191 results about "Band selection" patented technology

The invention discloses a two-stage hyperspectral image band selection and target detection method, which comprises the following steps of: based on a two-stage band selection and background reconstruction network of a transformer, realizing band selection through a first-stage training transformer and realizing background reconstruction through a second-stage training transformer; a transform position coding module and a multi-head self-attention mechanism are used for learning similar features and difference features among wave bands, a full connection layer network is used as a clustering device, a structural similarity index and an Euclidean distance are added to serve as a loss function training model, wave band clustering is achieved, and finally a local variance is used for estimating the noise level of an image of each wave band. Obtaining a band training subset; the band subset is subjected to constraint energy minimization detection and then sent to a background feature extraction network composed of a transformer and a discriminator, background pixels are arranged through position coding, network learning background features are enhanced through a multi-head self-attention mechanism, an improved loss function is used for training, and therefore reconstruction of a background image is achieved. The invention provides a two-stage wave band selection and background reconstruction network based on transformer so as to realize hyperspectral target detection. The network comprises a first-stage wave band selection work and a second-stage background spectrum learning work. And difference detection is carried out on the final background reconstruction image and the original image, so that a final task is realized, and the detection precision is improved.

The invention provides a regional soil component detection method based on a remote sensing image, and belongs to the technical field of soil detection. Aiming at the problems that a traditional detection method depends on a single optical remote sensing image, is interfered by weather and vegetation, and is low in detection precision, low in efficiency, unguaranteed in data credibility and the like, radar and thermal infrared remote sensing data are introduced to be fused with an optical image, features are extracted through principal component analysis, a generative adversarial network is adopted to carry out hyperspectral image super-resolution reconstruction, and a high-resolution hyperspectral image is obtained. A genetic algorithm is utilized to realize adaptive wave band selection, a real-time atmospheric correction model is constructed, a recurrent neural network and a spatial convolutional neural network are utilized to mine spatio-temporal context information, a quantum computing acceleration algorithm is introduced, block chain management data is utilized, and unmanned aerial vehicle cooperative operation is combined. According to the method, the detection precision, efficiency and data credibility are remarkably improved, and the regional soil component detection process is effectively optimized.

The present disclosure provides a curve-based radiometric calibration method and system of a spaceborne hyperspectral imager, which belongs to the field of remote-sensing optical technologies. A new radiometric calibration coefficient curve is introduced to describe the radiometric properties of sensor response, providing the radiometric calibration coefficients of all bands of the hyperspectral camera with linear variable filter (LVF) within an imaging spectral range to match the implementation of the programmable band selection imaging technology, and thereby efficiently and simply implementing single-band imaging, integral imaging of adjacent band, imaging of randomly-selected band combination and within-spectral-range cyclic imaging. In the present disclosure, the radiometric calibration coefficient is used to cover the entire imaging spectral range of the hyperspectral camera and match the implementation of the programmable band selection imaging technology, and realizing on-orbit absolute radiometric calibration with simple flow and strong universality.

The invention discloses a method and system for detecting surface defects of a sheet metal part before coating, and the method comprises the following steps: S1, multi-modal data collection: synchronously collecting three-dimensional point cloud data and a hyperspectral image of the surface of the sheet metal part through a laser three-dimensional contourgraph and a hyperspectral camera, and recording environment illumination and temperature parameters at the same time; s2, data preprocessing: performing denoising and smoothing processing on the three-dimensional point cloud, extracting surface contour features, and performing defective pixel repair, waveband selection and reflectivity correction on the hyperspectral image; according to the surface defect detection method and system before coating of the sheet metal part, through multi-modal data acquisition and fusion of the three-dimensional morphology and the hyperspectral features, compared with traditional single visual detection, the surface defects of the sheet metal part can be more comprehensively and accurately recognized, particularly, tiny defects and oxide layer defects which are difficult to distinguish by naked eyes, the recognition rate is remarkably increased, and the detection accuracy is improved. And by adopting the lightweight deep learning model, the detection precision is ensured, and the reasoning speed is improved at the same time.

