1142 results about "Satellite image" patented technology

The invention discloses a battlefield target behavior prediction method capable of being guided by micro-physical representation and a thinking chain, and the method comprises the steps: obtaining satellite images, radar / communication detection and open source text multi-source time sequence data, completing the entity recognition, relation extraction and event detection, and constructing a dynamic space-time knowledge graph; the maneuverability, sensor detection, weapon range and terrain accessibility mechanism are micronized to serve as a physical consistency constraint embedded prediction model; generating an intention-action-result causal priori chain and parameterizing the causal priori chain into a computable structure; performing multi-branch long-time-sequence situation deduction, and outputting a future target behavior track and a scene probability; and evaluating and explaining by integrating the causal confidence coefficient, the physical consistency and the data goodness of fit, and giving a key event probability and situation evolution report. According to the method, unified modeling of semantic causal and physical constraints is realized, and the method has explainable, verifiable and robust prediction capabilities, and is suitable for target behavior prediction and command information system decision support in a complex environment.

The invention discloses a building health state detection system, and relates to the technical field of photogrammetry and image processing, the system firstly obtains four-dimensional space-time coordinate data of a plurality of monitoring points on a target building based on satellite image analysis, constructs a four-dimensional space-time network model containing spatial topology and time sequence evolution information according to the four-dimensional space-time coordinate data, and then obtains a four-dimensional space-time network model containing space topology and time sequence evolution information; the system maps network model nodes to a predefined standard reference network associated with a local gravity direction, then, the system analyzes dynamic relevance in the four-dimensional network model by adopting a graph self-attention network, and meanwhile, calculates relative relation changes of monitoring points relative to the predefined standard reference network through geometric analysis; the deformation interpretation module distinguishes and determines internal relative deformation, overall displacement and absolute posture change (including settlement and inclination) of the building according to the two types of analysis results and by referring to surrounding environment information, and the state evaluation module integrates the information to generate a health state report containing risk levels.

The invention discloses a semi-supervised multi-temporal satellite image time-varying information extraction method, and belongs to the technical field of remote sensing image processing. The semantic change detection performance of the model and the detection precision of complex shape change ground objects are improved. The method comprises the following steps: constructing a semantic change detection model, carrying out full-supervised training on the semantic change detection model by using a binary change detection supervised loss function and a semantic segmentation supervised loss function by using a small amount of labeled dual-temporal remote sensing images to obtain an initial model, and obtaining a semantic change detection prediction result of each pair of images; using a pseudo label optimization strategy to optimize the semantic change detection prediction result of each pair of images; combining the obtained pseudo label data with high confidence and a small amount of labeled dual-temporal remote sensing images into a new training set, using the new training set to perform semi-supervised training on the initial model in a semantic change detection model, and using a consistency regularization combination loss function to perform supervised training to obtain a new model; and until a preset number of iterations is reached.

The invention relates to the technical field of space governance, and discloses a multi-agent space collaborative governance method which comprises the following steps: processing multi-source heterogeneous data such as satellite images and sensor readings, establishing cross-type semantic association through a geographic space data embedding technology, generating a unified structured text after optimizing the satellite images through vLLM, and synchronizing the unified structured text to a central database; an agent role portrait is dynamically generated by the central server large language model based on a preset Prompt template, and generation does not depend on a fixed rule; then, selecting a target node in the edge-center architecture, disassembling a total task into sub-tasks, establishing semantic mapping, calculating a matching probability, and performing optimal distribution by a reward borrowing function; generating a governance scheme in a perception layer-decision layer-execution layer framework, and outputting a coded operation instruction; based on an execution feedback updating strategy, a multi-level mechanism is set, roles are automatically redistributed, and the governance continuity is guaranteed. According to the invention, the overall efficiency and reliability of space governance can be improved in the face of dynamic scenes or emergency situations.

