1555 results about "Spectral image" patented technology

A system and method are disclosed for generating hyperspectral images from multi-modal sensor data including RGB, LiDAR, thermal, and near-infrared inputs. Training data includes hyperspectral images and corresponding multi-modal measurements. Spectral band grouping is performed based on correlation coefficients. A multi-modal decomposition network with cross-modal attention mechanisms generate reconstructed hyperspectral images by fusing complementary sensor information. A fine-tuning network creates reconstructed RGB images. A comprehensive quality assurance system analyzes spectral consistency, cross-modal coherence, and fusion artifacts to generate quality metrics. Missing data compensation strategies handle corrupted sensor inputs using information from other modalities. The system includes temporal integration for video sequences and multi-resolution processing for different sensor resolutions. Quality metrics guide network weight adjustments to improve reconstruction accuracy while maintaining robustness to sensor failures and environmental variations.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing image ocean green tide monitoring method and system fusing physical prior and spatio-temporal evolution. The method comprises the following steps: acquiring a multi-modal remote sensing monitoring image; performing multi-modal feature extraction on the acquired image, wherein the multi-modal feature extraction comprises spectral reflectivity feature extraction, ocean dynamics feature extraction and feature alignment and unified representation; establishing a physical prior of a green tide characteristic wave band by using an ocean optical radiation transmission model; constructing a dynamic space-time diagram based on the extracted multi-modal features to obtain a node global feature vector and a dynamic adjacency matrix; carrying out adaptive graph convolution feature coding based on physical prior and a dynamic space-time diagram; through fusion of multi-spectral images of multiple platforms such as satellites and unmanned aerial vehicles and ocean dynamic data and combination of atmospheric correction and wave band resampling, consistency processing and high-precision extraction of multi-source features are realized, and comprehensiveness and reliability of green tide feature recognition are remarkably improved.

The present invention relates to the technical field of image restoration, and in particular to a remote-sensing hyperspectral image super-resolution reconstruction method based on a hypergraph neural network, comprising: S1, acquiring a hyperspectral image, and preprocessing the hyperspectral image to obtain a training set and a verification set; S2, constructing a hypergraph neural network; S3, using training images in the training set to construct a three-layer hypergraph, and on the basis of the three-layer hypergraph, constructing hypergraph operators corresponding to the training images; S4, using the training images and the hypergraph operators corresponding to the training images to train the hypergraph neural network, and using a loss function to iterate network parameters of the hypergraph neural network, to obtain a trained hypergraph neural network; and S5, inputting, to the trained hypergraph neural network, low-resolution hyperspectral images to be reconstructed for reconstruction to obtain a high-resolution hyperspectral image. The present invention has an excellent reconstruction result.

The invention relates to the technical field of image processing, in particular to an image acquisition and analysis method and system, and provides the following scheme: obtaining a visible light and near-infrared multispectral image, generating a spectral difference image, and performing weighted fusion to obtain a first image; segmenting a target region based on the fused saliency map, and calculating a pixel reflectance ratio; solving a color mapping matrix according to the reflectance ratio, and carrying out color correction on the target region to obtain a standardized feature image; and extracting characteristic parameters such as spectrums, colors and textures, inputting the characteristic parameters to a multi-branch convolutional neural network, fusing the characteristic parameters through an attention mechanism, and outputting a state classification result and a quantitative index. The cross-spectral imaging difference can be adaptively compensated, and the fusion precision and the analysis stability are improved.

The invention provides a mountainous area tunnel crack intelligent identification and detection method based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: collecting multispectral image data, and carrying out the adaptive preprocessing to obtain an enhanced feature map; a double-flow network is combined with a space-channel cascade attention module to extract fusion features; constructing multi-scale feature representation through a feature association graph network; training the network by adopting a joint optimization target; and morphological processing and connectivity analysis are carried out to realize accurate identification and classification of the crack. According to the invention, the detection precision and the anti-interference capability of the tunnel crack in the complex environment are improved.