The invention discloses a hyperspectral band selection and extraction system and method based on an FPGA (Field Programmable Gate Array), and mainly solves the problems of high complexity, poor real-time performance, low compressed sensing band selection efficiency and more occupied resources of the existing band selection algorithm. According to the implementation scheme, the method comprises the following steps of: generating data storage and an address by using a three-layer nested counter, generating a storage address of a hyperspectral image, reading an external original hyperspectral image according to the address, and storing the external original hyperspectral image into an RAM (Random Access Memory) for caching; setting an n-order linear feedback shift register, feeding back a polynomial and a seed value, generating a sparse random binary matrix, and linearly outputting a 0-1 random number to a subsequent operation module; and carrying out operation on the input original hyperspectral data and the sparse random binary by adopting a judgment accumulation mode to obtain selected hyperspectral data. According to the method, the number of wavebands needing to select dimension reduction can be flexibly adjusted, internal resource consumption is reasonably reduced, the timeliness of waveband selection in a satellite-borne scene is improved, and the method can be used for efficient spectral information processing of satellite-borne, airborne and other resource-limited platforms.

The invention discloses a physical information neural network hyperspectral band selection method for target identification, which uses physical information SDI as prior information to be combined with a channel attention mechanism added in a deep neural network to guide band selection, and evaluates the advantages and disadvantages of band weights through a reconstruction module after the weights are generated. Wave band selection is carried out accordingly; according to the hyperspectral image target recognition waveband selection method based on the physical information neural network, compared with a traditional method, the process is simplified and the precision is improved through end-to-end learning, the utilization of spatial context information can be enhanced by adding an attention mechanism, and the model has interpretability by adding prior SDI information.

A communication system, a method, and a computer program product provide selecting and camping on primary band in idle mode in preparation for supporting a predicted data connection for demanding application(s) to a network system. The prediction is based on trigger event(s) that include one or more of a configuration of a communication system, context of use of the communication system, and user interaction with the at least one user interface device. A controller of the communication system identifies, selects, and camps on a combination of a primary band and at least one secondary band of the one or more available network nodes that satisfies a communication demand requirement for the demanding application(s) using carrier aggregation and / or dual connection in preparation for activation of the demanding application(s) and establishment of the data connection with the communication network.

The invention provides a self-adaptive frequency adjustment method, medium and system for an ammeter carrier communication module, and belongs to the technical field of intelligent ammeters, and the method comprises the steps: firstly obtaining and sampling a power line carrier communication signal, obtaining communication time distribution characteristics within 24 hours through time domain analysis, and carrying out frequency domain analysis to obtain noise spectrum characteristics; and establishing a time-frequency characteristic incidence matrix. 24 hours are divided into a plurality of transmission time windows based on the matrix, and the optimal frequency range of each window is determined and sub-band division is carried out. And performing channel quality evaluation on the sub-bands, substituting evaluation data into the noise characteristic equation set, solving to obtain a channel transmission characteristic curve, and selecting an optimal transmission sub-band for communication according to the channel transmission characteristic curve. The system monitors communication quality in real time, and performs time-frequency characteristic analysis and optimal sub-band selection again when a quality parameter is lower than a threshold value, so that adaptive frequency adjustment is realized, and the problem that current electric meter carrier communication is difficult to adapt to a complex power line channel environment is solved.

The invention provides a sea surface oil film thickness measuring method based on optimized laser-induced fluorescence spectrum wave bands, and belongs to the technical field of spectral analysis. The method comprises the following steps: emitting an excitation light beam to a to-be-measured sea surface oil film by using a laser, and exciting the oil film to generate a fluorescence signal; receiving the fluorescence signal based on a spectrum acquisition device, and obtaining fluorescence spectrum data of the oil film; selecting a characteristic wave band from the fluorescence spectrum data through a sparse partial least square model, and predicting the thickness of the sea surface oil film based on the characteristic wave band in combination with Lasso regression; meanwhile, the selection of characteristic wave bands is optimized by using a hedera helix algorithm. According to the method, the characteristic wave band of oil film detection is selected through the sparse partial least square model, the oil film measurement characteristic wave band is efficiently and accurately obtained, full-wave band collection is avoided, and the detection efficiency is improved; and in combination with the hedera helix optimized sparse partial least square model, the accuracy of characteristic wave band selection and the generalization of the model are further improved, and the precision and efficiency of oil film thickness measurement are remarkably improved.