The invention discloses an intelligent geographic surveying and mapping data processing analysis method and system, and the method comprises the steps: obtaining satellite image data, LiDAR point cloud data and unmanned aerial vehicle aerial photography data, obtaining multi-source heterogeneous surveying and mapping data, building a DC-CNN dual-channel convolutional neural network model, adding a cross-channel feature fusion module on the basis of a model spectrum channel and a geometric channel, and carrying out the multi-source heterogeneous surveying and mapping data fusion. And utilizing a convolutional layer in a depth separable convolution substitution model, inputting the fused multi-source heterogeneous surveying and mapping data into the target DC-CNN network model for identification, and performing association modeling on the surveying and mapping feature data, topographic parameters and environmental data to obtain a multi-scale geographic knowledge map. The extraction efficiency of the model on key features is improved, so that the model has better real-time performance and expandability while keeping high precision.

The invention discloses a method for evaluating the influence of black land ecological barrier pattern evolution on agricultural climate and yield, and relates to the technical field of agricultural ecological environment monitoring and remote sensing model coupling. Comprising the following steps: S1, acquiring a high-resolution satellite image, microwave remote sensing earth surface humidity data, optical remote sensing vegetation coverage data and thermal infrared remote sensing earth surface temperature data of a target area; according to the method for evaluating the influence of the black land ecological barrier pattern evolution on the agricultural climate and the yield, the problem of lack of ecological-climate-agricultural system collaborative modeling in the traditional technology is solved. A high-resolution satellite image is used for analyzing a network topology structure of the protection forest, microwave and thermal infrared remote sensing are fused to invert'water, soil and gas generation 'multi-element parameters, and dynamic quantitative evaluation of ecological barrier spatial heterogeneity evolution on local climate and crop growth is realized in combination with a dynamic weight distribution and two-way nesting technology.

The invention discloses a lightweight-based satellite landslide image intelligent detection method, device and equipment and a medium, and relates to the technical field of disaster detection, and the method comprises the steps: obtaining a whole-scene optical satellite image containing a landslide and a non-landslide region and landform auxiliary data; a dynamic segmentation strategy is adopted to carry out differential segmentation and standardized preprocessing on an image based on topographic data, and a standardized image is obtained. A target landslide image is screened through a double-layer machine learning model, the target image is input into an improved lightweight convolutional neural network for processing, an initial detection result is obtained, finally edge optimization and coordinate calibration are performed on the result, and an accurate landslide area detection result is output. The identification precision of the landslide image is effectively improved through dynamic segmentation and double-layer screening, the improved lightweight convolutional neural network realizes efficient detection in a low-resource environment, and the accuracy of a landslide detection result is further improved.

The invention provides a desertification monitoring grading and vegetation extraction method and system, and relates to the field of desertification remote sensing monitoring and ecological assessment, and the method comprises the steps: selecting a multi-temporal satellite image and an unmanned aerial vehicle image which cover a full research region according to a preset condition, and carrying out the data preprocessing of the multi-temporal satellite image and the unmanned aerial vehicle image; the method comprises the following steps: constructing a desertification difference index by fitting a feature space of a vegetation index and a surface albedo by using a multi-temporal satellite image, grading the desertification degree of a research area to obtain a grading result, and locking a key monitoring area in the research area according to the grading result; a vegetation sample image of a key monitoring area is obtained from an unmanned aerial vehicle image, HSL color space conversion and hue optimization processing are carried out on the vegetation sample image, and a normalized vegetation index based on HSL is constructed, so that vegetation information of the key monitoring area is finely extracted, a key desertification disaster area is effectively positioned, and the accuracy of the desertification disaster area is improved. And vegetation information in the region is finely extracted.

The invention provides a high-precision positioning method and system based on multi-source information fusion. The high-precision positioning method comprises the following steps: acquiring the omm positioning information of the pose of an unmanned aerial vehicle in real time based on a visual inertial navigation unit; a global feature extraction algorithm is combined with a similarity algorithm, and a target satellite image matched with the airborne image of the unmanned aerial vehicle is selected from a satellite image library; using a local feature matching algorithm to combine with the geographic coordinates of the target satellite image to obtain GPS predicted positioning coordinates; fusing the odom positioning information and the GPS predicted positioning coordinates by using a global fusion technology to obtain preliminarily fused odom positioning information; according to the method, secondary fusion is carried out on the basis of GPS positioning data obtained by a real GPS sensor in combination with the preliminarily fused odom positioning information to obtain final odom positioning information, and through a double fusion strategy, the advantages of a multi-source positioning technology are fully integrated, and the positioning accuracy is improved.