The invention discloses a wharf shoreline terrain reconstruction method based on remote sensing data, and belongs to the technical field of three-dimensional terrain reconstruction. The method comprises the following steps: acquiring a multi-temporal multispectral remote sensing image, a synthetic aperture radar image, a laser radar point cloud and synchronous tidal water level data of a target wharf area; processing the multispectral image to generate an initial land and water segmentation map; carrying out tide correction by utilizing the tide water level data, fusing the multi-polarization characteristics of the synthetic aperture radar image, and correcting the ground feature type to obtain an accurate land and water boundary diagram; processing the laser radar point cloud to generate a digital surface model, extracting a shoreline contour with sub-pixel-level precision from the digital surface model, and endowing the shoreline contour with an elevation value; and superposing the shoreline contour with the elevation with the multispectral image to generate a three-dimensional terrain model. According to the method, the problems of low terrain reconstruction precision, large tide influence and insufficient automation degree of a traditional method in a complex wharf environment are solved, and high-precision and high-efficiency wharf shoreline three-dimensional automatic reconstruction is realized.

The invention relates to the technical field of power distribution station inspection, and discloses a power distribution station automatic inspection method based on image recognition. According to the method, multi-spectral image data of multiple areas in a power distribution station are collected in real time, and a multi-channel feature tensor of an equipment state is generated through a feature extraction network; performing feature fusion of space and frequency spectrum dimensions on the multichannel feature tensor by using a multi-scale convolutional attention network, and outputting an enhanced device feature map; inputting the data into a cascade anomaly detection module, positioning an equipment surface defect region by adopting a region segmentation algorithm, and analyzing and generating a defect evolution trend vector in combination with time sequence characteristics; on the basis of the vector, probability distribution of equipment fault risks is predicted through a space-time propagation model, and a dynamic risk field is generated; and finally, constructing a self-adaptive early warning decision tree for the dynamic risk field, and generating an inspection maintenance instruction according to risk probability threshold grading. According to the method, automation and intelligentization of power distribution station inspection are realized, and support is provided for efficient maintenance of the power distribution station.

The invention relates to a hyperspectral image classification method and device based on a state space model. The hyperspectral image classification method based on the state space model comprises the following steps: sequentially carrying out feature extraction and serialization processing on hyperspectral image data to obtain a shallow feature projection vector; performing global-local feature extraction on the shallow feature projection vector by adopting a neural network based on a state space model to obtain a fused feature projection vector; and carrying out pixel-by-pixel classification and dimension rearrangement on the hyperspectral image data in sequence to generate a classification result of the hyperspectral image data. According to the hyperspectral image classification method based on the state space model, long-range dependence modeling is achieved through the neural network based on the state space model with linear complexity, the calculation complexity is effectively reduced, and through feature fusion and residual error connection, the classification accuracy of the hyperspectral image is improved. And the perception capability of the neural network on different scale space-spectrum structures in the hyperspectral image is effectively enhanced.

The invention discloses a panchromatic sharpening method and system based on cross-resolution adversarial learning and a Mama network. The method comprises the following steps: acquiring a low-resolution multispectral image LRMS and a high-spatial-resolution panchromatic image PAN; extracting features of the low-resolution multispectral image LRMS to obtain a first feature map; extracting features of the high-spatial-resolution panchromatic image PAN to obtain a second feature map; performing wavelet decomposition and weighted fusion on the first feature map and the second feature map to obtain a multi-channel feature map, and performing dynamic fusion on the multi-channel feature map through a cross attention gating mechanism to obtain a fused feature map; and carrying out residual connection on the fused feature map and the low-resolution multispectral image, and then carrying out feature reconstruction to obtain a high-resolution multispectral remote sensing image. According to the method, the panchromatic sharpening performance of the remote sensing image can be improved, and the high-quality and high-resolution multispectral remote sensing image is obtained.

The invention relates to an infectious bacterium detection method and system based on double effects of a reflection spectrum and autofluorescence, and belongs to the field of biomedical detection. The method comprises the following steps: acquiring hyperspectral images of different samples under different light sources; performing data preprocessing on the collected sample hyperspectral image, realizing pixel-level alignment of the dual-effect image based on feature matching and image registration, and constructing a data set; the method comprises the following steps: constructing a wound infection bacterium detection model based on double effects of a reflection spectrum and autofluorescence, and comprising a double-branch feature extraction module which comprises a reflection branch and a fluorescence branch which are respectively used for extracting reflection hyperspectral image features and fluorescence hyperspectral image features; the redundant feature screening module screens the features extracted by the reflection branch and the fluorescence branch through a channel attention mechanism; and the feature fusion module adopts a cross-branch cross attention mechanism to fuse the features of the double branches. According to the method, learning is carried out from two physicochemical characteristics, and the effectiveness and adaptability of the technology are improved.