The invention discloses a spectrum reconstruction method and system based on dynamic band selection and sparse sampling, and the method comprises the steps: selecting a fixed sampling mode or a sparse segmented sampling mode based on target spectrum features, so as to obtain a sampling wavelength sequence; any sampling wavelength in the sampling wavelength sequence is obtained, the theoretical cavity length corresponding to the sampling wavelength is calculated based on the linear relation between the cavity length of the FP cavity and the wavelength, the theoretical cavity length serves as the cavity length of the FP cavity after voltage is applied, the theoretical applied voltage corresponding to the sampling wavelength is calculated and measured, and a theoretical voltage signal sequence corresponding to the sampling wavelength sequence is obtained; according to the method, only the characteristic wave band of the measured target needs to be extracted instead of full-spectrum sampling, redundant information is reduced, the transmission and storage cost is reduced, meanwhile, the imaging speed is increased, the real-time performance is improved, full-spectrum reconstruction is achieved through spectrum reconstruction, and the method is suitable for large-scale popularization and application. And no loss is caused.

The invention discloses a point source methane emission wave band sensitivity enhancement signal identification method and system. The method aims at accurately identifying methane emission plume observed by a satellite. The identification method comprises the following key steps: data acquisition and preprocessing, refined construction of a methane unit absorption spectrum, sensitive wave band screening, and integration of a methane enhanced signal identification algorithm. The method solves the problems of suppressing false positive and enhancing methane signal detection, reduces the error of a methane concentration inversion result, improves the accuracy and reliability of methane identification by optimizing band selection and integrating an inversion algorithm especially in the aspect of point source methane emission plume identification, and has a good application prospect in the field of point source methane emission plume identification. And high-reliability data support is provided for refined methane emission identification and control.

The invention relates to the technical field of water body pollution monitoring, in particular to a multispectral image fused water body pollutant accurate recognition system, which comprises a data acquisition module, a cloud processing module, a boundary processing module, a pollution recognition module, a diffusion prediction module and a visualization module, the system constructs sub-pixel representation of a water pollutant boundary by using a differential geometry manifold theory, and realizes high-precision pollutant boundary description through a multi-scale analysis and curvature flow optimization technology; enhancing pollutant characteristic expression by adopting multispectral image fusion and an optimal wave band selection technology; using a support vector machine model to accurately identify various pollutant types such as oil films, oil spots, algae blooms and the like; based on a boundary fine description result and a pollutant type identification result, the diffusion trend of pollutants is accurately predicted in combination with historical flow, wind direction and wind speed data, and the system improves the water pollutant boundary identification precision and enhances the identification capability of complex boundary forms and low-contrast regions.

This disclosure provides methods, components, devices and systems for dynamic sub-band operation information. Some aspects more specifically relate to transmission of information from APs to STAs to aid in DSO sub-band selection. For example, such information may indicate a quantity of clients that have selected each DSO sub-band, a quantity of clients that have selected each DSO sub-band and are actively communicating, a rolling average of active STAs on each DSO sub-band, a recommendation of one or more DSO sub-bands, or any combination thereof. In some examples, such information may be provided on a more narrow basis (such as on a per-20 MHz subchannel basis or other individual sub-band basis) or on a broader basis (such as on a per-80 MHz subchannel basis or a group basis, such as on a per-group of sub-band basis).

The invention provides a soil ammonium nitrogen content hyperspectral prediction method based on an improved extreme learning machine, and relates to the technical field of hyperspectral prediction. The method comprises the following steps: firstly, collecting and treating a soil sample for soil spectral measurement and NH4 < + >-N content determination; measuring soil spectral reflectivity data; preprocessing the soil spectral reflectivity data to form a spectral reflectivity data set; then carrying out characteristic wave band selection by adopting a sequential forward selection algorithm; an improved butterfly optimization algorithm IBOA is adopted to optimize model parameters of an extreme learning machine ELM, and then a hyperspectral prediction model used for predicting the NH4 < + >-N content of the soil is constructed; and finally, the ELM model after parameter optimization is selected to construct a hyperspectral prediction model to predict the NH4 < + >-N content. The method not only provides theoretical and technical support for soil ammonium nitrogen content monitoring, but also provides important reference and guidance for soil nitrogen cycle research and soil management.

The invention discloses a hyperspectral image classification method based on light spectrum hybrid adaptive band selection, which belongs to the technical field of remote sensing image processing, and comprises the following steps: based on a data cube sample set and label vectors, carrying out hierarchical random sampling in proportion, and dividing a training set and a test set; inputting the training set into a hyperspectral image classification model based on light spectrum hybrid adaptive band selection, and performing forward propagation to record an optimal weight; inputting a test set into the model, performing forward propagation by using the optimal weight, outputting a pixel-level category probability through a convolution integral category head, and determining a prediction label; the forward propagation comprises the steps of establishing learnable weight vectors for all spectral bands at the first layer of a data loader, performing band-by-band weighting on each cube sample, extracting spatial spectral features by using a light spectrum mixed structure combined with a local convolution global converter, performing cross attention band screening on high-level spatial spectral tensor, and performing data processing on the high-level spatial spectral tensor. And dynamically calculating the importance weight of the spectral band, and updating the learnable band weight vector by using the importance weight of the spectral band.

