443 results about "Multispectral data" patented technology

The invention discloses a multi-sensor fusion unmanned equipment agricultural monitoring method and system, and belongs to the technical field of agricultural monitoring, and the method specifically comprises the steps: obtaining a farmland image and multispectral data through an image shooting device and a multispectral sensor carried by an unmanned aerial vehicle, and recognizing the features of diseases and pests through a deep learning model, generating a distribution diagram and statistical data, and performing early warning and risk assessment in combination with the multispectral data; formulating a pesticide management strategy according to an evaluation result, and pushing the pesticide management strategy to an unmanned equipment control system in real time; meanwhile, according to actual and operation requirements of the farmland, generating an optimal operation path by using a path planning algorithm, setting operation parameters, and allocating tasks by using a task allocation algorithm; in the operation process, environment changes are monitored in real time, an edge computing device is used for finding out abnormity in time and adjusting operation instructions, and efficient and accurate monitoring and intelligent operation management of farmland diseases and insect pests are achieved.

The invention relates to the technical field of remote sensing monitoring, in particular to an intelligent forest disease and pest monitoring method and system based on unmanned aerial vehicle remote sensing, and the method comprises the following steps: collecting multispectral data by an unmanned aerial vehicle, extracting reflectivity and smoothing the reflectivity, carrying out differential recognition on abnormal pixels, extracting curves and screening significant changes, segmenting scab boundaries, and classifying health states. And generating a pest and disease map layer prediction trend. According to the method, the reflectivity time sequence is constructed, differential processing is carried out, the vegetation change trend is dynamically captured, abnormal areas are identified by combining slope offset and persistence analysis, significant pixels are screened according to main peak wavelength offset, boundary information is extracted, the scab positioning precision is improved, and the recognition resolution of the lesion state is enhanced through reflectivity combined analysis; accurate description of disease spot dynamic changes is realized, static image dependence limitation is broken through, monitoring time continuity and space response capability are enhanced, and disease and insect pest change capture efficiency and state classification accuracy are effectively improved.

The invention discloses an online detection system and method based on ink-jet printing, and relates to the technical field of ink-jet printing, and the method comprises the steps: collecting the operation state data of ink-jet printing in real time, and transmitting the data to a deep learning model; predicting the track, drop point and shape change of the ink droplet through a deep learning model in combination with a fluid dynamics principle, generating a prediction result, and providing positioning information of an ink jet area; inputting the multispectral data into a convolutional neural network model, identifying color deviation and defect types in combination with a prediction result, and generating corresponding correction parameters; the correction parameters are transmitted to ink jet control equipment, and the jet parameters of the nozzle are adjusted in real time; according to the invention, the information of the boundary, the central point, the area, the coverage rate and the offset of the ink jet area is generated in real time, and a high-precision positioning basis is provided for subsequent multispectral detection and defect correction, so that efficient and accurate online detection and ink jet quality control are realized.

The invention relates to the technical field of hydraulic engineering intelligent operation and maintenance, and discloses an AI unmanned aerial vehicle-based auxiliary decision-making platform for hydraulic engineering operation and maintenance, and the platform comprises a data collection module which is used for obtaining multi-modal data of a water conservancy facility through an AI unmanned aerial vehicle, the multi-modal data comprises a visible light image, thermal imaging data, multispectral data and laser radar data; the data processing and fusion module is used for carrying out formatting processing, space-time alignment and feature fusion on the multi-modal data; the health assessment and prediction module is used for assessing the health state of the water conservancy facility based on the processed data and predicting the future change trend of the facility state; and the auxiliary decision-making module is used for generating inspection priority planning of the water conservancy facilities. According to the invention, through multi-source data fusion, dynamic risk assessment and intelligent optimization distribution, the effects of accurate monitoring, efficient operation and maintenance and risk visual management of the whole life cycle of the water conservancy facilities are realized.

The invention relates to the technical field of building information modeling (BIM), in particular to a three-dimensional modeling method and system of a high-altitude curtain wall, multispectral data, structured light scanning data and thermal imaging data are collected through an unmanned aerial vehicle, the data are subjected to multidimensional analysis and fusion, three-dimensional modeling is performed through a multi-modal feature fusion algorithm, and the three-dimensional modeling method and system of the high-altitude curtain wall are obtained. And carrying out global and local geometric correction, material feature optimization and thermal feature correction on the model by utilizing a dynamic feedback and recursive optimization mechanism. And finally, generating high-precision three-dimensional model data through a multi-channel fusion algorithm. The method effectively solves the problems of insufficient geometric precision, limited material processing and neglect of thermal characteristics in the prior art, remarkably improves the precision and reality of the model, and is suitable for the fields of building design, structural analysis, energy consumption evaluation and the like.