The invention discloses a phenological self-adaptive corn automatic mapping method, which comprises the following steps of: 1, screening to obtain Sentinel-2 time sequence satellite image data of a target area, preprocessing, and synthesizing a time sequence clear sky image data set with one scene in a half month; 2, filtering the time sequence clear sky image data set to obtain a denoised and smoothed time sequence satellite image data set; step 3, based on peak and valley values of the NDVI index of the filtered image, adaptively acquiring a corn identification key phenological period of the target area; 4, analyzing typical spectrum differences of the corn and other main background ground objects, providing a corrected red edge normalized vegetation index, and forming a typical index data set of the corn identification key phenological period with an NDVI index and an LSWI index; and step 5, automatically realizing rapid mapping of the corn in the target area by utilizing the division rule of the typical index data of the key phenological period of the corn and the main background ground feature, and realizing a corn planting distribution monitoring effect with higher precision and efficiency.

The invention discloses a self-adaptive 4D Gaussian splashing high-precision three-dimensional reconstruction system and method, and belongs to the technical field of computer graphic processing. The invention aims to realize high-precision automatic registration of multi-source heterogeneous data and improve the calculation efficiency. The method comprises the following steps: collecting multi-source heterogeneous data; constructing a multi-modal fusion registration method, which comprises the following steps: combining satellite image data and low-altitude oblique photography data to realize spatial distribution geometric coarse registration, fusing low-altitude laser radar point cloud data and ground acquisition vehicle laser radar point cloud data to realize luminosity fine registration, establishing semantic features to assist registration, and obtaining registered multi-source data; initializing a 4D Gaussian primitive and executing adaptive splashing reconstruction to obtain an optimized 4D Gaussian splashing model; designing a cloud edge cooperative computing architecture oriented to 4D Gaussian splash reconstruction, and performing distributed parallel processing on the obtained optimized 4D Gaussian splash model; and executing quality evaluation and adaptive optimization, and outputting a final adaptive 4D Gaussian splash model.

The invention discloses a fishery sea area monitoring system based on satellite remote sensing, and belongs to the technical field of fishery monitoring, and the system specifically comprises a remote sensing data acquisition module which obtains and preprocesses sea surface temperature, chlorophyll concentration and sea surface height abnormal data; the buoy data acquisition module synchronously acquires temperature-salinity profile data of the Argo buoy array, and the layout density of the Argo buoy array is dynamically adjusted along with historical fish catch and ocean frontal surface intensity; the space-time alignment module divides a time window through an orbital period, corrects space offset in combination with a buoy drift trajectory, sets a time buffer area based on a georotation flow rate and a satellite image resolution, and generates a space-time unified three-dimensional marine environment data set; the fishery suitability calculation module constructs a biophysical coupling model, correlates surface chlorophyll and subsurface thermohaline structures, and calculates the suitability index of each water layer; the fishery division module intelligently defines a core area and an edge area according to the index three-dimensional distribution characteristics; according to the invention, accurate fishery identification based on multi-source data fusion is realized.

The invention relates to the technical field of value accounting, and discloses an ecological system total production value accounting method based on GIS spatial analysis, and the method comprises the steps: carrying out the accounting of regional basic data and satellite image data, and constructing a bidirectional dynamic semantic mapping library corresponding to land change investigation land parcel classification-ecological system type; internet of Things monitoring data and statistical report data are obtained, and a standardized data set is obtained through space-time fusion of spatial interpolation-boundary correction and a sliding window smoothing method; constructing an urban area-typical district double-level accounting index system, and synchronously calculating a real object quantity and a value quantity; a model correction factor is called based on a GIS, indexes are quantified to grid units, a result is obtained through neighborhood analysis, and after parameters are calibrated, a multi-year ecological value trend map is generated in combination with the GIS; and outputting a customized result according to scenes such as ecological compensation, and outputting a final result after the customized result is qualified through three-level quality control verification. According to the method, the requirements of practical affairs such as ecological compensation and EOD projects on accounting precision and practicability can be met.

