133 results about "Visual interpretation" patented technology

The invention relates to the technical field of artificial intelligence and medical diagnosis, in particular to a disease diagnosis prediction method and system based on a graph neural network. The disease diagnosis prediction method based on the graph neural network comprises the five steps of heterogeneous medical knowledge graph construction, adaptive node embedding representation, hierarchical graph attention network modeling, incremental learning dynamic graph updating and multi-dimensional feature input and result output. The invention discloses a disease diagnosis and prediction system based on a graph neural network. The system comprises a multi-source data acquisition module, a heterogeneous graph construction module, a self-adaptive embedding module, a graph network calculation engine, a dynamic updating module, a disease prediction module and a feedback optimization module. According to the method, the multi-modal heterogeneous knowledge graph is constructed to integrate the multi-dimensional data of the patient, and the hierarchical graph attention network and the dynamic incremental learning are combined, so that the accurate prediction of the disease risk and the visual explanation of the pathological association path are realized.

Techniques are disclosed for enabling creators to create multimodal chatbots that are based on or simulate / model characters. The characters may be from audiovisual (AV) media such as films and TV shows or real people. The application leverages a combination of Visual Interpretation AI, Retrieval-Augmented Generation (RAG), Low-Rank Adaptation (LoRA), and function calling to provide rich, interactive experiences. The inferencing performed by an instant multimodal chatbot utilizes base weights, character weights, relationship weights, experience weights as we as environmental inputs. An instant chatbot uses a number of AI models including a video to text model, an image to text model, a sensory to text model, a large language model, a text to video model, a text to image model and a text to voice model. A user can interact with the chatbot in a variety of ways including text, audio and video.

The invention relates to a small sample fault prediction method based on physical information guidance and multi-source adaptive fusion. The method comprises the following steps: collecting real operation data of preprocessing target equipment; according to the physical model or domain knowledge of the target equipment, generating simulation sensor data conforming to a physical rule under various fault modes of different degrees; constructing diversified training samples in combination with real data and simulation data; for different types of sensor data, designing corresponding feature extraction branches, mining potential fault features in the data, and dynamically adjusting the weight of each data source fault feature for fusion based on an output result of a physical model and data-driven feature correlation analysis; inputting the obtained fusion features into a fault prediction model based on a small sample learning framework for training; comprehensively considering the fault prediction result, the real operation data and the analysis result of the physical model, and carrying out quantitative evaluation on the overall health state of the target equipment; and causal diagnosis and visual interpretation are carried out.

The invention discloses a water area and resource quantity monitoring and predicting method and system based on a remote sensing technology, and belongs to the technical field of water resource monitoring. The method comprises the following steps: determining a water area range according to historical data of a research area and an on-site survey condition, and determining a research area range; the method comprises the following steps: respectively acquiring ground data and water depth and water surface elevation data by using an unmanned aerial vehicle and an unmanned ship, respectively processing to obtain a land DEM and a water area DEM, and fusing to form an all-terrain DEM; downloading satellite image data of the research area, and performing geographical registration on the satellite image data; in the geographically registered satellite image data, describing the boundary of the water area through visual interpretation; superposing the water area boundary to an all-terrain DEM, obtaining the elevation of each point on the water area boundary line, calculating the average elevation of the water area boundary, and calculating the water area and the water resource amount to obtain the historical water area and the water resource amount; and on the basis of the obtained historical water area and water resource quantity, utilizing an ARIMA model to predict future water resource quantity and water area.

The invention relates to the technical field of literature measurement, and discloses a literature visual analysis method and system assisted by a large language model, and the method comprises the steps: matching a chart and an introduction document corresponding to the chart in a visual knowledge base according to an input problem; combining the input question and the introduction document to generate a first cue word, and inputting the first cue word into a large language model; the big language model outputs a structured language and a visual image reconstructed by a four-step method; interpreting the visual image by the visual large language model, and outputting visual interpretation data; visual interpretation and question input are combined to generate a third cue word, the third cue word is input into the large language model, and the large language model generates a final visual chart and chart visual interpretation; based on a large language model, by combining a customized knowledge base and a general knowledge base, the visualization diversity is increased, and high professional performance and strong generalization ability of unconventional problems are shown in the field of literature metering; and by adopting abnormal feedback, closed-loop control of the intelligent agent on a visual result is realized, and the reliability is remarkably improved.