The invention discloses an amphibious unmanned aerial vehicle riverway sediment concentration real-time monitoring method based on underwater light field imaging and cross-modal fusion, and relates to the technical field of riverway sediment monitoring. Comprising the following steps: acquiring aerial multispectral data and preprocessing the aerial multispectral data; an integrated polarized light field camera is carried on an amphibious unmanned aerial vehicle to capture underwater light field data, and underwater three-dimensional light field information is reconstructed and sediment scattering characteristics are extracted through an optical model and a lightweight deep learning network; the air multispectral data and sediment scattering characteristics are fused through space-time alignment and an attention mechanism, a sediment concentration prediction model is generated, and then a spatial distribution diagram of the river sediment concentration is output; and carrying out real-time monitoring and early warning on the river sediment concentration according to the spatial distribution diagram of the river sediment concentration through edge calculation and cloud collaboration architecture. The method is combined with an advanced deep learning algorithm, high-resolution silt concentration inversion can be realized, and the method has real-time performance and adapts to a complex water body environment.

The invention relates to the field of forest protection, and discloses an unmanned aerial vehicle intelligent monitoring system for forest protection. Comprising a multispectral data dynamic acquisition module, an environment field real-time modeling module, an image intelligent enhancement module, a multi-scale frequency domain feature extraction module, a Bayesian anomaly probability inference module, a space-time correlation verification module, an unmanned aerial vehicle cluster scheduling module and a heterogeneous hardware acceleration module. According to the system, real-time monitoring and accurate identification of the hidden ecological damage behavior are realized through a closed-loop process of multi-modal sensor collaborative acquisition, environmental parameter dynamic prediction, image illumination invariance transformation, cross-scale texture fingerprint extraction, particle filter dynamic threshold optimization, multi-constraint space-time clustering, resource allocation optimization and reconfigurable hardware acceleration. According to the invention, on the basis of an image preprocessing architecture based on illumination reflection separation and edge preserving enhancement, the feature distortion bottleneck of a traditional algorithm in a complex illumination environment is broken through, and a high-fidelity data base is provided for hidden ecological damage detection.

The invention discloses an intelligent management method for liquidation receipts based on multi-modal data fusion, and particularly relates to the field of data analysis. Comprising the steps of S1, multispectral data acquisition in a limited illumination environment, S2, cross-modal feature decoupling and recombination, S3, space-time heterograph neural network analysis, S4, multi-scale attention decision fusion, S5, resistance enhancement verification, and S6, incremental management based on knowledge distillation. According to the method, the physical anti-counterfeiting capability is remarkably improved, the paper material, the ink components and the surface structure are deeply analyzed through a multispectral sequence acquisition mechanism, and hidden tampering behaviors such as color fading and chemical altering of the thermo-sensitive paper are accurately identified. Cross-modal deep correlation analysis is achieved in a breakthrough mode, a physical-semantic decoupling technology and a space-time heterogeneous graph network are adopted for modeling, and non-dominant laws such as commodity position offset and tax rate anomaly are effectively captured.

The invention discloses a zinc coating shedding performance detection method and system, particularly relates to the technical field of metal surface treatment quality detection, and is used for solving the problems of high misjudgment rate and insufficient detection precision caused by coating surface pseudo defect interference in the existing method. The method comprises the following steps: synchronously acquiring a multispectral reflection image and three-dimensional morphology data of the surface of a zinc coating through an industrial vision imaging device, extracting grain boundary distribution information based on multispectral data, and identifying radial pseudo defects in combination with a curvature manifold geodesic line energy gradient direction of the three-dimensional morphology; and further performing micro-crack topology analysis and grain boundary spatial correlation verification on the candidate region, eliminating texture interference and grain boundary overlapping regions, and finally outputting a high-confidence fall-off defect detection result, so that the identification capability of real defects under a complex galvanized surface is remarkably improved, and the method is suitable for automatic detection requirements of a high-speed continuous production line.