The invention discloses a regional termite detection method and system based on multispectral fusion, and the method comprises the steps: carrying a multi-mode spectrum collection device through a movable detection platform, and synchronously collecting a visible light image, a near-infrared hyperspectral image and Raman spectrum data of a to-be-detected region; preprocessing and feature extraction are carried out on the modal data, and the modal data are converted to a frequency domain to obtain frequency features; based on the physical and biochemical characteristics of the termites and the nests thereof, calculating the matching degree and the credibility of each modal feature, and performing adaptive weighted feature fusion according to the matching degree and the credibility to generate comprehensive spectral features; inputting the fusion features into a pre-trained termite identification and risk assessment model to realize precise identification, positioning and threat level assessment of termite individuals, termite paths and nests; and carrying out trajectory analysis on termite activities by combining a multi-target tracking algorithm, and giving out early warning based on identification and tracking results. According to the invention, early-stage, lossless and accurate detection and active early warning of termites are realized, and the detection efficiency and reliability are significantly improved.

A pharmaceutical hyperspectral reconstruction method based on a coded aperture snapshot spectral imaging (CASSI) system includes: collecting and processing original pharmaceutical hyperspectral images to obtain augmented pharmaceutical hyperspectral images; performing simulated spatial encoding on the augmented pharmaceutical hyperspectral images to obtain encoded measurement images; performing spectral inverse shift on the encoded measurement images, then performing inverse encoding to obtain inversely encoded three-dimensional hyperspectral images, using the augmented pharmaceutical hyperspectral images as target images, and constructing a training set and a testing set according to the inversely encoded three-dimensional hyperspectral images and the target images; constructing a deep symmetric neural reconstruction network, and training and testing the deep symmetric neural reconstruction network; and deploying a tested deep symmetric neural reconstruction network onto the CASSI system, real-time collecting pharmaceutical measurement images using the snapshot coded imaging system, and performing computational reconstruction on the pharmaceutical measurement images to obtain reconstructed three-dimensional hyperspectral images.

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

The invention discloses an augmented reality intelligent navigation and operation quantitative evaluation method and system in an operating room, and the method comprises the steps: collecting multispectral image data and depth point cloud data of an operation region in the operating room in real time, and generating a multidimensional perception data set; performing feature extraction and spatial registration on the multi-dimensional perception data set, and decomposing the dynamic three-dimensional augmented reality model into an anatomical structure feature layer and an instrument interaction feature layer; performing dynamic matching and real-time rendering on the anatomical structure feature layer and the instrument interaction feature layer based on a digital twinning technology to generate augmented reality navigation information and an operation guidance scheme; and performing multi-dimensional quantitative analysis on the surgical operation process according to the augmented reality navigation information and the operation guidance scheme, and outputting a quantitative evaluation result. By utilizing the embodiment of the invention, augmented reality navigation guidance which is synchronous with a real operation scene in real time can be provided, meanwhile, accurate quantitative evaluation is carried out on the operation operation, and the accuracy, the safety and the trainability of the operation are improved.

The invention discloses a skin cancer pathological image analysis system and method based on a Transform model. The system comprises an image acquisition and quality control module, a multi-scale preprocessing module, a hierarchical feature extraction module, an intelligent diagnostic reasoning module, a knowledge graph aided decision-making module, a result output and feedback module and a federal learning training module. According to the method, multi-spectral image acquisition and quality control are carried out, multi-modal enhancement and dyeing standardization preprocessing are carried out, pathological features are extracted in a layered manner by using an improved Transform model, subtype identification and malignancy degree evaluation are realized in combination with multi-task learning, uncertainty is quantified by means of Monte Carlo dropout, cases and guidelines are associated through a knowledge graph, privacy is protected through federal learning, and the model is optimized. According to the scheme, the diagnosis efficiency and accuracy are greatly improved, the model interpretability is enhanced, various clinical scenes are adapted, diagnosis standardization is promoted, and improvement of basic medical capacity is assisted.