The invention provides a forest degeneration process identification and degeneration degree division method based on a time sequence, and relates to the field of forest resource degeneration restoration. The method comprises the following steps: acquiring surface reflectance data, and calculating an NBR index after preprocessing; fitting an NBR time sequence through a LandTrendr algorithm, and extracting sample place time sequence data based on random sampling and visual interpretation; generating an adversarial network and expanding sample point fitting data into a simulation data set in combination with a self-supervised learning technology; training CNN, random forest and BOSSVS time sequence classification models, and constructing a hybrid classification model through an integrated voting strategy; and identifying a forest degeneration process by using a hybrid classification model, and generating a degeneration degree diagram through reclassification. The method can achieve the efficient recognition of the complex degradation process of the forest region, has good time sequence adaptability and regional applicability, and provides technical support for the monitoring and management of ecological restoration of regional forests.

The invention discloses a judgment method suitable for a multi-branch river deformed river bay, and belongs to the field of water conservancy projects. The method comprises the steps of coarse extraction of a multi-branch river water body, depiction of a river multi-branch channel network structure, batch calculation of plane geometric indexes of all branch channels of the river, identification of main flow paths of the multi-branch river, and judgment and output of the multi-branch river abnormal river bay under the variable spatial scale. A traditional deformed river bay quantification and discrimination method is popularized to a multi-branch river with a complex plane form. The judgment method provided by the invention not only embodies the advantages of the remote sensing and image processing technology in the aspects of automation and calculation efficiency, but also considers the calculation precision close to that of visual interpretation, so that a large amount of cost is saved, and meanwhile, calculation errors caused by a data source and subjectivity are also avoided.

The embodiment of the invention discloses a causal inference method for traffic accident data processing and prediction model optimization, and the method comprises the steps: constructing a multi-source heterogeneous accident database, and constructing a standardized feature matrix; based on the standardized feature matrix, extracting a feature constraint rule through a decision tree, and generating a minority class anti-fact sample conforming to domain knowledge in combination with a heuristic algorithm to form an equalized data set; inputting the equalized data set into a double-path model system for training, expanding a feature space by a data path to improve the characterization capability of a minority class, and embedding an interpretability constraint module into an interpretation path to obtain a trained model; based on the trained model, constructing a visual interpretation system comprising a feature importance map and a causal association network; and based on a visual interpretation system and the standardized feature matrix, constructing a multi-level causal network identification core influence factor, and quantifying the causal effect of intervention measures through anti-fact simulation to form a priority intervention list.

The invention discloses an active visual explanation system for blind person field travel, and belongs to the technical field of man-machine interaction. Based on the system, a user can selectively broadcast navigation obstacle avoidance or scenery description, real-time image information collected by a deep vision detection module is recognized through a deep neural network model, speed and type information of objects in front and below is obtained in combination with a positioning system, and when the user selects to broadcast navigation obstacle avoidance, the navigation obstacle avoidance or scenery description is broadcasted. After the audio instruction sequence corresponding to each sub-state is generated according to the shortest state sequence, the user acts under the guidance of the audio instruction, and each action can be fed back by the system, so that the correctness and efficiency of the traveling forward route operation are greatly improved; when the user selects to broadcast the beautiful scenery description, the AI model generates an audio instruction sequence according to the identified object information and informs the user of the audio instruction sequence; not only is the safety of the user during the on-site travel improved, but also the user can be helped to know the scenic spot in which the user participates, and the whole-process visual supervision is provided for the on-site travel of the user.

The invention relates to a dual ERA-LFD kit for visually and rapidly detecting salmonella typhimurium or a single-phase variant thereof, a primer group and application of the dual ERA-LFD kit and the primer group, and aims at solving the problems that an existing detection method is complex in operation, long in time consumption and depends on professional equipment, and primer cross interference, high false positive rate and incapability of distinguishing the single-phase variant caused by the fact that dual ERA primer design is not standard. Screening to obtain a specific primer probe combination aiming at the fljB gene deletion region and the STM4495 gene; a dual ERA-LFD detection system is constructed, and double targets are synchronously amplified under a constant temperature condition; visual interpretation is realized through a double-detection-line color development mode of the transverse flow test strip. The detection specificity can be remarkably improved, and false positive is effectively avoided; the accurate distinguishing between the salmonella typhimurium and the single-phase variants is realized; the detection process is rapid and simple, does not need professional instruments and personnel, and is suitable for on-site rapid screening; the detection cost is obviously reduced and the basic application feasibility is improved.

