15 results about "Vegetation classification" patented technology

The invention discloses a resident-free island vegetation classification method. The method comprises the steps of S1, data acquisition and preprocessing; s2, constructing a random forest model; s3, feature importance evaluation; s4, evaluating feature correlation; s5, feature optimization; s6, precision evaluation; according to the method, the problem that high-precision classification and time sequence dynamic monitoring of non-resident island coastal blue carbon resources in a complex environment are difficult to realize is solved, a remote sensing fine monitoring method is provided, and only satellite remote sensing and ground monitoring data are used; and fine classification and time sequence change detection of resident-free island vegetation types are realized.

The application provides a ConvNeXt-based remote sensing image vegetation classification method and device, relates to the technical field of vegetation classification, and comprises the following steps: acquiring sample remote sensing image data, expanding the sample remote sensing image data by using an Fmix mixed sample data enhancement algorithm, and obtaining a sample data set; training a vegetation classification model by using the sample data set to obtain a target vegetation classification model, wherein the vegetation classification model comprises a feature encoder constructed based on ConvNeXt and a decoder constructed based on UperNet; after acquiring to-be-classified remote sensing image data, inputting the to-be-classified remote sensing image data into the target vegetation classification model to obtain an initial classification result; performing adjacent category fusion processing on target objects in the initial classification result, and performing contour simplification processing on graph patches in the category fusion processing result to obtain a target classification result, thereby solving the technical problem of low precision of the existing vegetation classification method.

The application provides a loess hilly vegetation disease and pest remote sensing monitoring and evaluation method and device, relates to the technical field of remote sensing monitoring and evaluation methods, and comprises the following steps: calculating the coverage of vegetation by using a double threshold plane method, taking the coverage of vegetation of each grid area as a correction term of ground surface temperature; constructing a temperature difference anomaly index of each grid area; based on the temperature difference anomaly index and transpiration efficiency index of each grid area, calculating the transpiration efficiency index of each grid area by using a vegetation temperature difference method, constructing a vegetation classification model by using a logistic regression method, and classifying each grid area according to a preset threshold value to distinguish a risk grid area and a normal grid area. A prediction model is constructed through the statistical relationship between the vegetation index and the ground surface temperature, the temperature difference anomaly index is quantified, early stress signals of vegetation that are difficult to find can be recognized, the transpiration efficiency is introduced to comprehensively evaluate the water utilization of vegetation, and through a dynamic threshold value and a statistical model, potential risk grids can be recognized before the outbreak of diseases and pests.

[Problem] To provide a vegetation determination system that estimates vegetation proportions of a plurality of plant classifications on the basis of a satellite image. [Solution] In the present invention, an information processing system generates a second trained model by using a training data set which includes a satellite image as input data and includes, as teacher data, an output result of a first trained model generated using a UAV image. The information processing system comprises: a vegetation proportion map generation unit that generates the first trained model by performing training processing for determining a vegetation classification, by using a high-resolution vegetation classification training data set in which the UAV image and the teacher data indicating the vegetation classification are associated with each other, and generates a vegetation proportion map on the basis of a vegetation classification image outputted from the first trained model; a low-resolution vegetation proportion training data generation unit that generates a low-resolution vegetation proportion training data set in which the vegetation proportion map is associated, as the teacher data, with the satellite image; and a second training processing unit that performs machine learning processing for determining vegetation proportions from the satellite image, by using the low-resolution vegetation proportion training data set.

The application discloses a kind of estimation method and system of vegetation canopy combustible moisture content based on meteorology and remote sensing data, comprising the following steps: first, combustible moisture content and various meteorological data and remote sensing data and other several kinds of subsidiary data are selected as combustible moisture content estimation data.Meteorological data includes air temperature, relative humidity, rainfall and wind speed.Remote sensing data includes two vegetation indexes: enhanced vegetation index and normalized vegetation index.Subsidiary data includes: root zone soil moisture, vapor pressure difference, drought index, fire weather factor.Then the long time sequence characteristics of meteorological data are extracted.The size of time window is determined first, and the experimental results show that the correlation coefficient of most sites is relatively high under the time window of 90-210 days.90 days, 150 days and 210 days of time window are selected respectively to extract the time characteristics of four kinds of meteorological data.Secondly, the samples of experimental area are divided into five vegetation classifications, which are closed shrub, sparse shrub, multi-tree tropical grassland, tropical savanna and grassland.Finally, the data sets of the five different vegetation types are sequentially adjusted to obtain the respective estimation model.