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The invention provides an automatic intelligent defect analysis method and system based on power transmission and distribution line unmanned aerial vehicle image acquisition. The method comprises the following steps: acquiring infrared thermal imaging time sequence data and multispectral imaging data of the surface of a wire through an unmanned aerial vehicle carrying an infrared thermal imager and a multispectral camera; calculating a temperature gradient direction of a conductor main body area, screening an abnormal hot spot area whose axial included angle with the conductor is smaller than a preset angle, and positioning an oxidation area boundary by using a spectral absorption attenuation curve of an oxidation layer in the multispectral data; performing space matching on the hot spot migration direction and the oxidation boundary to generate a visual map; establishing a dynamic association rule according to an included angle variable quantity of a hot spot path in the map and a spectrum abrupt change point in the oxidation region; according to the critical angle threshold value and the sudden change point number threshold value, the defect grade is judged, and a defect analysis report is generated. According to the technical scheme provided by the invention, the automation degree and accuracy of defect detection are improved.

The invention discloses a shoal plastic garbage unmanned aerial vehicle monitoring method based on map characteristics. The method is characterized by comprising the steps of carrying out aerial photography modeling on a multispectral unmanned aerial vehicle, preprocessing visible light and multispectral data, training an instance segmentation model to detect garbage targets and generate masks at the same time, and extracting image and spectral features of each garbage target; and classifying and identifying each garbage in combination with the image and spectral features, and establishing a mass-occupied area model of each garbage for stock estimation, and the like. According to the method, the image features reflecting the morphology difference of the plastic products and the spectral features reflecting the material difference are extracted based on the instance segmentation model, the recognition dimension of the plastic garbage is increased, and compared with the prior art, multi-dimensional high-precision plastic garbage type recognition is achieved by combining the image features and the spectral features; and the plastic garbage stock estimation corresponding to the category is realized based on the identification result, and the method has good application prospects and commercial development values.

The invention relates to the technical field of vegetation remote sensing recognition, in particular to a multispectral and visible light remote sensing image fusion segmentation method and system and a medium. The method comprises the following steps: constructing a double-branch model comprising a visible light branch encoder, a multispectral branch encoder, a multi-scale feature fusion module and a shared decoder; inputting the visible light data into a visible light branch encoder to obtain a spatial feature map; inputting the multispectral data into a multispectral branch encoder to obtain a spectral feature map; the multi-scale feature fusion module applies a channel-space joint attention weight to an input feature map, then performs channel splicing according to scales, compresses the feature map to an original channel number by adopting 1 * 1 convolution, and then applies a channel attention weight to obtain a multi-scale fusion feature sequence; and the shared decoder analyzes the multi-scale fusion feature sequence and outputs an invasive plant segmentation mask image of the target detection area, so that high-robustness invasive plant identification under a complex background is realized.

The invention relates to the technical field of neural networks and machine learning, in particular to a lithium ore multi-component content prediction and detection optimization system based on machine learning. The system comprises a multispectral data fusion and preprocessing part, a correction and interference elimination part and a machine learning model prediction part. The multispectral data fusion and preprocessing part is used for performing fusion and normalization processing on the first light intensity signal and the second light intensity signal to obtain a fusion signal; the correction and interference elimination part is used for performing spectral line overlapping interference elimination processing on the fusion signal to obtain an interference elimination signal, and performing matrix effect correction on the interference elimination signal to obtain a correction signal; the machine learning model prediction part is used for performing feature extraction on the correction signal by using a preset machine learning model to obtain features; based on the feature response function, an initial content of each element is predicted. According to the invention, high-precision and intelligent detection of multiple components of the lithium ore is realized.

The invention relates to the technical field of data processing, and further relates to a forest ecology monitoring system and method with an unmanned aerial vehicle carrying a multispectral sensor. The system comprises an unmanned aerial vehicle, a hyperspectral data processing device and an ecological health detection platform, the unmanned aerial vehicle carries a multispectral sensor so as to acquire multispectral data of a forest area and perform atmospheric correction on the surface reflectance; the hyperspectral data processing device is used for reconstructing hyperspectral data from the multispectral data by using a compressed sensing theory and carrying out noise reduction by using wavelet transform to obtain the hyperspectral data after noise reduction; and the ecological health detection platform is used for calculating forest health indexes and forest carbon reserves according to the hyperspectral data after noise reduction, and presenting the forest health indexes and the forest carbon reserves to users. According to the method, comprehensive and accurate evaluation of the health condition and the carbon reserve of the forest ecosystem is realized, and the defects in the aspects of data precision, processing efficiency and evaluation comprehensiveness in the prior art are overcome.