The invention discloses a medical image segmentation method based on a Kolmogorov-Arnold network, and the method comprises the steps: firstly obtaining a medical image, then constructing an IKANet model, and carrying out the segmentation of the medical image after training, testing and verification. Wherein the IKANet model adopts an encoder-decoder structure and integrates three core components, namely a DKC module, a multi-scale attention module and an interpretability analysis module, and the DKC module realizes adaptive nonlinear transformation and feature decoupling through a Kolmogorov-Arnold network; the multi-scale attention module enhances space-channel feature fusion through multi-scale convolution and a channel attention mechanism; and the interpretability analysis module generates a multi-scale class activation mapping thermodynamic diagram and provides visual interpretation. The technical problems that an existing medical image segmentation method is limited in generalization ability and insufficient in interpretability are solved, and the method can be widely applied to data sets of colon polyp, pneumonia, pneumothorax and the like.

The invention discloses a TBM tunneling geological condition intelligent identification method and system fused with multiple modes. The method and system are used for identifying various geological condition scenes faced by a shield tunneling machine TBM during construction. The method comprises the steps that muck images and parameter information data are collected based on a shield tunneling machine industrial camera system and a sensor network, and data preprocessing operation is carried out; based on the collected data information, an improved Swin Transform model is provided, and multi-modal input of muck images and TBM parameters is fused; an improved multi-modal fusion model Swinin-T-M is integrated on the basis of a Dempster Shafer theory, the limitation and instability of a single model are improved, an integrated multi-modal deep learning model ESwinin-T-M is built, and the recognition accuracy and robustness of an algorithm are improved; and a visual interpretation technology is utilized to analyze the learning rule and key features of the model. According to the method, intelligent and accurate recognition of complex geological conditions in the TBM tunneling working process is achieved, and a feasible scheme is provided for detection of the construction environment of the shield tunneling machine.

The invention discloses an intelligent automatic drawing system and method, and relates to the technical field of agricultural remote sensing monitoring. The method comprises the following steps: acquiring a multi-temporal remote sensing image of a farmland area, performing preprocessing and spatial alignment, dividing the farmland into spatial units, extracting time sequence spectrum and vegetation index characteristics of the spatial units, and forming a visual characteristic vector sequence; analyzing the sequence by using unsupervised clustering, and automatically identifying the time sequence evolution trajectory categories representing different growth states; and finally, the system automatically marks the space units which are judged to be abnormal categories on a map, generates early warning and outputs a farmland abnormity thematic map. The system comprises a data preprocessing module, a data preprocessing module, a track classification module and an abnormal graph generation module. According to the invention, full-automatic and intelligent dynamic monitoring and abnormity identification of the farmland growth state are realized, the limitation of traditional single-temporal analysis or artificial visual interpretation is overcome, and the monitoring efficiency and objectivity are significantly improved.

The invention provides a method and a device for evaluating a credible result of a graph neural network. The method comprises the following steps: constructing an objective function based on a space-time relationship; obtaining an identification result obtained by identifying the skeleton behavior by the to-be-evaluated space-time diagram neural network, and constructing an augmented Lagrange function corresponding to the target function; iteratively calculating a node feature mask matrix and a human skeleton motion rotation model; and taking the node feature mask matrix and the human skeleton motion rotation model as an evaluation result of a prediction result of the space-time diagram neural network. According to the method, the to-be-evaluated space-time diagram neural network is explained and evaluated based on the human skeleton motion rotation model and the node feature mask corresponding to the human body. The interpretation result obtained by the method has high sparsity, and the geometric consistency of the space-time structure is consistent with that of the space-time diagram input into the space-time diagram neural network to be evaluated; the decision result of the space-time diagram network can be quantitatively and visually explained.

The application discloses a remote sensing multi-task interpretation method and device based on cross-knowledge interactive learning and self-supervision auxiliary learning, relates to the technical field of remote sensing visual interpretation, and comprises the following steps: acquiring an image to be processed, setting task requirements of the image to be processed, the task requirements comprising one or more of a single-mode data classification task, a multi-mode data classification task, a single-mode data segmentation task, a multi-mode data segmentation task, a single-mode data change detection task and a multi-mode data change detection task; inputting the image to be processed into a trained multi-task interpretation network, and obtaining a result of the image to be processed after executing the task requirements according to the task requirements of the image to be processed; wherein the trained multi-task interpretation network is trained in a self-supervision learning mode by taking data of preset categories as a training data set, and is obtained by training an initial multi-task interpretation network. The application can realize the interpretation of remote sensing visual single-mode classification, segmentation, change detection and multi-mode classification, segmentation and change detection, and obtain high-precision interpretation results.

