527 results about "Vegetation Index" patented technology

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 crop planting area intelligent extraction method and system based on multispectral remote sensing, and relates to the field of remote sensing image processing, and the method comprises the steps: obtaining and correcting a multiband remote sensing image; calculating a vegetation index and constructing a crop growth characterization index to extract canopy features; obtaining a multi-period feature map, calculating a spatial distribution entropy, and establishing an evaluation function to determine a time sequence fusion weight; a spatial constraint function is established based on the spectral distance, and a segmentation criterion is constructed by combining the spatial constraint function with time sequence features for classification iteration. According to the method, the accuracy and the discrimination degree of crop planting area extraction are improved, and crop identification requirements in a complex agricultural environment are met.

The invention relates to the technical field of image recognition, and discloses a multispectral image and deep learning-based forest disease and insect pest automatic recognition method, which comprises the following steps of 1, carrying a multispectral camera containing a red edge wave band through an unmanned aerial vehicle to obtain a forest region image; 2, calculating a red edge normalized vegetation index of the image; 3, performing time sequence modeling on the red edge normalized vegetation index data of more than five consecutive periods, and inputting a time sequence convolutional network to generate an early lesion probability graph; 4, detecting a pest and disease damage target by adopting a multi-scale adaptive feature pyramid network; 5, outputting a disease and pest distribution thermodynamic diagram; and 6, driving the unmanned aerial vehicle cluster to execute precise pesticide spraying. According to the method, through the high sensitivity of the red-edge wave band to chlorophyll degradation and in combination with sequential convolutional network dynamic modeling, an initial lesion area can be recognized 7-10 days before disease development, the early disease recognition capability is remarkably improved, the disease discovery period is shortened, and large-scale disease diffusion is effectively avoided.

The invention discloses a soil water content inversion method and system based on L-band dual-polarization data and a medium, and belongs to the field of remote sensing technology and environment monitoring. The system comprises an SAR data acquisition module, an optical data processing module, a vegetation scattering separation module, an inversion model construction module, a spatial distribution calculation module and a risk assessment module, wherein the SAR data acquisition module is used for regularly acquiring L-band dual-polarized SAR data of a target area; the optical data processing module assists in identifying and separating vegetation scattering components by calculating vegetation indexes; the vegetation scattering separation module is used for extracting pure soil back scattering signals; the inversion model building module is used for training and optimizing a soil water content inversion model based on the pure soil back scattering signals and field sampling data; the spatial distribution calculation module is used for calculating a spatial distribution diagram of the surface soil water content of the target area; and the risk assessment module is used for carrying out landslide risk grade division and early warning management.

The invention discloses an inland river basin intelligent ecological scheduling rehearsal method fusing a scheduling process and an ecological process. The inland river basin intelligent ecological scheduling rehearsal method comprises the steps of multi-source data acquisition and digital twinborn construction; carrying out reservoir intelligent scheduling reinforcement learning modeling; ecological process lag response modeling is carried out; spatial diffusion modeling of ecological influence; performing cross attention guided ecological response interpolation; and carrying out rehearsal and visual display on the ecological scheduling scheme. According to the method, a hydrological-ecological response modeling mechanism is introduced, and a time-space response relationship between scheduling behaviors such as water level and water volume and ecological indexes such as vegetation indexes and habitat indexes is combined, so that lagging response characteristics of an ecological process to the scheduling behaviors can be quantitatively described, and the defect that a traditional scheduling model is insufficient in ecological expression capability is overcome. A reinforcement learning algorithm is utilized to fuse multi-source data for state perception and strategy iteration, and the scheduling strategy can be dynamically adjusted according to the current hydrological situation and ecological feedback result of the watershed. Compared with static rule type scheduling, the regulation and control efficiency and ecological adaptability are remarkably improved.

