maximum increment step, and outputting the calculation after the maximum increment step is reached. The application adopts the above method, uses the hybrid algorithm of OSBPD-NLFEM to construct a wide-area sea ice environment field, uses the OSBPD method suitable for damage problems to simulate the crack initiation and expansion of the ice body by presetting the sea ice fracture area, and greatly reduces the time consumption of the calculation.

A method for preparing an ice ridge model, comprising: placing a first ice layer and a second ice layer at intervals on the water surface of an ice water pool, the first ice layer being fixed relative to the ice water pool; moving the second ice layer so that the end of the second ice layer interacts with an ice blocking structure located in an ice crushing area to form an ice crushing body, with upper part of the ice crushing body being flipped over from the upper surface of the ice blocking structure and lower part of the ice crushing body being flipped over from the lower surface of the ice blocking structure; moving the ice blocking structure away from the first ice layer step by step, and repeating the formation of the ice crushing body after each movement of the ice blocking structure; when the total displacement of the ice blocking structure after multiple movements reaches a set displacement, stopping the movement and leaving the ice crushing area, and the ice crushing body is re-frozen to form an ice ridge model connected to the first ice layer and the second ice layer at the same time. The ice ridge model is prepared by simulating the actual ice ridge formation process, so that the physical property parameters and mechanical property parameters of the ice ridge model are closer to the real polar sea ice, and more accurate sea ice environment data is provided.

This disclosure provides a sea ice classification method, relating to the field of artificial intelligence technology, applicable to scenarios such as radar image processing. The specific implementation scheme includes: determining the dual-polarization characteristics of a target sea area; using the dual-polarization characteristics of the target sea area to characterize the multidimensional features of the dual-polarization synthetic aperture radar data of the target sea area; generating simulated full-polarization characteristics of the target sea area using a feature enhancement model based on the dual-polarization characteristics of the target sea area; and generating sea ice classification results for the target sea area using a sea ice classification model based on the simulated full-polarization characteristics of the target sea area; wherein the feature enhancement model and the sea ice classification model are two neural network models obtained through joint training. This disclosure also provides a model training method, an electronic device, and a storage medium.

The invention discloses a railroad bed capable of being frozen on the sea level, and relates to the technical field of ocean engineering and railway transportation. The railway roadbed comprises a roadbed body, the roadbed body is of a hollow box structure capable of floating on the water surface, and steel rails are laid on the top face of the roadbed body. A reinforcing support structure is arranged below the steel rails in the roadbed main body, comprises a truss bearing wall and a steel rail bearing platform, and is used for supporting a load; lateral buoyancy supporting assemblies are arranged on the two sides of the roadbed body and used for providing auxiliary buoyancy and balance. The floating characteristic of the hollow box body is utilized, the hollow box body is laid on the sea surface before freezing in winter, the hollow box body and an ice layer are frozen into a whole after seawater is frozen, and train passing is achieved by directly utilizing the bearing capacity of the ice layer. The roadbed has the advantages of high structural strength, good transverse stability and the like, and can be disassembled and dragged away after sea ice melts in summer, so that the smoothness of a channel is ensured, the seasonal limitation is effectively broken through, and the cross-sea transportation cost is reduced.

The application discloses a sea ice image recognition analysis method and system based on YOLOv8, the method comprises the following steps: acquiring sea ice image or video stream data and carrying out pretreatment, the pretreatment comprises geometric correction and image enhancement; based on the pre-trained YOLOv8 model, the sea ice region of the pretreated sea ice image data is preliminarily detected, and the boundary box containing the sea ice is output; the region of interest is extracted based on the boundary box, the fusion algorithm of watershed segmentation combined with random forest classification is applied, and the pixel-level sea ice mask is generated; the pixel-level sea ice mask is post-processed, and the post-processing includes morphological operation and connected region analysis; according to the post-processed pixel-level sea ice mask, the sea ice coverage ratio is calculated, and the calculation result of the sea ice coverage ratio is dynamically displayed at the predetermined position of the user interface. The application realizes the full-process automation from image input to risk output, without the need for personnel to continuously monitor and prevent, thereby reducing the labor intensity and human error rate.