The invention relates to a rice gelatinization characteristic rapid detection method based on a near infrared spectrum, which comprises the following steps: acquiring optical signals of different varieties of rice in a near infrared band and gelatinization characteristic values measured according to a traditional method, preprocessing the acquired optical signals by utilizing a smoothing (Savitzky-Golay) method; an evaluation model of rice gelatinization temperature, peak time, peak viscosity, minimum viscosity, cold glue viscosity, disintegration value, retrogradation value and subduction value is constructed based on machine learning and a characteristic wave band selection algorithm, and effective prediction of rice gelatinization characteristics is achieved only through optical characteristic parameters. According to the method, the gelatinization characteristics of the rice can be evaluated only by collecting the near infrared spectrum of the rice, and technical support is provided for rapid, efficient and stable product quality monitoring of food processing enterprises. Therefore, a rapid, efficient and stable quality monitoring technical support is provided for food processing enterprises, so that the quality control in the production process is more convenient and accurate. Through the technology, an enterprise can adjust production parameters in time so as to ensure the consistency and excellent quality of products.

The invention provides an unsupervised hyperspectral image wave band selection method and device based on variable granularity search, relates to the technical field of hyperspectral images, and solves the technical problems that the calculation complexity is high, only fixed-scale wave band subsets can be obtained, and relevance among different-scale subsets is ignored in the prior art. The method comprises the following steps: acquiring a hyperspectral image; preprocessing the hyperspectral image to generate a label image; performing feature grouping on the image wave bands according to the label images to obtain wave band groups; screening the wave band groups based on a variable granularity search algorithm to obtain candidate wave band subsets; and optimizing the candidate band subset based on a single target search algorithm to obtain an optimal band subset. The method is used in an unsupervised hyperspectral image waveband selection process.

The invention discloses a hyperspectral image waveband selection method and system based on a three-dimensional convolution auto-encoder. The method comprises the following steps: carrying out block processing on an original hyperspectral image; designing a three-dimensional space-spectrum reconstruction network based on a global spectrum sensing module; constructing a combined loss function of a reconstruction error term and a sparse regularization constraint term, and performing iterative updating on the network by using an Adam optimizer; constructing a wave band comprehensive evaluation criterion based on triple constraints and a dynamic fusion mechanism; and successively selecting an optimal wave band by adopting a greedy algorithm according to a comprehensive evaluation criterion, and outputting a wave band selection result of the hyperspectral image. Through fusion of a three-dimensional spatial spectrum reconstruction module and a multi-head self-attention mechanism, local and global association of hyperspectral data is comprehensively mined, and deep joint representation of spectrum and spatial information is realized. Meanwhile, sparse regularization constraint is introduced into a loss function, and the network is guided to focus the most valuable key wave band.

The invention belongs to the technical field of corn detection, and discloses a KAN convolution-based near infrared spectrum corn oil quantitative prediction method and system, and the method comprises the steps: obtaining the near infrared spectrum data of corn, and carrying out the preprocessing; dividing the spectral data into a plurality of windows, calculating the contribution degree of each window to a prediction target, selecting the windows according to the contribution degrees, performing feature extraction on the spectral data in the selected windows to obtain a low-dimensional potential representation, and reconstructing the low-dimensional potential representation to obtain a reconstructed potential representation; performing feature extraction on the reconstructed potential representation by using an improved KAN convolutional network to obtain comprehensive features, and performing quantitative prediction on the corn fat based on the comprehensive features; defining a loss function, optimizing model parameters, and obtaining a trained prediction model. According to the method, the KAN convolutional network is constructed based on the Kolmogorov-Arnold network, and an efficient prediction algorithm is established in combination with an auto-encoder and a moving window band selection technology, so that the prediction precision and generalization ability are improved, and meanwhile, the risk of overfitting is also reduced.