The invention discloses a winter wheat LAI and SPAD estimation method based on a lightweight semi-supervised model, and relates to the technical field of agricultural remote sensing monitoring, and the method comprises the steps: obtaining multispectral image data of a winter wheat key growth period, and carrying out the preprocessing of the multispectral image data to generate a standardized multichannel vegetation index image; the method comprises the following steps: constructing a lightweight semi-supervised model MCVI-SANet, and carrying out self-supervised training on the MCVI-SANet by adopting a semi-supervised training strategy driven by VICReg; and inputting the multi-channel vegetation index image into the trained MCVI-SANet, and outputting quantitative estimation results of the LAI and SPAD of the winter wheat. Through combination of a saturation perception mechanism and semi-supervised learning, estimation deviation caused by dense canopy vegetation index saturation and data noise is effectively eliminated, and the estimation precision and generalization ability in a complex agricultural scene are improved while the lightweight deployment characteristic of the model is ensured.

The invention relates to the field of vegetation index monitoring, and discloses a method for monitoring solanum roselle based on a multi-spectral index change rate of an unmanned aerial vehicle, which comprises the following steps: acquiring multi-spectral image data of a target area under a continuous time sequence; extracting NDVI, GNDVI and red edge vegetation index change layers from the reconstructed time sequence multispectral image by adopting a self-adaptive spectrum calibration algorithm to obtain a vegetation index change rate initial map; the method aims at the spectrum confusion problem of the eggplant at different growth stages and peripheral plants in change rate expression. Carrying out deep fusion on the initial vegetation index change rate map and the growth fluctuation contour model, constructing a change rate anomaly response factor map layer, and outputting suspected distribution candidate regions of the solanum aureocauda var. Aureocauda var. Aureocauda; and combining the change rate evolution trend of the historical extension path with a neighborhood growth consistency index, and dynamically generating a high-confidence identification map of the solanum aureum. The method has the advantage of improving the change rate discrimination precision.

The invention discloses a poplar degenerated forest stand accurate restoration method, and relates to the technical field of poplar degenerated forest stand restoration, and the method comprises the steps: obtaining a multi-temporal satellite remote sensing image and an unmanned plane multi-spectral image of a target area; in combination with soil moisture content, rhizosphere respiration rate and micrometeorological data collected by ground Internet of Things sensing nodes arranged in a forest stand, geographic coordinate registration and time sequence alignment are performed on the data to form a multi-source observation data set; the method comprises the following steps: extracting vegetation indexes, short-wave infrared reflectivity, soil moisture and rhizosphere physiological parameters based on a multi-source observation data set, constructing a degradation diagnosis index fusing spectral change, moisture stress and root activity, calculating the degradation degree of each pixel, and generating a degradation level spatial distribution diagram; the area with the degradation degree reaching a set threshold value is determined as a to-be-repaired area; and selecting representative poplar individuals in the to-be-repaired area, measuring the photosynthetic ability of the individuals, and calculating the individual competition intensity in combination with the three-dimensional point cloud data.

The invention relates to the technical field of remote sensing geological prediction, in particular to a slope support stability prediction method and system based on remote sensing data, and the method comprises the steps: obtaining a multispectral remote sensing image of a target slope region, and generating a vegetation mask through employing a normalized vegetation index; removing interference pixels of a vegetation coverage area in combination with morphological filtering and connected area analysis, then identifying support structure features through an improved edge detection algorithm, delimiting an influence area by adopting a region growing algorithm based on machine learning, inputting an area image into a convolutional neural network model, and obtaining an image of the support structure; a deformation probability graph is output by using a data enhancement technology and a weight optimization layer, finally, a threshold value is determined according to historical data, slope support stability categories are divided in combination with spatial neighborhood information and a voting mechanism, vegetation interference is effectively eliminated, a support structure is accurately recognized, prediction precision is improved, slope support stability can be accurately judged in time, and the method is suitable for popularization and application. And a reliable basis is provided for early warning and protection of slope disasters.