The invention discloses a farmland precise fertilization control method based on multi-source data fusion and deep learning, and the method comprises the steps: obtaining the multi-dimensional heterogeneous data of a target farmland in real time, including the crop spectrum time series data of a satellite image, the ion concentration data collected by a soil sensor network, and the microenvironment time series monitored by a meteorological station; and constructing a Delaune triangulation network spatial index, and re-sampling satellite image data to be matched with a soil sensor network space. Calculating a time-varying mutual information entropy, extracting related microenvironment parameters as coupling factors, combining the coupling factors with soil basic parameters to form a three-dimensional feature matrix, and converting the three-dimensional feature matrix into a growth period feature vector fused with time-space correlation; the collaborative decision model is input, the main branch predicts the basic fertilization amount, the auxiliary branch detects the ion concentration space mutation area and corrects the fertilization amount, a fertilization control instruction is output, and the fertilization device is controlled to conduct precise fertilization. The problem that fertilization control cannot be accurately and efficiently performed on farmland in the prior art is effectively solved.

The invention discloses an alpine grassland no-tillage restoration suitability evaluation method based on a 3S technology, and particularly relates to the technical field of data processing. Through the combination of multi-temporal satellite images and global positioning system data, the spatial dynamic change of the frozen soil layer of the alpine grassland and the long-term influence of the spatial dynamic change on vegetation growth are accurately obtained, and a frozen soil layer depth and distribution dynamic monitoring model is constructed based on multi-source remote sensing data and historical weather and soil temperature and humidity data. Analyzing the influence of freezing and thawing alternation on plant root growth; establishing a vegetation recovery potential index and a soil moisture supply index by combining the vegetation coverage and the dynamic change of the frozen soil layer, and generating a suitability partition map through a GIS spatial analysis and analytic hierarchy process; through field sampling and long-term vegetation growth monitoring data verification and model optimization, the accuracy of no-tillage restoration suitability evaluation is remarkably improved, and scientific and accurate decision support is provided for ecological restoration of alpine grassland.

The invention discloses a satellite image-based marine ranch culture area distribution information extraction method and system. The method comprises the following steps of S1, making a marine ranch raft type and net cage culture area semantic segmentation data set based on a high-resolution optical satellite image; s2, constructing a bidirectional Fourier convolution long short-term memory network; s3, training a bidirectional Fourier convolution long short-term memory network by adopting the semantic segmentation data set; and S4, carrying out image segmentation on satellite image test data by adopting the trained bidirectional Fourier convolution long-short-term memory network to obtain marine ranch culture area distribution information. Compared with the prior art, the method has the advantages that feature extraction is carried out in the frequency domain and the space domain at the same time, the capturing capacity of the model for marine ranching raft type and cage culture area details and global features is enhanced, and the image segmentation precision and efficiency are remarkably improved.

The invention relates to the technical field of satellite remote sensing application, and discloses a crop classification method based on a multi-source satellite image, and the method comprises the steps: firstly obtaining multi-temporal satellite image data, extracting spectral reflectivity characteristics, generating a matrix, and constructing a classification model set; setting a feature parameter category set, and establishing a feature fusion correlation model based on the obtained texture, vegetation index parameters and spatial resolution data of the historical image; extracting spectrum and texture feature parameters of a crop area in the current image, and generating an optimized feature set in combination with model optimization; updating classification logic based on the optimized feature set and the classification model, generating a target classification scheme and calibrating a spatial relationship; and finally, acquiring crop growth cycle data, and establishing a time sequence feature association rule to optimize a crop type spatial distribution map. According to the method, through multi-source feature fusion and dynamic optimization, the crop classification precision and reliability are improved, and the method is suitable for a precision agricultural management scene.