The invention belongs to the technical field of nondestructive testing of plant diseases, and discloses a nondestructive testing method and system for Candidatus Liberobacter asiaticum based on hyperspectral reflection and transmission fusion, and the method comprises the following steps: respectively obtaining hyperspectral reflection spectrum data and hyperspectral transmission spectrum data of to-be-detected citrus leaves, single reflection characteristic wave band data and single transmission characteristic wave band data are obtained after preprocessing and wave band selection; based on the single reflection characteristic wave band data, the single transmission characteristic wave band data and the spectrum data after the characteristic level fusion of the single reflection characteristic wave band data and the single transmission characteristic wave band data, obtaining a first prediction calculation result, a second prediction calculation result and a third prediction calculation result through respective optimal classification models; and determining a corresponding weight according to the classification accuracy of each optimal classification model, and performing decision-level fusion calculation on the first, second and third prediction calculation results to obtain the citrus Huanglongbing grade. According to the method, feature level and decision level fusion is introduced, so that the method has the capability of distinguishing the surface physical form and the internal structure components of the sample, and can be used for rapidly and nondestructively detecting the citrus huanglongbing.

The invention discloses an image definition evaluation method. The method comprises the following steps: acquiring an original image and an optical modulation transfer function; based on the original image and the optical modulation transfer function, generating a multi-scale baseline definition response set, an information-physical definition kernel, an uncertainty measure and an evidence vector; estimating a failure posterior probability based on the evidence vector; generating a gating weight based on the failure posterior probability and the uncertainty measure; and applying the failure posterior probability and the gating weight to execute failure posterior coupling processing on the multi-scale baseline definition response set and the information-physical definition kernel to generate a final definition evaluation result. The coupling process preferably includes applying analytic bias correction and performing cross-scale band selection. According to the method, the evaluation robustness in a complex scene is improved through a failure posterior coupling mechanism.

The present invention relates to the field of image processing technology, and specifically to a density peak clustering band selection method based on inverse nearest neighbor, generation of band inverse nearest neighbors, construction of enhanced local density, and feature band selection. The method adopts band inverse nearest neighbors, Euclidean distance calculation of distances between bands, and feature band selection to ensure that the selected band subset has better classification accuracy, effectively solve the density information of the local density description band, and thus provide a reliable basis for ground object recognition and classification through hyperspectral influence.

The present invention discloses a sparse adversarial attack method for a hyperspectral image classification model based on deep learning. A band selection algorithm based on an attention mechanism selects a specific subset of bands to generate a specific spectral band image. The generator of the sparse band perturbation generation module generates an adversarial perturbation according to the specific spectral band image, and the discriminator judges whether the input sample is a clean sample or an adversarial sample. Through the confrontation between the generator and the discriminator, the level of the perturbation generated by the generator is improved. The sparse band perturbation generation module is used to generate adversarial band data. The clean band data and the adversarial band data are combined to be restored to the original data size to obtain sparse band adversarial samples, which are input into the target hyperspectral image classification model to obtain incorrect classification results, thus completing the adversarial attack. Starting from the intrinsic attributes of hyperspectral, the present invention fully considers the influence of spectral bands on classification, and while achieving the purpose of adversarial attack by adding perturbations only on a few specific bands, reduces the amount of perturbation.

For user equipment (UE) that operates in a multi-link, single-radio (MLSR) mode, an access point (AP) of a (e.g., WiFi) local area network calculates link usage capacities (LUCs) for the different associated bands and performs comparisons based on those LUCs to the client traffic (CT) to select the band(s) to be used to transmit that CT during the next communication session between the UE and the AP. For example, if at least one LUC is greater than CT, then the AP selects the band with the greatest LUC. Otherwise, if two times the LUCs are greater than CT for at least two bands, then the AP distributes CT to the bands proportionately and sequentially. Otherwise, the AP uses conventional spraying to select the band. This band-selection technique provides improved throughput compared to using only conventional spraying for band selection.

This invention provides a feature band selection method based on neural network model pruning. The method includes the following steps: acquiring a hyperspectral dataset; dividing the dataset into a training set, a validation set, and a prediction set; constructing a classification network model based on the dataset; constructing a loss function for training the classification network model; inputting the training set and validation set into the classification network model for training, pruning the feature bands, and selecting discriminative hyperspectral data bands; and using the trained classification network model and the selected bands to classify the prediction set to obtain the classification result. This invention reduces the required bands while maintaining the accuracy of the analysis technique, retaining bands with stronger discriminative power for classification, thereby significantly improving classification accuracy, enhancing the generalization ability of the classification model, and reducing the cost of acquiring hyperspectral data in industrial settings.