The invention relates to the technical field of remote sensing technology and forest ecological system carbon sink monitoring, and particularly discloses a forest carbon sink dynamic monitoring method based on near-surface multi-source remote sensing data, which comprises the steps of S1, data acquisition, S2, data processing, S3, forest change information monitoring, S4, forest carbon reserve estimation, S5, forest carbon sink dynamic analysis and S6, result verification and uncertainty analysis. By integrating multi-source information such as the multispectral data, the laser radar data and the field survey data, the structure and functional characteristics of the forest ecosystem can be comprehensively and accurately reflected. Compared with a single data source, the comprehensive data acquisition mode has the advantages that the precision and reliability of forest parameter extraction can be remarkably improved, and a solid foundation is provided for subsequent forest carbon reserve estimation and carbon sink dynamic analysis.

The invention relates to the technical field of information, in particular to an intelligent agricultural information big data intelligent acquisition management system and method. The method comprises the following steps: acquiring farm sensing data, and performing crop growth analysis according to the farm sensing data so as to obtain crop growth data; performing crop health state feature extraction and crop physiological feature extraction according to the crop growth data so as to obtain crop health state data and crop physiological data; performing disease and pest detection according to the crop health state data and the crop physiological data to obtain crop disease and pest data; the method comprises the following steps: acquiring farm remote sensing data, performing crop multispectral analysis according to the farm remote sensing data so as to obtain crop multispectral data, and performing crop moisture state evaluation according to the crop multispectral data so as to obtain crop moisture state data. The pesticide spraying strategy is optimized based on the information technology, and the agricultural production management efficiency is improved.

The invention discloses a water body remote sensing recognition method based on hyperspectral data simulation and U-net. The water body remote sensing recognition method comprises the following steps: A, data collection and preprocessing; b, selecting and manufacturing a sample area; c, data simulation; d, derived parameters are calculated; e, manufacturing a training sample and a verification sample; f, training a U-net network model; g, multispectral water body detection; and H, precision evaluation. According to the method, a high-quality training sample is obtained through a hyperspectral remote sensing image, multispectral data of different sensors are simulated, the problem that the reusability of water body classification samples among different multispectral satellites and at different geographic distances and time is not sufficient is solved, the multispectral data Landsat8 and Sentinel2 are taken as examples, and the reusability of the water body classification samples is improved. It is proved that by means of the method, easily-confused ground objects such as building shadows and cloud shadows in the water body recognition process can be effectively removed, and on the basis of accurately extracting large-area rivers and lakes, the recognition precision of small lakes and ponds is improved.

The invention provides an ocean red tide early warning method based on remote sensing inversion and a time-space map convolutional network. The ocean red tide early warning method comprises the following steps: collecting hyperspectral data and multispectral data; respectively carrying out data quality inspection on the collected hyperspectral data and multispectral data; preprocessing the hyperspectral data and the multispectral data after the quality inspection is qualified to respectively obtain a hyperspectral result image and a digital orthoimage; analyzing the hyperspectral result image and the digital orthoimage to obtain remote sensing inversion data of water temperature, salinity, total nitrogen, total phosphorus and chlorophyll a, and generating a gradient distribution thematic map of each index; according to water temperature, salinity, total nitrogen, total phosphorus, chlorophyll a and historical monitoring data, in combination with a change trend of key environmental parameters, introducing a space-time diagram convolutional network model, simulating a red tide diffusion situation, setting a risk threshold, and performing risk prediction on red tide occurrence through a red tide occurrence risk prediction model; and updating the red tide occurrence risk prediction model based on buoy monitoring and field monitoring data.

The invention relates to the technical field of crop remote sensing monitoring, and provides a crop and bean mixed planting crop growth estimation method and device based on a multispectral technology, and the method comprises the steps: collecting multispectral data of a mixed planting region, calculating a vegetation index and a texture index according to the multispectral data, and determining the biomass and nitrogen content of crops in the mixed planting region. Based on a general linear regression algorithm and a partial least squares regression algorithm, constructing a plurality of estimation models for estimating biomass, screening out a target estimation model from the plurality of estimation models, and estimating the growth condition of crops in the mixed planting region through the target estimation model. According to the method, an estimation model with high estimation precision is established by combining texture indexes and vegetation indexes which express space detail difference characteristics, and the estimation model is established for a mixed planting region with complex crop object phenotypic characteristics and high estimation difficulty, so that the biomass and the nitrogen content of the mixed crop of rice and beans are estimated losslessly, efficiently and accurately.