The invention relates to the technical field of satellite-borne hyperspectral image processing and analysis, and solves the technical problem that the huge spectral and spatial resolution difference between low-resolution hyperspectral data and high-resolution multispectral data cannot be fully considered in the existing method. The forest hyperspectral satellite image-oriented deep and shallow double-branch super-resolution method comprises the steps of constructing a double-branch network architecture, performing feature fusion reconstruction on output features of the double-branch network architecture, and obtaining high-resolution hyperspectral data with low spectral variation characteristics of forest vegetation in spaceborne hyperspectral image data. According to the method, the problem of modal difference between low-resolution hyperspectral data and high-resolution multispectral data is effectively solved by learning on different feature levels and scales, and the model is enabled to pay more attention to low-spectral variation characteristics of different forest vegetation in a satellite image through a plurality of feature attention mechanisms. And the requirements of subsequent fine monitoring tasks of various forest resources can be met.

The invention relates to the field of remote sensing image processing and computer vision, and discloses a satellite image cascade matching method and system based on double-branch context awareness, and the method comprises the following steps: constructing a heterogeneous double-branch feature extraction network, extracting multi-scale detail features through a main feature network, and capturing global semantic information through a context coding network; fusing multi-scale main features and context features to construct a group-related cost body, and adaptively enhancing key region matching response in combination with a channel incentive mechanism; optimizing the cost body by adopting a cross-scale information transfer and multi-dimensional attention fusion strategy, and generating a multi-scale initial disparity map; image edge geometric features and semantic contexts are fused, and high-resolution parallax details are recovered through a multi-stage residual decoder. According to the method, the problem of matching fuzziness of a traditional method in weak texture, repeated structure and parallax abrupt change areas of a satellite image is solved, and the three-dimensional reconstruction precision and the edge detail integrity in a complex scene are remarkably improved.

The invention relates to an urban live-action three-dimensional construction method based on a high-resolution satellite image. The method comprises the following steps: carrying out stereo image pair screening and stereo image pair preprocessing on a high-resolution multi-view satellite image; carrying out adjustment optimization based on a rational function model on the satellite stereo image pair; generating an epipolar ray image based on the optimized rational function model; performing dense matching on the epipolar line image by combining building edge straight line segment constraint and an MGM algorithm to generate a disparity map; generating a three-dimensional point cloud and a three-dimensional model formed by a plurality of stereo image pairs through forward intersection based on the disparity map and the optimization parameters; generating a digital elevation model, a digital surface model and a three-dimensional Mesh model based on the three-dimensional point cloud; and carrying out precision evaluation and integrity evaluation by adopting pixel proportions of plane precision, elevation precision and height difference. According to the method, the urban live-action three-dimensional model and the corresponding digital product can be constructed quickly, efficiently and accurately by using the high-resolution satellite image.

The invention discloses a building information extraction method, device and equipment based on a monocular side-looking satellite image. The method comprises the following steps: performing multi-scale feature extraction and enhancement on an inclined satellite image of a building by using an image encoding-decoding device to obtain multi-scale pixel-level features; gradually fusing the features by utilizing a query Transform module so as to extract attribute information of the building instance; segmenting the building instance by using a multi-layer sensing mechanism, generating key attributes of the building instance, and generating a contour boundary and a category label of the building according to the key attributes of the building instance; height features are extracted from the key attributes of the building instances through the attention mechanism in the instances and the attention mechanism between the instances, and then the height features are utilized to predict the height of the building through an adaptive binning regression strategy. According to the method, high-precision extraction of the contour and the height of the building is realized through an instance-level multi-task learning framework, and an economical and efficient solution is provided for urban three-dimensional modeling.