The invention provides a CPA-LFD method for detecting Rahnella aquatilis and a detection kit, and belongs to the technical field of microbiological detection. On the basis of a single cross primer isothermal amplification technology (CPA) and in combination with a transverse flow test strip (LFD) prepared by colloidal gold, a real-time detection method for Rahnella aquatilis under the condition of 63 DEG C isothermal amplification is developed. According to the method, a specific primer is designed, and a probe containing Biotin and an FAM label is used for identifying a DNA specific region, so that rahnella aquatica is effectively detected within 1 hour; the optimized Bst DNA polymerase can be directly applied to isothermal amplification reaction of DNA, so that the detection process is simplified, and the time is shortened; the method is simple to operate, does not need to depend on professional detection personnel, has low requirements on instruments, realizes visual interpretation of detection results, has relatively high sensitivity and specificity, can detect Rahnella aquatica with the lower limit of 48fg / mu L, is suitable for rapid detection of Rahnella aquatica in human clinical samples, animals, fishes and food pollution, and can be widely applied to detection of Rahnella aquatica. The method has important significance on monitoring, diagnosis and epidemiological prevention and control of diseases caused by rahnella aquatica.

The system comprises a laminated 5 diagnostic strip including a backing layer (105), an absorbent pad layer (106) configured for controlled capillary transport of milk, and a reagent deposit layer (107) defining multiple spatially separated dry reagent zones. The reagent zones include a pH indicator zone (101), a leukocyte esterase detection zone (102), a hemoglobin detection zone (103), and a nitrite detection zone (104), each adapted 10 for operation in the milk matrix. A calibrated sample application point (108) receives a predefined volume of untreated milk and fluidically communicates the sample to the reagent zones via the absorbent pad layer. Colorimetric reactions develop in a result window area (109), enabling simultaneous visual interpretation without analytical instruments, typically within three minutes. The invention further 15 includes a moisture-controlled storage container comprising desiccant elements (204, 205), a reference color chart (208), and a calibrated pipette (209), ensuring reagent stability and standardized use.

The application discloses a line-shaped slope map spot extraction method and system with a width less than or equal to 1 grid and a storage medium, and relates to the field of land resource management. Firstly, spot points are generated in a slope classification map to obtain all boundary node coordinate information of a polygon, all coordinate point combinations are calculated according to the coordinate information, all points are compared with existing nodes, then rectangular spots with a side length of two grid widths or more are found and marked in the spot area, the above process is repeated until all spots are traversed, finally, spots with qualified rectangles are removed, and the remaining spots are line-shaped slope map spots with a width less than or equal to 1 grid. The application does not need to rely on complex polygon geometry width calculation, avoids large errors and low efficiency problems of manual visual interpretation, provides a reliable basis for subsequent merging of spots according to the average allocation principle, guarantees the quality of slope classification map results, and supports the scientificity and accuracy of natural resource management decision-making.

The invention discloses a multiplex nucleic acid detection kit for swine acute diarrhea pathogens and application of the multiplex nucleic acid detection kit. The kit comprises an RAA (Reverse Amplification) primer pair, a specific crRNA (Ribonucleic Acid), a Cas12a protein, an NEBbuffer r2.1 and an ssDNA (Deoxyribose Nucleic Acid) reporter molecule, wherein the RAA primer pair, the specific crRNA, the Cas12a protein, the NEBbuffer r2.1 and the ssDNA reporter molecule are used for detecting the porcine epidemic diarrhea virus (PEDV), the porcine rotavirus (PoRV) and the porcine sapelovirus (PSV). According to the method, reverse transcription, amplification and Cas12a detection processes are coupled through an integrated system or a continuous reaction mode, the whole process is completed under the condition of constant temperature (37 DEG C), thermal cycle equipment is not needed, and only a constant-temperature heating device is needed. After the reaction is finished, a result can be observed through a portable blue light detector or naked eyes, so that on-site real-time visual interpretation is realized.

The present application belongs to the field of laser processing, and relates to a method for reducing damage to walls based on a penetration model and area light control, comprising the following steps: 1) constructing a penetration model; 2) judging the penetration model constructed in step 1) based on camera visual interpretation technology to obtain a judgment result; 3) capturing images of an actual production process in real time, comparing the images of the actual production process with the judgment result obtained in step 2), and sending a control signal to a light control system according to a comparison result; and 4) adjusting the distribution of light sources in time according to the control signal in step 3) to complete an actual hole forming process. The present application provides a method for reducing damage to walls based on a penetration model and area light control, which can make judgments in time and reduce strict requirements on real-time performance of a system.