The invention relates to the cross technical field of remote sensing technology and ecological environment monitoring, and discloses an ecological sensitive area monitoring method based on multispectral remote sensing, which comprises the following steps: multispectral data acquisition: performing multispectral data acquisition on a target ecological sensitive area by using an unmanned aerial vehicle carrying a multispectral camera, the multispectral camera at least comprises a plurality of wave bands such as visible light and near-infrared light; data preprocessing: preprocessing the acquired multispectral data, including radiation correction, image registration and orthoimage generation, and converting an original DN value into reflectivity data by radiation correction; and vegetation coverage extraction: extracting the vegetation coverage by using a normalized vegetation index based on the preprocessed multispectral data. The ecological sensitive area monitoring method based on multispectral remote sensing aims at solving the problems that a traditional ecological sensitive area monitoring method is limited in monitoring range, insufficient in data precision, poor in timeliness, bottleneck in data processing and analysis and the like.

The invention relates to the technical field of robot reconnaissance, and discloses a tracked robot intelligent reconnaissance system based on panoramic vision and multispectral analysis, which comprises the following steps: dynamically adjusting sensor parameters based on environmental spectral characteristics to adapt to different light conditions, and collecting high-quality panoramic vision and multispectral data; performing panoramic vision fusion processing on the acquired data to compensate light change in a complex environment and enhance image quality; performing multispectral data analysis according to the fused data, and classifying target materials based on an analysis result to improve reconnaissance accuracy; and a relevance optimization algorithm of visual data and spectral information is combined to filter interference in multi-target reconnaissance and feed back and correct path planning of the robot in real time. Target materials are classified according to a multispectral data analysis result to solve the problem of insufficient target recognition accuracy in the reconnaissance process, and the success rate and high efficiency of a reconnaissance task are ensured by combining machine learning with a prediction algorithm.

A low-light environment multispectral pedestrian detection method based on a cross-modal attention fusion network comprises the following steps: constructing a pedestrian detection network comprising a double-flow feature extraction backbone network (CSPDarknet53), a multi-model aggregation Transformer network (MFT), a double-model feature aggregation network (DMFF) and a detection head at an offline stage, training by adopting a multispectral data set, and then, carrying out multi-spectral detection on the pedestrian detection network; and performing real-time image processing through the trained pedestrian detection network in the online stage. According to the method, better pixel-level attention can be generated for small objects, and feature representation under different scales is enhanced.

According to the unmanned aerial vehicle multi-source quantitative remote sensing rape growth state evaluation method, rape is divided into a seedling stage, a flowering stage and a legume stage according to growth nodes, ground hyperspectral and multi-spectral data are synchronously obtained, resolution is increased and decreased, the data are converted into all-dimensional, multi-scale and multi-temporal data, a growth state factor inversion model of the rape is established, and the rape growth state is evaluated. Evaluating the contribution of different growth state factors to the growth vigor of the rape, and evaluating the growth state of the rape based on comprehensive evaluation indexes of leaf area index, overground biomass and chlorophyll content. The method comprises the following steps: establishing a model based on multi-layer linear regression and partial least square optimization models of three types of growth factors LAI, AGB and CC, multi-mode prediction based on entropy aggregation and hierarchical analysis of entropy loading expert knowledge in combination with expert knowledge and agronomic knowledge, and setting a model with an optimal verification index in different growth periods as a growth state evaluation model in the period. The rape growth state evaluation period is short, the pertinence is strong, and the accuracy is good.