The invention relates to the technical field of agricultural remote sensing information processing, and particularly discloses a crop planting pattern spot intelligent extraction system based on remote sensing information, which constructs a multi-scale feature vector by fusing pattern spot area, compactness, time sequence vegetation index fluctuation and field ridge slope variation coefficient, and combines dynamic threshold adjustment and spatial semantic verification to obtain a multi-scale feature vector. According to the method, automatic correction of abnormal fragments, giant spots and topological conflicts is achieved, in complex scenes such as Yunnan terraced fields, through vertical field and ridge error response suppression and dynamic graph reconstruction, the pattern spot boundary precision and topological rationality are remarkably improved, and a classification result in a GeoJSON format is output.

The invention relates to the technical field of ecological remote sensing and image processing, and particularly discloses a method and system for monitoring the ecological restoration state of thinopyrum intermediary grassland in an alpine region. The method comprises the following steps: acquiring a multi-temporal remote sensing image and ground ecological parameters, and constructing a remote sensing-ground combined observation data set; utilizing a semantic segmentation model of a multi-scale attention mechanism to extract a thinopyrum intermedium block mask; vegetation indexes, texture features and ecological factors are extracted from the mask region to construct a multi-dimensional feature sequence; inputting the feature sequence and manual intervention information into a training factor interaction model in a graph neural network and causal modeling architecture, and outputting a repair level and evolution data according to the training factor interaction model; and generating a visual layer in combination with historical data, and updating a remote sensing feature extraction strategy based on model feedback. The alpine grassland ecological restoration state monitoring system realizes intelligent monitoring and dynamic feedback of the alpine grassland ecological restoration state, has strong timeliness, clear causality and adaptive optimization capability, and is suitable for continuous monitoring and management of a complex ecological system.

The invention provides a coastal wetland ecological corridor identification and optimization method and system, and relates to the technical field of ecological environment monitoring. The method comprises the following steps: integrating optical, radar, terrain, night light and road data on an earth engine cloud platform, and unifying time and space references to form an initial data set; extracting a normalized vegetation index and a vegetation coverage degree, and combining with a water body frequency to identify an ecological source land; generating a comprehensive resistance surface according to the land utilization and coverage type and the road distance; and performing minimum cost path and circuit connectivity analysis in a geographic information system to obtain potential galleries, key nodes and barrier regions, and outputting gallery grades and optimization suggestions in a grading manner according to a cost threshold, thereby realizing high-precision identification and optimization of the coastal wetland ecological network.

The invention discloses a rainfall type landslide early warning method based on multi-source environment threshold and nonlinear fusion. The method comprises the following steps: constructing a landslide disaster-inducing factor set; introducing soil humidity, vegetation indexes and evapotranspiration, and calculating a threshold parameter corresponding to the rainfall type landslide event to obtain an environment threshold point set; according to the landslide disaster-inducing factor set, constructing and training a landslide susceptibility model based on progressive learning; adopting a double-layer nonlinear fusion model based on multi-source data fusion and quality perception gating, and combining a landslide susceptibility model to obtain comprehensive risk probability distribution; and performing graded early warning based on the comprehensive risk probability distribution to complete rainfall type landslide early warning. According to the method, on the basis of traditional rainfall parameters, early-stage disaster-pregnant environment factors are introduced, and a multi-source environment threshold value is formed; fuzzy logic reasoning is adopted to realize nonlinear fusion of the threshold information and the landslide susceptibility base map; and dynamically reflecting a regional environmental condition evolution process in combination with an annual iterative updated susceptibility layer, and realizing finer and more adaptive space grading early warning.

The invention belongs to the technical field of research on the relationship between snow drought and vegetation browning, and particularly discloses a quantification method and terminal equipment for the transfer relationship between snow drought and vegetation browning, and the method comprises the steps: obtaining a standard rainfall index, a nonparametric standardized snow water equivalent index and a normalized vegetation index in a preset time period of a research region; the snow drought grade of the research area is determined according to the non-parametric standardized snow water equivalent index, the snow drought type of the research area is determined according to the standard rainfall index and the non-parametric standardized snow water equivalent index, and the snow drought type comprises dry snow drought and warm snow drought; according to the normalized vegetation index, the standard rainfall index and the nonparametric standardized snow water equivalent index, determining the transmission time of vegetation browning caused by the corresponding type of snow drought; and according to the transmission time, determining the transmission probability of vegetation browning of different degrees caused by different grades and different types of snow and drought. According to the method, the transmission time and the transmission probability from snow drought to vegetation browning are quantified, and early drought monitoring and early warning can be improved.