The invention discloses an enzymatic colorimetric coding-decoding system for weighted detection of multiple biomarkers and application, and belongs to the field of biomedical detection. According to the enzymatic colorimetric coding-decoding system provided by the invention, the mapping relation between the color and the multi-dimensional biomarker can be realized through direct visual interpretation or simple spectral analysis. The invention also provides an enzymatic colorimetric coding-decoding method for weighted detection of multiple biomarkers, and the method comprises the following steps: converting multi-dimensional biomarker information into quantifiable and visually analyzable optical signals through an integrase catalyzed multicolor reaction, nucleic acid driven signal amplification and optical coding-decoding mechanism; and single-tube multi-dimensional joint detection, high information density output and rapid disease state discrimination are realized. The method is successfully applied to early screening of pancreatic cancer and can be expanded to dynamic monitoring of multiple markers of other cancers and infectious diseases in the future, and an efficient and low-cost solution is provided for precise medical treatment and digital medicine.

A unified framework system and method for a computer implemented artificial intelligent assistant to perform multiple tasks for remote sensing, includes a task input field for receiving a task identity, a global image encoder configured to receive a remote-sensing image and a user query and encode patch-level tokens at a high resolution via interpolation positional encodings, an MLP adapter configured to receive the patch-level tokens and adapt the tokens to language space, and a large language model configured to generate natural language responses interleaved with corresponding object locations based on the language space and task specific prompts. The global image encoder further includes a region input field for receiving region location parameters. The framework system is configured to switch based on the input task identity between different types of remote sensing visual interpretation tasks.

This invention belongs to the field of molecular biology detection technology, specifically relating to a Nipah virus (NiV) nucleic acid detection kit combining RT-LAMP and immunochromatography, and its application. This kit combines the high sensitivity and specificity of loop-mediated isothermal amplification (LAMP) technology with the rapid and visual interpretation advantages of immunochromatographic test strips, enabling on-site, rapid, and accurate detection of NiV. The core components of this invention include: a set of specific primers designed for conserved regions of the NiV gene, an optimized RT-LAMP reaction system, and specially treated immunochromatographic test strips for detecting LAMP amplification products. This kit eliminates the need for reverse transcription of RNA virus nucleic acids and expensive thermal cycling equipment. The entire detection process is completed within 20–30 minutes, with a sensitivity of up to 300 copies / μL. This solution combines the high efficiency of RT-LAMP technology with an intuitive, equipment-free detection method, achieving truly rapid and accurate on-site diagnosis of NiV.

This invention discloses a method for assessing ecosystem service losses due to regional climate change. The region includes counties, cities, or important ecological function zones. The steps are as follows: S1. Collect remote sensing data and preprocess the data through band combination, geometric correction, and projection transformation. Select a standard color composite scheme, establish interpretation markers, and perform visual interpretation. S2. Calculate and statistically analyze the unit area yield value and equivalent factor of grain crops. The equivalent factor is 1 / 7 of the average market value of grain in the current year. S3. Statistically analyze land use types, calculate the magnitude of land use change, reflecting the changes in the total amount of different land use types and the overall trend of land use type changes. S4. Select vegetation cover as an indicator, and adjust the ecosystem service value based on two parameters: net primary productivity and vegetation cover. This invention is of great significance for formulating ecosystem management strategies under the background of future climate change.

The invention discloses a precise recognition method for cultivated land parcels in hilly and mountainous areas based on DeepLabV < 3 + >, and the method comprises the following steps: S1, obtaining a remote sensing image with the spatial resolution of 2m of a Gaofen-6 satellite, and carrying out the data preprocessing; s2, making a sample data set, and obtaining ground object sample label data required by model construction by adopting an artificial visual interpretation technology; dividing a training set and a test set; s3, constructing and training a DeepLabV3 deep learning network model based on a PyTorch framework, and obtaining an optimal model; and S4, inputting the preprocessed remote sensing images of the hilly and mountainous areas, and performing accurate identification on the cultivated land parcels by using the optimal number. The DeepLabV3 + model has remarkable advantages in processing complex terrains and land utilization types in hilly areas, and can effectively solve the problem of broken cultivated land distribution.