The invention belongs to the technical field of image data processing, relates to a ground identification generation method and system based on an airport detailed rule AIP map, and aims to solve the problems that an existing identification generation method is incomplete in information, poor in adaptability and insufficient in compliance. The method comprises the following steps: acquiring an ICAO rule vector; constructing a knowledge graph based on identification information in an airport detailed rule AIP graph, and obtaining AIP graph symbol features; performing cross-modal feature alignment on the satellite image and the AIP symbol features, extracting multi-scale features of the satellite image, constructing a three-layer feature fusion network in combination with the feature-enhanced AIP symbol features, and weighting to obtain comprehensive features; and inputting the comprehensive feature and the ICAO rule vector into a condition GAN generator to generate an identification image. According to the method, three types of heterogeneous data of the satellite image, the AIP map and the ICAO rule are deeply fused, the accuracy is higher, and the method can adapt to different standards and environments.

The invention provides a construction site pollution source analysis method and system, and the method comprises the steps: determining the atmosphere pollution source intensity information of each monitoring region of a construction site based on a preset Gaussian diffusion model, environment monitoring data and meteorological data, and carrying out the detection and recognition of a video stream through a pre-trained target detection model, the comprehensive score of the construction site in a preset time period is determined based on a preset multi-dimensional scoring model, the environment monitoring data, the detection and recognition result and the atmospheric pollution source intensity information, and the key pollution information of the construction site is determined based on the atmospheric pollution source intensity information and the satellite image data and the construction progress information of the construction site; and performing pollution cause analysis based on the environmental monitoring data and historical construction data of the construction site, and determining a pollution source analysis result of the construction site based on the comprehensive score, the key pollution information and the pollution cause analysis result. With the adoption of the method, the high-real-time pollution source analysis requirement caused by quick change of the actual site construction condition of the construction site can be met.

The invention provides a sub-mesoscale flow field extraction method and device based on boundary perception and Poisson constraint. The method provided by the invention comprises the following steps: acquiring stationary ocean water color satellite image data; inputting the continuous satellite image into the flow field extraction model to obtain a continuous feature map; a boundary attention module performs feature extraction and enhancement on a boundary structure of the continuous feature map, and a detail enhancement module performs feature extraction and enhancement on detail features in the continuous feature map to obtain an enhanced continuous feature map; a volume construction module generates a multi-scale pyramid based on the enhanced continuous feature map, determines a search radius based on each scale of the multi-scale pyramid, and constructs a related volume; the circulating flow field refining module is used for carrying out circulating flow field refining on the related volume by utilizing a convolution gating circulating unit, and carrying out iterative updating to obtain a primary flow field; and the physical constraint correction module performs physical constraint correction on the initial flow field to obtain a final sea surface flow field.

The invention relates to a high-position mountain collapse dynamic monitoring and early warning method based on InSAR satellite image comparative analysis, and belongs to the technical field of image data processing, and the method comprises the following steps: 1, arranging monitoring equipment at high-position dangerous rock and high-steep mountain cracks, monitoring the high-position dangerous rock and high-steep mountain cracks through the monitoring equipment, and generating a monitoring image; 2, according to a positioning system in the monitoring equipment, sending a monitoring image in the monitoring equipment to an SAR satellite, performing time sequence InSAR monitoring on the accurately positioned mountain high-position dangerous rock and high and steep mountain crack by using the SAR satellite, and feeding back time sequence InSAR monitoring data to a ground center by the SAR satellite; 3, the ground center analyzes mountain deformation conditions of high-position dangerous rocks and high and steep mountain cracks by using an SBAS-InSAR algorithm; 4, early warning analysis is conducted on mountain collapse according to the mountain deformation conditions of all the time sequences; the mountain collapse monitoring and early warning system has the beneficial effect that mountain collapse monitoring and early warning analysis are realized.

The invention discloses a building contour extraction and regularization method, device and equipment based on a satellite image and a medium, and the method comprises the steps: constructing a basic model through a first initial building extraction model based on YOLOv8 and an annotation training data set, constructing a pseudo tag for unannotated training data through a dual-threshold method by employing weak enhancement and the basic model, and extracting a second prediction contour by using a second building extraction model in which feature disturbance is added in the first initial building extraction model, training the building extraction model based on a pseudo tag and the second prediction contour, and extracting a building contour by using the building extraction model, so that the GF-7 building extraction precision can be improved under the condition of annotated data. And efficient and low-cost GF-7 image building extraction is realized. And meanwhile, scale feature fusion optimization and edge features are introduced into the first initial building extraction model based on YOLOv8 for feature enhancement, so that the instance segmentation task precision is effectively improved.