The invention provides a water chlorophyll a concentration inversion method and system based on multi-modal data and a lightweight model, and relates to the technical field of water environment remote sensing evaluation. The method comprises the following steps: firstly, acquiring a Gaofeng No.5 satellite remote sensing image, a sentinel No.3 satellite image and ground actual measurement data, and completing image preprocessing and water body pixel extraction; constructing a hyperspectral index and an aquatic vegetation index, and fusing the hyperspectral index and the aquatic vegetation index with the water body temperature, the pH environmental factors and the spectral reflectivity to form a multi-dimensional feature sample set; a core feature subset is obtained through random forest and XGBoost coupling feature selection, and a lightweight student model is trained based on knowledge distillation; and constructing a to-be-predicted feature sample for the to-be-predicted time phase image and the environment factor, inputting the to-be-predicted feature sample into the lightweight student model to obtain a chlorophyll a concentration predicted value, and generating a spatial distribution map and a quality control map layer. According to the invention, high-precision, low-redundancy and efficient deployment chlorophyll a concentration inversion is realized.

The invention relates to the technical field of agricultural quantitative remote sensing and intelligent agriculture, and discloses a multi-source remote sensing data radiation correction and chlorophyll content cross-platform inversion method, which comprises the following steps: synchronously obtaining ground actual measurement, unmanned aerial vehicle and satellite remote sensing data, calculating vegetation indexes, and screening sensitive characteristic variables by using Pearson correlation analysis; establishing a radiation correction model by adopting a ratio averaging method, performing radiation normalization processing on satellite data by taking unmanned aerial vehicle data as a reference, and eliminating sensor response differences; an SPAD inversion model is constructed and optimized based on Z-score standardization, the SPAD inversion model is migrated and applied to corrected satellite data, a regional scale chlorophyll distribution diagram is generated through pixel-by-pixel calculation, and then a differentiated farmland management strategy is formulated. According to the method, the difficulty of multi-source data collaboration is solved, the regional scale crop chlorophyll monitoring precision is improved, and scientific decision support is provided for large-area crop fertilization.

The invention belongs to the technical field of remote sensing, and discloses a wolfberry planting distribution high-precision remote sensing monitoring method based on multi-temporal fusion and spectral feature optimization. According to the method, a multi-temporal and multi-spectral satellite image is used as a data source, and preprocessing and multi-temporal image fusion are firstly carried out; the method comprises the following core steps: establishing a time sequence characteristic curve according to a unique phenological period (such as bare soil characteristics in a dormancy period and high vegetation coverage in a rapid growth period) of wolfberry; in a spectral domain, screening out a characteristic spectrum dimension combination with the highest discrimination degree between the wolfberry and other crops through a characteristic wave band optimization algorithm (such as vegetation index difference degree and red edge characteristics); and in combination with an object-oriented classification or deep learning classification model, constructing a space-time coupling classifier, and performing high-precision extraction and distribution mapping on the Chinese wolfberry planting region. The method can effectively solve the problem of confusion classification of Chinese wolfberry and similar ground features (such as other shrubs and orchards), and realizes rapid and accurate monitoring of Chinese wolfberry planting area and spatial distribution.