The invention discloses a vegetation classification method based on space-time multi-modal deep learning, and relates to the technical field of image processing, and the method comprises the steps: obtaining a multi-temporal optical image, a radar image and digital elevation model data of a to-be-classified region, and forming multi-modal data; extracting spectral features, microwave features, topographic features and texture features to form fusion features; calculating graph node features, and updating the graph node features to form graph features; and fusing the fusion features and the graph features to obtain pixel-level coarse classification logs, carrying out feature extraction and fusion on the fusion features to form region-level coarse classification logs, carrying out fusion to obtain a coarse classification probability, and carrying out fine classification on coarse basic features to obtain a final classification result. The method provided by the invention can adapt to a mountainous area environment with multiple clouds, multiple shadows and large topographic relief, improves the stability and classification fineness of vegetation type identification, and is suitable for wide-range vegetation monitoring and ecological assessment scenes.

The application discloses a power transmission channel vegetation intelligent classification method and device based on UNET and hyperspectrum, and a medium, belongs to the field of computer vision and image processing, including collecting hyperspectrum images of the power transmission channel area by a hyperspectrum remote sensing device and preprocessing; designing and constructing a UNET neural network, training the model using labeled tree category data, classifying using newly collected test data, outputting the tree category to which each pixel point belongs, performing post-processing operations, removing small area noise through morphological operations, using a conditional random field optimization model output, and improving the classification results of similar categories between different tree species. The application improves the accuracy of vegetation classification, improves the processing efficiency and automation degree, effectively solves the classification problem of similar tree species, enhances the spatial consistency of the classification results, saves the labor cost and reduces the error rate.

This application discloses a vegetation classification method based on spatiotemporal multimodal deep learning, relating to the field of image processing technology. The method includes: acquiring multi-temporal optical images, radar images, and digital elevation model data of the region to be classified, forming multimodal data; extracting spectral features, microwave features, topographic features, and texture features to form fused features; calculating graph node features and updating the graph node features to form graph features; fusing the fused features and graph features to obtain pixel-level coarse classification logits; extracting and fusing the fused features to form region-level coarse classification logits; fusing to obtain coarse classification probabilities; and performing fine classification on the coarse class basic features to obtain the final classification result. The method of this application can adapt to mountainous environments with frequent cloud cover, shadows, and large topographic relief, improving the stability and classification precision of vegetation type identification, and is suitable for large-scale vegetation monitoring and ecological assessment scenarios.

The invention discloses a wetland vegetation classification method and system, and the method comprises the steps: obtaining a synthetic aperture radar SAR image and a multispectral MSI image of a to-be-classified region, and carrying out the preprocessing; inputting the MSI image into a convolutional neural network (CNN) branch, and extracting spectral texture features by using an adaptive scale sensing module (ASPM); performing super-pixel segmentation on the SAR image to construct a super-pixel image; inputting the superpixel image into a GCN (Graph Convolution Network) branch, and extracting structural features by using an ADMS (Adaptive Graph Structure Module); performing pixel-level fusion on the spectral texture features and the structural features by using a gating fusion module GFM to obtain fusion features; and inputting the fusion features into a classifier, and outputting a classification result of the wetland vegetation.

The invention discloses an underwater vegetation classification method based on unsupervised learning and feature fusion. The method comprises the following steps: S1, collecting an original image; s2, inputting the original image into a deep convolutional neural network, removing redundant information, and generating a low-dimensional visual feature vector; s3, performing quantification processing on the original image through a multi-modal large model, performing ID processing on the unstructured graph according to the biological cue word, inputting the processed unstructured graph into an EAPCR-AE model, and extracting a low-dimensional high-density semantic feature vector; s4, performing L2 normalization on the low-dimensional visual feature vector and the low-dimensional high-density semantic feature vector respectively, and then performing splicing; constructing an orthogonal subspace by using a principal component analysis technology, extracting a principal component of which the cumulative variance contribution rate reaches a preset threshold value, and outputting a fusion feature vector; and S5, performing clustering and voting judgment on the fused feature vector to form a classification result. According to the underwater vegetation classification method, the feature fusion barrier of visual features and word meaning features in label classification is overcome, and the classification accuracy is high.