The invention relates to the technical field of leaf nitrogen content estimation, in particular to a method for establishing a grassland plant nitrogen content monitoring model based on a remote sensing technology. The method comprises the following steps: controlling an unmanned aerial vehicle platform carrying a narrowband multispectral camera and a linear polarization camera, and collecting a spectral image sequence and a polarization image group at an observation window with a solar zenith angle of 30-40 degrees; extracting a reflectivity value of each wave band in the spectral image sequence, calculating a reflectivity first-order derivative and determining a red edge position offset; extracting horizontal and vertical polarization components in the polarization image group, calculating a polarization degree and constructing a polarization degree difference index; and identifying the center of the fecal spot by searching a pixel with a local maximum polarization degree difference index and red edge position offset blue shift exceeding a preset threshold value. According to the method, excrement spot recognition and remote sensing description of the nitrogen influence range thereof are achieved through double-index complementation of the red edge position offset and the polarization degree difference index.

The invention relates to a disease diagnosis and treatment method, system and device based on ear-nose-throat endoscope images and a medium, and the method comprises the steps: synchronously collecting dual-spectrum images through time sequence triggering, and solving the problem of shielding of an anatomical structure caused by mucus flow; a dynamic mucus displacement field is modeled through pixel gradient, and misjudgment of a traditional segmentation method on static lesions and dynamic secretions is eliminated; a deformable convolutional layer is adopted to correct the spatial offset of white light and a narrow-band image, and the mismatch of a multi-mode characteristic due to optical scattering is overcome; and finally, a real-time surgical navigation mark and a clinical treatment scheme are synchronously generated based on topological attributes of the focus probability graph, and a closed-loop link from image analysis to diagnosis and treatment decision is realized. According to the method, the functions of mucus interference suppression, cross-modal accurate registration and real-time diagnosis and treatment assistance are integrated in a breakthrough manner, and the focus recognition accuracy and clinical operation efficiency of the endoscope image are remarkably improved.

The invention provides a gallium nitride radio frequency device defect detection method and system based on deep learning, and relates to the technical field of gallium nitride devices, and the method comprises the steps: generating and preprocessing a multispectral image data set; performing channel and space two-dimensional attention calculation, and performing feature optimization in combination with deformable convolution and residual connection; constructing a multi-scale feature pyramid, and constructing a multi-level classification tree based on feature similarity to perform fine-grained defect classification; and utilizing the deep neural network model to evaluate the influence degree of the defect on the device performance. According to the method, the defect position and type of the gallium nitride radio frequency device can be accurately identified, the performance influence of the defect is quantitatively evaluated, and the detection precision and efficiency are improved.

The invention relates to the field of optical nondestructive testing, and discloses a visualization method for detecting mango quality based on hyperspectrum, which comprises the following steps of: firstly, selecting mango samples, and simulating different mango ripening stages by dividing the mango samples into a plurality of groups of samples; acquiring a high-dimensional spectral image by using a hyperspectral imaging system, and measuring the hardness, the soluble solid content and the titratable acid content; correcting and extracting the high-dimensional spectral image to obtain and optimize the average reflection spectrum of the sample, and establishing a mango maturity classification model and a quality regression prediction model; performing characteristic wavelength contribution degree analysis and screening, and optimizing a quality regression prediction model by adopting a key wave band; the method is applied to each pixel point of a high-dimensional spectral image, a two-dimensional distribution diagram is generated, and mango quality visualization is achieved. The method solves the problems that in the prior art, rapid and lossless judgment of the mango ripening stage cannot be achieved, accurate prediction and visual distribution of key internal quality indexes cannot be achieved, and intelligent grading and quality control of picked fruits are not ideal.