The disclosed technology provides a system and method for allocating frequency bands to a mobile device or user equipment (UE) based on priorities assigned to the different frequency bands. When no priority is assigned to the frequency bands, when a special or reserved priority is assigned, or when equal priority is assigned, the frequency band allocation to the UE is based on a default frequency allocation algorithm (e.g., based on a relative bandwidth of the different frequency bands). When a UE is capable of utilizing a first and a second frequency band, and the priority assigned to the first frequency band is higher than the priority assigned to the second frequency band, the network (e.g., eNB / gNB) overrides the default algorithm and preferentially allocates the first frequency band to UE even when the first frequency band has a smaller bandwidth than the second frequency band or when the default algorithm would otherwise prefer the first frequency band.

The invention provides a soil organic matter content prediction method based on hyperspectrum, and relates to the technical field of hyperspectrum prediction. The method comprises the following steps: firstly, collecting and treating a soil sample, and carrying out SOM content measurement and soil spectral reflectivity measurement; the soil spectral reflectivity data are preprocessed, and spectral noise and redundant information are eliminated; secondly, preliminarily screening characteristic wave bands by adopting an improved spotted serow optimization ISHO algorithm, and then carrying out secondary screening on the characteristic wave bands by utilizing an iterative reserved information variable IRIV algorithm; and finally, establishing a hyperspectral prediction model to predict the SOM content, and evaluating the precision of the model. According to the method, the ISHO algorithm and the IRIV algorithm are adopted to perform characteristic wave band selection on the spectral reflectivity data, and the XGBoost method is adopted to establish the SOM content prediction model, so that the precision of the prediction model is improved, and theoretical and technical support is provided for SOM content monitoring.

The invention discloses a multi-scale heterogenous remote sensing data collaborative water quality inversion method, computer equipment and a medium, and belongs to the technical field of water quality remote sensing monitoring. S3, an inversion model is constructed, and the step S3 comprises the steps of S31, wave band combination optimization and correlation analysis; s32, constructing a multi-scale heterogeneous remote sensing data collaborative fusion inversion model; and S33, multi-source data collaborative wave band selection and model determination. According to the method, high-frequency and high-spatial-resolution water quality inversion is realized, the spatial-temporal resolution and precision are improved, a more comprehensive and accurate water quality inversion model is constructed, the robustness of the model in a turbid water body and an eutrophicated water body is remarkably improved, the adaptability to a complex water body is enhanced, and the method is suitable for large-scale popularization and application. An efficient and economical technical means is provided for water quality inversion in a complex water body environment, and the method has remarkable low cost and universality.

The application provides a thermal infrared hyperspectral characteristic wave band selection method based on an improved particle swarm algorithm, and comprises the following steps: step 1, preparing a mixed salt ore sample, collecting a hyperspectral emissivity spectrum of the mixed salt ore sample, measuring the mass content percentage of each mixed salt mineral in the mixed salt ore sample as a data set for selecting a characteristic wave band and predicting a content; step 2, taking the data set in step 1 as input, and establishing a prediction model of a characteristic wave band combination of each substance in the mixed salt ore and corresponding content prediction based on the improved particle swarm algorithm; step 3, based on the prediction model in step 2, predicting the characteristic wave band combination of each substance in the unknown mixed salt ore and the corresponding content, collecting the content percentage of the mixed salt ore and verifying the result. The improved particle swarm algorithm does not need to artificially adjust the parameters of the algorithm, and the obtained wave band combination prediction result is stable.

The invention discloses a high-throughput remote sensing estimation method and system for a crop leaf area index based on an unmanned aerial vehicle image and application, and relates to the technical field of crop recognized.The high-throughput remote sensing estimation method for the crop leaf area index based on the unmanned aerial vehicle image comprises the following steps that multispectral or hyperspectral remote sensing image data of a target crop is obtained; the image data comprises a plurality of spectral bands; constructing a multiband vegetation index; optimizing the wave band combination of the multiband vegetation index by adopting an optimization algorithm; based on the optimal wave band combination, constructing a random forest regression model for predicting a leaf area index LAI of the target crop; and outputting an LAI prediction result. According to the crop leaf area index estimation method based on the multiband vegetation index, the fitting degree of the model is remarkably improved, and through the accurate band selection and optimization process, the utilization efficiency of data is improved, and the stability and accuracy of a prediction result are enhanced.