The invention relates to the technical field of virus detection, in particular to a primer probe combination, a kit and a method for detecting G3 gene group pig bocavirus based on a constant-temperature RAA fluorescence method in a non-diagnostic purpose mode. Aiming at a conserved region of a PBoV G3 virus genome, a probe which is specially modified and designed is adopted and has a synergistic effect with an upstream primer as shown in SEQ ID NO.4 and a downstream primer as shown in SEQ ID NO.7. The primer probe combination is used for detecting the G3 gene group pig bocavirus by a constant-temperature RAA fluorescence method, the detection sensitivity and specificity can be remarkably improved, viral nucleic acid as low as 10.5 copies / mu L can be detected in an extremely short time, meanwhile, cross reaction with other pig bocavirus gene groups or common pig pathogens is effectively avoided, and the detection sensitivity and specificity are improved. And a dual detection mode of instrument quantification and blue light naked eye interpretation can be supported, so that the detection efficiency and the convenience of field application are effectively improved.

This invention relates to the field of intelligent remote sensing image recognition technology, and discloses a method for identifying ground features in multi-temporal multispectral images based on pixel image fingerprints. The method includes: calculating several feature index bands for the original multi-temporal multispectral images and integrating them into a structured data file; selecting sample points of various target ground features based on high-resolution reference images and visual interpretation to construct a training sample set; inputting the data into a deep autoencoder network for training, using the trained autoencoder for feature learning, generating a standard pixel image fingerprint database, and setting a high-confidence recognition threshold; traversing the structured data file to be identified, using the trained autoencoder for matching and recognition, and generating target ground feature recognition results; and outputting the target ground feature recognition product through reclassification mapping rules and configuration of visualization colors. This invention effectively solves the problem of identifying "different objects with the same spectrum" caused by the limited number of bands and insufficient spectral separability in multispectral remote sensing images.

The invention discloses a medical image diagnosis method, device and system and a storage medium, and the method comprises the steps: carrying out the preliminary diagnosis of a medical image through a medical context big language model, and generating a medical report; quantifying the credibility of each sentence in the medical report by using an uncertainty measurement algorithm to obtain a credibility score; dynamically marking report contents by using color gradient mapping, and highlighting high-risk diagnosis entries; generating a structured visual thermodynamic diagram for each sentence of report by utilizing an explainable method based on explicit attribution and an attention mechanism; a React-based front-end interaction framework is utilized to integrate visual interpretation and report editing functions, explainable diagnosis decision basis and risk entry early warning are provided, and doctors are assisted in visual and rapid targeted verification; a continuous learning framework is adopted, feedback of doctors is collected, and sentence-level correction and a flexible training strategy are introduced to continuously optimize diagnosis precision. According to the invention, the interpretability and continuous learning ability of medical image diagnosis are improved.

The present application relates to the field of railway engineering, and relates to a kind of extraction method of rock slope based on hyperspectral remote sensing image, the method includes obtaining hyperspectral remote sensing image;Linear stretching treatment is carried out based on hyperspectral remote sensing image, obtain first spectral image;Based on first spectral image, carry out hue analysis processing, obtain first classification chart, first classification chart includes the area meeting the hue feature of rock slope;Band analysis is used to process hyperspectral remote sensing image, obtain second spectral image;Based on first spectral image and second spectral image, processing is carried out, obtain second classification chart, second classification chart includes the area meeting the lithology brightness of rock slope;Hyperspectral remote sensing image is processed, obtain third classification chart;First classification chart, second classification chart and third classification chart are processed, obtain the range of rock slope, the present application solves the problem that traditional rock slope extraction method relies on artificial visual interpretation, low efficiency, realizes the accuracy of extracting rock slope range.

The invention belongs to the technical field of image processing, and discloses a full-polarization SAR image sea surface oil spill extraction method based on an active contour. The method comprises the following steps: S1, obtaining four-channel data of a complete polarization SAR image, wherein the four-channel data covers polarization characteristic difference of oil spill; s2, selecting a closed curve surrounding a suspected oil spill area through visual interpretation based on fusion features of the complete polarization image, and optimizing a contour line through smooth and uniform sampling; s3, maintaining the continuity and expansion characteristics of the contour line by constructing an internal energy function; s4, iteratively adjusting the positions of the contour points according to the minimum energy principle, and judging the termination opportunity by combining the contour stability; s5, filling holes in the area through morphological operation, removing edge small bulges, and optimizing the area form; and S6, verifying the extraction precision through field observation, and judging whether a result meets an application standard or not. According to the invention, accurate segmentation of the oil spill area is realized through the multi-energy item active contour, and the method can be applied to marine environment monitoring and marine oil spill emergency response scenes.