The invention discloses a coastal culture pond expansion recognition and mangrove forest ecological occupation and supplement balance evaluation method, and belongs to the technical field of environment remote sensing monitoring and ecological evaluation. Aiming at the problems that a mangrove forest ecological system is damaged by expansion of a coastal culture pond and a dynamic monitoring and quantitative evaluation mechanism is lacked, the evaluation method is provided and comprises the following steps: preprocessing a multi-temporal satellite remote sensing image; using a convolutional neural network computer model to supervise, classify and distinguish culture ponds, mangrove forests and other land types; extracting boundaries of the two-time-phase culture pond and identifying a newly-added area; in combination with the mangrove forest distribution map, obtaining an occupied area through spatial overlay analysis; establishing a biomass regression model based on the vegetation index value of the infringed area to estimate the biomass per unit area, and hierarchically calculating the carbon reserve loss amount; and calculating a compensation area according to the carbon loss amount and the unit carbon reserve of the mature mangrove forest in the target area, and generating an occupation and compensation balance scheme in combination with unutilized mud flat spatial distribution. The method is used for dynamic monitoring, accurate quantification and scientific compensation of mangrove forest ecological loss.

The invention discloses a forest land ecosystem carbon reserve accounting method and system, and the method comprises the steps: obtaining and screening feature variables, such as optical remote sensing data, vegetation indexes, texture factors, satellite-borne laser radar feature parameters, so as to determine features having the most influence on a target variable, and removing redundant and irrelevant variables; the method comprises the following steps of: screening characteristic variables, reserving variables which contribute to the model prediction effect, improving the precision and stability of the model, constructing a canopy height inversion model by adopting a random forest method based on the screened characteristic variables, realizing accurate estimation of the canopy height, and finally calculating the biomass of vegetation of the forest ecological system by using a different-speed growth equation. And finally, summarizing the biomass of the vegetation of the forest ecological system to obtain the total biomass of the vegetation, and multiplying the total biomass of the vegetation by the carbon-containing coefficient to obtain the vegetation carbon reserve of the forest ecological system, so that wide-area and efficient forest parameter monitoring is realized, the monitoring cost is reduced, the environmental adaptability is improved, and the problems of data continuity and standardization are solved.

The invention belongs to the technical field of agricultural remote sensing, and discloses a method for evaluating cold resistance of pennisetum alopecuroides based on unmanned aerial vehicle multispectral images, which comprises the following steps: acquiring agronomic characters related to cold resistance, such as chlorophyll content, overwintering rate and the like of the pennisetum alopecuroides before and after low-temperature stress through manual measurement, and acquiring the multispectral images by using an unmanned aerial vehicle; the image is preprocessed, and various vegetation indexes such as DVI, NDVI and RVI are extracted; on the basis of agronomic character data, a comprehensive cold resistance index CRI is constructed by adopting principal component analysis and a membership function method; using the vegetation index as an independent variable and CRI as a dependent variable, and using a machine learning algorithm to construct a prediction model; and finally, realizing rapid and nondestructive evaluation on the cold resistance of a large-range pennisetum alopecuroides group by utilizing the model, and outputting a five-level cold resistance grading result according to a CRI value. According to the method, the problems of low efficiency and high difficulty of traditional manual evaluation are solved, high-throughput and precise cold resistance identification is realized, and reliable technical support is provided for cold-resistant breeding and cultivation management of pennisetum alopecuroides.

The invention relates to the technical field of remote sensing image recognition, in particular to a method for determining a moso bamboo forest snow disaster affected area based on a GEE platform. The method mainly solves the problems of low efficiency, high cost and difficulty in realizing large-range rapid evaluation caused by dependence on manual field investigation in the prior art. According to the technical scheme, the method comprises the following steps: firstly, obtaining a multi-temporal Sentinel-2 image before and after a snow disaster, and carrying out the preprocessing of the multi-temporal Sentinel-2 image; extracting spectral bands, vegetation indexes and texture features to form an initial feature set; then, a key feature variable combination is determined through statistical significance filtering and machine learning optimization; on this basis, establishing a logistic regression discrimination model and determining an optimal classification threshold; and finally, carrying out snow disaster state classification on the moso bamboo forest region by utilizing the trained model, and generating a disaster region spatial distribution diagram.