The invention discloses a sea ice concentration inversion method and system based on physical information guidance, and a medium. The method comprises the following steps: obtaining a corresponding space sea ice concentration graph by using a pre-trained sea ice concentration inversion model; carrying out multi-scale feature extraction on a microwave scattering graph, a microwave radiation graph and a meteorological element graph; aiming at the microwave scattering characteristic, the microwave radiation characteristic and the meteorological element characteristic of each scale, guiding microwave radiation characteristic correction by utilizing the meteorological element characteristic and the microwave scattering characteristic so as to realize cross-modal characteristic fusion guided by physical information; and extracting a spatial information graph and carrying out step-by-step decoding on the spatial information graph and the fused potential feature graph to obtain a corresponding spatial sea ice concentration graph. The method aims at fully modeling the physical dependency relationship among radiation, scattering and environment driving factors, realizing space-time cooperation among features, and improving the ability of learning sea ice distribution continuity and structural properties and the physical consistency and space generalization ability of sea ice concentration inversion results.

The invention discloses a sea ice edge determination method based on a remote sensing satellite. A first image corresponding to a remote sensing image and an actual sea ice edge image can be acquired to serve as training samples, then a horizontal gradient map and a vertical gradient map corresponding to the first image are determined, and then through the first image, the horizontal gradient map and the vertical gradient map, based on a first generative network, a sea ice edge image is obtained. A first sea ice edge prediction image corresponding to the first image is generated, and then a first discrimination result corresponding to the actual sea ice edge image and the first sea ice edge prediction image can be determined through a first discrimination network. Then, training the first generative network and the first discriminant network through a training mode of a cyclic generative adversarial network; and through the trained first generation network, determining a second sea ice edge prediction image corresponding to the to-be-identified remote sensing image, thereby realizing sea ice edge prediction, and improving accuracy of sea ice edge identification.

The invention provides a ship target fusion detection method in a polar region channel based on SAR and optical images, and belongs to the field of remote sensing image processing and target detection. In order to solve the problems of difficulty in moving target association and high sea ice false alarm rate in different-time-phase images of the arctic ice region, the technical scheme comprises the following steps: respectively carrying out target initial detection on SAR and optical images, and extracting features to remove sea ice false alarms; secondly, saliency detection is carried out on the image to extract a channel area, and target parameters such as the target main axis angle, the position and the intersection-to-union ratio are calculated; meanwhile, the motion state is judged according to the target parameter difference, and the maximum correlation area is determined by combining the maximum speed constraint; and finally, target matching is completed in the region, and decision-level fusion is carried out by adopting a D-S evidence theory. According to the method, accurate correlation and fusion detection of the SAR and the time-shifting ship target in the optical image are realized, background interference is effectively overcome by comprehensively utilizing bimodal information, and the method has important application value in the fields of polar region navigation safety monitoring and the like.

The invention relates to an image and signal fused high-precision sea ice classification method, which comprises the following steps of: acquiring original echo signal data of polar sea ice by using a dual-band SAR (Synthetic Aperture Radar) system, and generating an SAR image of each band; respectively analyzing amplitude information and phase information corresponding to at least one polarization mode under double frequency bands from the original echo signal data, and calculating multi-order statistical moments of the amplitude and the phase under each wave band and each polarization mode based on the amplitude information and the phase information; constructing a sea ice classification sample library; training the sea ice classification sample library by using the neural network model to obtain a trained sea ice classification model; and classifying the polar region sea ice data to be classified by using the trained sea ice classification model. According to the method, the SAR image and the original echo signal data are fused, surface layer and internal scattering feature information of the sea ice can be covered at the same time, the limitation that sea ice features provided by a single data source are limited is effectively solved, and therefore the sea ice classification accuracy is remarkably improved.

The present application relates to the technical field of weather model optimization and polar ocean data processing, and particularly relates to a method and system for optimizing COARE model based on sea ice density, comprising: obtaining polar ocean historical measured data, calculating real roughness and model roughness output by COARE model and determining roughness difference value; dividing wind speed interval, fitting and establishing correction function of sea ice density and roughness difference value; introducing sea ice density to COARE model, determining corresponding interval according to wind speed and calling corresponding function to correct model roughness, and outputting ocean-atmosphere flux meeting polar sea ice condition to complete model optimization. The present application realizes dynamic correction of roughness of COARE model under polar sea ice covering condition by introducing sea ice density and establishing wind speed partition correction relationship, breaks through the limitation of original model in wind speed applicability and polar adaptability, and significantly improves the accuracy and practicability of polar ocean-atmosphere flux calculation.