The invention provides a satellite image real-time processing system and method, the system comprises an image preprocessing module and an image processing module, the image preprocessing module is in communication connection with the image processing module, and an AI lightweight model is deployed on the image processing module; the image preprocessing module is used for preprocessing remote sensing image data observed in the flight process of a satellite to obtain target image data; the image processing module is used for inputting the target image data into the AI lightweight model to obtain an image processing result so as to generate an image product according to the image processing result; wherein the image processing result is obtained by executing a target processing task on the target image data by the AI lightweight model. The AI lightweight model is deployed on the satellite image real-time processing system, so that the transmission delay is reduced, the satellite image data processing efficiency is improved, and the real-time scene processing requirement is met.

A high temporal-spatial resolution vegetation index fusion method based on a multi-source optical satellite image comprises the following steps: firstly, performing radiometric calibration, atmospheric correction and geometric fine correction on Landsat, Sentinel-2 and MOD09A1 data, and unifying temporal-spatial resolution to 10m / 8 days; pixel-level fusion is carried out by adopting an improved continuous correction method, a correction coefficient K is introduced to compensate Sentinel-2 critical period data defect influence, and fusion precision is improved through dynamic weight adjustment; and finally, a continuous and smooth EVI time sequence is constructed by using cubic spline interpolation and Savitzky-Golay filtering. According to the method, single-source data space-time limitation is broken through, after fusion, the vegetation index spatial resolution reaches 10 m, the time resolution reaches 8 days, the key phenological period extraction error is smaller than or equal to 3 days, the crop classification precision is larger than or equal to 90%, the accuracy and continuity of farmland-scale vegetation monitoring can be remarkably improved, high-precision data support is provided for agricultural application such as crop growth assessment and water resource management, and the method is suitable for popularization and application. The method is suitable for cloudy and rainy areas and various crop types.

The invention belongs to the technical field of airport light layout, particularly relates to an airport light generation method based on an AIP document and image data, and aims to solve the problems that layout and attribute configuration are wrong and airport light requirements are difficult to meet due to fixed setting and manual planning of a traditional light system. According to the method, the geometric attributes and the spatial relationship of the runway / taxiway are extracted by analyzing the AIP text, and the satellite image and the GIS data are aligned to obtain accurate image data; carrying out standardization, feature matching and fusion superposition on the two types of data to generate a runway and taxiway layout map; according to the layout map and the rule base, grid sampling is combined with self-adaptive processing to obtain airport lamp positions; a cognitive rule and a multi-target model are constructed, target attributes are solved by using an MOPSO algorithm, and actual attributes are controlled in cooperation with an enhanced Hi-IDRL algorithm. According to the invention, layout and attribute dynamic optimization is realized, and diversified light simulation requirements of the airport are met.

The invention discloses a hyperspectral satellite target rock and mineral identification method and system based on spectral characteristic parameters. The method comprises the following steps: S1, collecting and preprocessing a standard rock and mineral reflection spectrum curve; s2, collecting a regional hyperspectral satellite image and carrying out image preprocessing; s3, identifying diagnostic spectral reflection and absorption characteristics of the target rock mine based on the standard curve; s4, screening and extracting a diagnostic spectral range of the hyperspectral satellite image; s5, calculating diagnostic spectral characteristic parameters of the hyperspectral image target rock mine; and S6, on the basis of the S5, processing the spectral characteristic parameters obtained by calculation, and realizing target rock and mine identification based on multi-dimensional diagnosis spectral characteristics. According to the method, the application potential of the hyperspectral satellite image in the field of remote sensing geology is effectively improved and the scientificity and the accuracy of the hyperspectral satellite image in large-range rock and ore recognition drawing application are improved through the target rock and ore standard spectral curve characteristic analysis, hyperspectral satellite image processing and spectral characteristic parameter estimation methods.