The invention relates to a crop plot identification method and system based on time sequence vegetation characteristics, and the method comprises the steps: constructing a normalized difference vegetation index time sequence according to a multi-temporal satellite remote sensing image, and generating a crop probability distribution diagram through a crop classification model; performing connected domain analysis on the crop probability distribution map, extracting the center of gravity of an effective connected domain as a forward attention point, scanning the crop probability distribution map by using a sliding window, and generating a reverse attention point; based on the forward attention point and the reverse attention point, segmenting the high-resolution remote sensing base map through a visual segmentation model, generating a candidate mask set, and screening to reserve a mask with the maximum area as a land parcel segmentation mask of the forward attention point; and combining all the plot segmentation masks to generate a crop plot identification graph, and converting the boundary of each plot segmentation mask into a geodetic coordinate sequence to obtain plot vector boundary data. The method improves the recognition accuracy, and achieves the automatic and precise segmentation of the boundary of the land parcel.

The invention provides an agricultural image background removal method, and relates to the technical field of agricultural image processing, and the method comprises the steps: carrying out the adaptive illumination condition judgment of a preprocessed agricultural image; calculating and selecting corresponding vegetation indexes according to the light condition judgment result, and calculating to generate a plurality of vegetation index feature maps; inputting the plurality of vegetation index feature maps into a machine learning model, learning the relative importance of each vegetation index through the machine learning model, and generating a comprehensive vegetation feature map; performing vegetation texture region judgment on the preprocessed agricultural image to obtain a target vegetation texture region judgment result; judging a foreground mask based on the comprehensive vegetation feature map and a target vegetation texture region judgment result; performing morphological processing on the foreground mask to obtain an optimized foreground mask; and according to the optimized foreground mask, removing a background from the agricultural image in the original RGB format to obtain a target vegetation image.

The invention discloses a cross-platform soybean seed protein content estimation method based on a spectral time sequence. The method comprises the following steps: step 1, acquiring high spectral data of a soybean canopy; 2, collecting a soybean multispectral image; step 3, acquiring soybean physiological parameters and grain protein content data; step 4, obtaining soybean photosynthetic parameter data; step 5, calculating a vegetation index for estimation; step 6, constructing a vegetation index time sequence; 7, screening an optimal time window; 8, extracting dynamic characteristics; step 9, establishing a model, estimating the soybean protein content, and performing precision verification; and step 10, carrying out mobility analysis on the model from a near-ground canopy to a satellite platform. According to the method, a theoretical basis and a practical path are provided for realizing remote sensing-based soybean GPC accurate monitoring, wide application of the method on a satellite remote sensing platform can be promoted in the future, and high-precision, high-time-efficiency and large-scale soybean GPC estimation is realized.

The invention discloses a soil attribute high-precision mapping method and system for optimizing the sampling number by fusing an air-ground imaging hyperspectral technology, and belongs to the technical field of soil monitoring. The method comprises the following steps: collecting and preprocessing a regional unmanned aerial vehicle hyperspectral image; collecting a sample point soil sample and obtaining ground imaging hyperspectral data of the sample point soil sample; screening and optimizing a sample point subset based on ground imaging hyperspectral data, measuring the available phosphorus content and training a prediction model; segmenting and extracting a soil region in the unmanned aerial vehicle image by combining a vegetation index and a maximum between-cluster variance method; and carrying out wave band alignment and reflectivity correction on the ground imaging hyperspectrum and the unmanned aerial vehicle soil spectrum, complementing corrected unmanned aerial vehicle hyperspectral data, and inputting the corrected unmanned aerial vehicle hyperspectral data into a soil available phosphorus prediction model to carry out inversion mapping. The problems that ground imaging data coverage is insufficient and unmanned aerial vehicle data is easily affected by the environment are solved, and key technical support is provided for intelligent agriculture and accurate soil nutrient management.