This invention relates to a pair of intelligent, multimodal smart eyeglasses designed for adaptive vision, biometric ocular monitoring, and predictive emergency response. The system integrates multilayer lenses filled with electrochromic gel and surrounded by elastic structures to enable dynamic focal length modulation in response to user visual behavior. An array of sensors—including visible light, ultraviolet, micro-cameras, infrared emitters, gyroscopes, and ultrasonic transceivers—collects multispectral data to evaluate environmental and physiological parameters. Real-time data processing is performed by an embedded quantum microcontroller, which manages light transmittance, detects eye fatigue, maps ocular surfaces, and identifies hemodynamic abnormalities such as fainting, seizure, or ocular stroke. Advanced imaging techniques, such as ultrasonic ptychography and wave diffraction analysis, enable non-invasive 3D mapping of ocular layers and internal eye motion. The system also supports wireless communication with external platforms including smartphones, vehicles, and brain-machine interfaces, enabling proactive safety interventions. These features work in tandem to support early diagnosis of critical conditions, protect vision, and personalize user experiences through machine learning and adaptive feedback mechanisms.

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

The invention provides an automatic online detection method and system for the surface smoothness of a precise injection mold, and relates to the field of precise manufacturing, the method comprises the following steps: S1, synchronously collecting visible light and near-infrared reflection images of the surface of the mold through multispectral coaxial imaging; s2, calculating a dynamic texture parameter based on a multi-directional gradient field weighted variance, and generating a composite reflection parameter in combination with wavelet domain fusion; s3, fusing the features by adopting a dual-path neural network, and comparing the standard library through a modified Stereer ratio model to output the degree of finish; and S4, adjusting the period in real time according to the detected dynamic standard deviation and linking closed-loop control. According to the method, the restriction of single-band characterization is broken through through multispectral data fusion, and the processing speed is increased by utilizing gradient field parallel computing hardware; in combination with a self-adaptive threshold value and a process parameter dynamic correction mechanism, the problems of poor adaptability of a fixed period, high hardware delay and single feature dimension of a traditional method are solved, and the online detection precision and the process control efficiency are remarkably optimized.

The invention provides a rice yield prediction method and system based on multi-source time sequence remote sensing data, and relates to the field of agricultural informatization and remote sensing technology application, and the method comprises the steps: collecting multi-spectral data and laser radar data, and carrying out the precise geographic positioning of the data through a high-precision satellite positioning system, obtaining multi-source unmanned aerial vehicle remote sensing data; performing camera internal orientation, wave band registration, orthoimage splicing and embedding, radiation correction and remote sensing index calculation on the multispectral data to obtain processed multispectral data; and performing point cloud reconstruction and point cloud filtering on the laser radar data, and calculating the height of the rice plant to obtain processed laser radar data. According to the method, multi-source remote sensing data and spatio-temporal data are fused, and remote sensing characteristics of different phenological periods of rice are dynamically analyzed and filtered, so that the precision and reliability of rice field yield prediction are remarkably improved.

The invention belongs to the technical field of crop growth prediction, and particularly relates to a crop growth prediction method and system based on multispectral unmanned aerial vehicle monitoring. The method comprises the following steps: acquiring multi-dimensional data of real crops in different growth stages under different soil water contents and disease and insect pest states, determining the contribution degree of each vegetation index to crop growth through a factor analysis algorithm, carrying out dimension reduction on hyperspectral data by using a principal component analysis algorithm, fusing with the multi-spectral data, constructing a multi-spectral resolution characteristic space, and carrying out multi-spectral analysis on the hyperspectral data. The method comprises the following steps: firstly, obtaining a real plant height growth fitting function of crops through analogue simulation by combining LiDAR data and vegetation indexes, thirdly, calculating a real growth vegetation index space and obtaining a real growth state space of a standard staged growth period, and finally, inputting the calculated space and function into a model constructed by a reinforcement learning algorithm for training, and accurate prediction of the crop growth state is realized.

The invention provides a multispectral image spectral super-resolution method based on adaptive learning and application, and belongs to the technical field of remote sensing image processing. The method solves the problems that in an existing spectrum super-resolution method, manual setting of prior information and complex parameter selection are carried out, in a deep learning method, pixel dependence between space long distances and self-similarity between spectrums are ignored, and deep network parameters cannot be dynamically adjusted in real time. Constructing a pseudo label based on the multispectral data set; constructing a spectrum super-resolution network, and performing supervised training on the spectrum super-resolution network based on a pseudo tag to generate a preliminary reconstructed image; inputting the preliminary reconstructed image into an adaptive learning module, learning the residual error of the preliminary reconstructed image and the real image, and performing residual error correction; constructing an adaptive learning network, and updating parameters of an adaptive learning module; and carrying out residual connection on the adaptive learning network after parameter updating and a to-be-processed image, and outputting a target high-spatial-resolution hyperspectral image.