The present application provides a kind of vegetation classification method, device, equipment and medium based on space-spectrum neural network, method includes: high spectral dataset is divided into training set, verification set and test set, and quantitative evaluation index is set;Based on the information separation of space-spectrum mixed search space, the differentiable architecture search strategy based on gradient optimization is used to search the neural network architecture, and the target network architecture is obtained, the information separation spectrum transformation operator for extracting spectral features is included in the space-spectrum mixed search space, and the information separation space attention depth convolution operator for extracting spatial features;Based on training set and verification set, the target network architecture is trained to obtain a classification model;Test set is input into the classification model to obtain vegetation classification result, and the classification result is evaluated according to quantitative evaluation index, solves the problem that classification accuracy and efficiency still have room for improvement in complex vegetation fine classification task, cannot meet the demand of large-scale, high-precision remote sensing monitoring.

Vegetation management system includes: a data acquisition unit that acquires input data including remote sensing data obtained by photographing, by remote sensing, a facility and vegetation to be analyzed; a vegetation classification unit that classifies the vegetation photographed in the remote sensing data; a wide-area growth prediction unit that predicts a time-series change of a growth range of the vegetation photographed in the remote sensing data; a vegetation amount simulation unit that predicts a fluctuation in the growth amount of each vegetation by a simulation; a three-dimensional construction unit that constructs a three-dimensional model expressing the facility and the vegetation; and a risk determination unit that determines a contact risk indicating a contact possibility between the facility and the vegetation.

The application discloses a vegetation species identification method for strengthening AI computing power and time sequence tracing, comprising the following steps: collecting multi-source remote sensing data and performing pretreatment, constructing a vegetation classification dataset with spatiotemporal alignment and unified resolution; generating a vegetation mask based on the vegetation classification dataset, obtaining a standardized sample slice, and constructing a training dataset in combination with spectral characteristics; performing time-phase processing on the training dataset based on vegetation phenological characteristics, and outputting a preliminary vegetation classification result; performing object-level optimization on the preliminary vegetation classification result, and obtaining an optimized vegetation classification result; correcting the optimized vegetation classification result in combination with terrain data, generating a vegetation species classification map of a target year, and realizing vegetation dynamic change inversion in a specified time period through transfer learning. Therefore, the traditional resolution limit can be broken through, the classification error problem caused by independent use of multi-source data can be solved, the discrimination of complex vegetation types can be significantly improved, and historical vegetation dynamic backtracking analysis can be supported.

The invention provides a remote sensing monitoring evaluation method and device for loess hill vegetation diseases and insect pests, and relates to the technical field of remote sensing monitoring evaluation methods, and the method comprises the steps: calculating the coverage rate of vegetation through employing a dual-threshold leveling method, and taking the vegetation coverage rate of each grid region as a correction term of the surface temperature; constructing a temperature difference abnormal index of each grid region; and calculating the transpiration efficiency index of each grid region by adopting a vegetation temperature difference method based on the temperature difference anomaly index and the transpiration efficiency index of each grid region, constructing a vegetation classification model by adopting a logistic regression method, classifying each grid region according to a preset threshold value, and distinguishing a risk grid region from a normal grid region. A prediction model is constructed through a statistical relationship between a vegetation index and a surface temperature, a temperature difference abnormal index is quantified, an early stress signal of the vegetation which is difficult to find can be identified, transpiration efficiency is introduced to comprehensively evaluate water utilization of the vegetation, and a potential risk grid can be identified through a dynamic threshold value and a statistical model before pest and disease damage outbreak.

A vegetation management system includes a vegetation classification unit that classifies vegetation photographed in remote sensing data, a long-term change prediction unit that predicts, based on a classification result of the vegetation and the remote sensing data, long-term wide-area fluctuation that is range fluctuation of the vegetation in a predetermined long-term time sequence, a short-term change prediction unit that predicts, based on the classification result of the vegetation and the remote sensing data, short-term wide-area fluctuation that is range fluctuation of the vegetation in a predetermined short-term time sequence, a risk determination unit that determines, based on a prediction result by long-term change prediction unit and a prediction result by short-term change prediction unit, a risk due to contact of the vegetation and a facility, and a visualization unit that visualizes a determination result of the risk.