The invention relates to a crown segmentation method based on a CSAF framework, and the method comprises the steps: integrating Fourier transform and a state space model through a GM-Mama module, and effectively improving the extraction capability of a fuzzy crown contour; a forgetting factor is dynamically regulated and controlled by using an MASA-Optimizer module in combination with a multi-agent negotiation mechanism, and continuous self-adaptive learning of dynamic changes of species and phenology is realized; a physical constraint based on a Poisson equation is introduced by means of an MPC-Poisson module, and spectral interference of non-target vegetation is significantly inhibited; according to the method provided by the invention, high-precision crown instance segmentation and counting can be realized in a cross-species and cross-phenological complex scene, accurate calculation of a vegetation index of a single plant level is supported, and a flexible and powerful method is provided for analysis of different types of forest crowns.

The invention discloses a forestry restoration vegetation state identification method and system based on image identification. The method comprises the following steps: obtaining a standard image sequence; segmenting the visible light image through an improved SegFormer network to obtain vegetation areas of different scales, and calculating vegetation canopy temperature distribution characteristics based on the thermal infrared image; extracting vegetation index features based on the multispectral image to obtain multi-scale feature data; according to the multi-scale feature data, utilizing an improved GAT model to process a vegetation spatial relationship graph, learning a spatial dependency relationship between vegetation areas through a multi-head attention mechanism, and generating a vegetation feature vector containing spatial information; and classifying the vegetation states by using an improved multi-scale fusion classifier in combination with the vegetation feature vectors, outputting a vegetation recovery index, and classifying the forestry restoration vegetation states according to the vegetation recovery index. The whole forestry restoration monitoring process is automatic, the monitoring efficiency is improved, and the labor cost is greatly reduced.

A crop species identification system based on satellite telemetry data is disclosed, which includes: a receiver module, receiving plural telemetric vegetation indexes of a target area; a data cleaning module, cleaning anomalous data in the telemetric vegetation indices, to correspondingly generate cleaned index data; a feature extraction module, including at least two different convolution kernels for mapping the cleaned index data into at least two feature scale mapping data which respectively correspond to the convolution kernels, performing a pooling operation of the feature scale mapping data to generate a pooled data, and concatenating the at least two feature scale mapping data and the pooled data into concatenated data; and a classification module, including a fully-connected layer, for extracting features of the concatenated data, to generate a species classification result for the target area.

The invention relates to the technical field of remote sensing geological information processing, and discloses a mineral geological exploration system based on remote sensing image texture analysis, which comprises a time sequence base line library construction unit, an ecological proxy texture channel processing module, a remote sensing image texture analysis unit and a remote sensing image texture analysis unit, the system comprises a normalization vegetation index texture purification module for purifying a normalization vegetation index texture of a current image according to a time sequence statistical baseline so as to generate an ecological proxy texture anomaly graph, a texture channel construction processing module for generating a texture anomaly graph, and a collaborative verification engine module for performing collaborative verification on the texture anomaly graph before executing spatial coupling judgment. According to the method, the information quality of two channels is evaluated according to image information entropy, verification logic is dynamically selected, earth surface covering information regarded as interference in traditional exploration is converted into an independent verification dimension, and an evidence chain of internal cross verification is constructed through double constraints of space and time and self-adaptive evaluation of the information quality.

The invention discloses a landscaping maintenance monitoring and early warning method and system based on computer vision, particularly relates to the field of computer-aided identification, and is used for solving the problem that the existing landscaping maintenance depends on manual patrol and is difficult to find abnormal plant growth in time. The method comprises the following steps: acquiring a plant optical image, segmenting the plant optical image, extracting features such as shadow area proportion, compactness and gray average, and establishing a multi-scale shadow feature sequence; a shadow feature prediction model is trained by combining the environment and phenological data in the sliding time window; performing longitudinal and transverse residual calculation on the real-time observation features and the model prediction result, and fusing to generate an abnormal confidence score; enabling the abnormal confidence coefficient to correspond to a canopy projection region, and recognizing an abnormal region; and finally, the vegetation index difference ratio, texture and edge features of the abnormal region are extracted, the abnormal type is judged through a rule base, an early warning report is generated, and intelligent identification and efficient early warning of garden plant abnormity are realized.