The invention discloses a method for estimating plant biomass based on map multi-modal feature extraction and fusion. The method comprises the following steps: acquiring a plant RGB image and a multi-spectral image; inputting the preprocessed RGB image and NIR wave band image into a double-model cooperation segmentation framework to realize image segmentation; binary image features, color features, texture features, reflectivity and the like are calculated, feature splicing is carried out, and high-dimensional features are constructed; carrying out dimension reduction on the high-dimensional features; and training a deep neural network through the effective features and the biomass to realize biomass estimation. According to the method, a zero sample learning-based double-model cooperation segmentation framework is utilized to realize accurate segmentation of a single plant on the premise that a large number of training sets are not needed; multi-modal feature information is extracted based on the segmented single plant image, an improved SHAP model is introduced to reduce the feature space dimension, and the inversion precision and the operation efficiency are improved while the information effectiveness is ensured; through a high-precision deep neural network model, rapid, lossless and accurate biomass acquisition is realized.

The invention discloses a multi-spectral image fusion building surface biological attachment area detection method and device and a medium, relates to the technical field of image processing, and discloses a multi-spectral image fusion building surface biological attachment area detection method comprising the following steps: based on a to-be-detected building, obtaining a visible light image collected by a visible light camera of an unmanned aerial vehicle, the multispectral camera acquires a multispectral image sequence based on at least two different frequency bands; registering the visible light image and the multispectral image based on a calibration and preprocessing module to obtain a pixel-level aligned visible light image and multispectral image sequence; and according to a deep learning feature identification module, identifying the visible light image and the multispectral image sequence after pixel-level alignment, and obtaining a pixel-level segmentation result of the organism attachment area of the to-be-detected building. Therefore, detection is carried out based on the unmanned aerial vehicle, deep learning and an image feature fusion mode are combined, and the building surface bioattachment recognition accuracy is improved.

The invention discloses a panchromatic sharpening image fusion method and device based on a double-domain flexible converter. The method comprises the following steps: performing multilayer space-frequency joint attention operation on a low-resolution multispectral image and a high-resolution panchromatic image to generate a feature tensor; performing double-domain feature alignment operation, aligning structural semantic features based on an attention mechanism, fusing amplitude and phase frequency spectrum information through Fourier transform, and matching modal distribution differences by using instance normalization; multi-level fusion is carried out, and splicing, convolution and space-frequency joint attention operation are carried out on each level in sequence; carrying out residual error reconstruction on the fusion features; and adding the reconstructed features and the up-sampled low-resolution multispectral image element by element, and outputting a high-resolution multispectral image. According to the method, image structure distortion and detail blurring can be remarkably reduced, the image definition and the edge reduction capability are improved, the fusion consistency and the physical authenticity are improved, and the detail expressive force of the fused image is enhanced.

The invention provides a hyperspectral anomaly detection method based on double-stage attention guidance and a state space model, which relates to the technical field of hyperspectral image processing and comprises the steps of scene background modeling based on an auto-encoding network, generation of a reconstructed background image and a reconstructed residual image. Carrying out target signal enhancement on the original hyperspectral data based on the reconstructed residual image to obtain attention enhancement data, carrying out target feature depth extraction by adopting a state space model based on the attention enhancement data, generating an abnormal semantic feature image, and fusing a background guide feature image and the abnormal semantic feature image to obtain a hyperspectral image; and an abnormal probability graph is generated through adaptive gating fusion, and collaborative optimization is carried out based on a multi-task loss function. According to the method, the internal contradiction of a single network architecture is fundamentally solved, the prior guiding capability of the reconstruction method and the strong feature representation capability of the state space model are fully combined, and the accuracy and reliability of hyperspectral anomaly detection are remarkably improved.

The invention discloses a multispectral and hyperspectral imaging proportion balance optimization method based on satellite remote sensing, and relates to the technical field of balance optimization, and the method comprises the following steps: collecting a first image data set which comprises a low-resolution multispectral image and a hyperspectral image; preprocessing the first image data set, screening out a low-resolution multispectral image from the preprocessed images, improving the spatial resolution of the multispectral image by adopting a super-resolution reconstruction algorithm, and generating a reconstructed multispectral image; fusing the reconstructed multispectral image and the preprocessed hyperspectral image, and performing optimization processing on a fusion result to generate an image with an optimal resolution; and extracting second image data based on the optimal resolution image, and establishing a proportion balance optimization model. According to the method, the data processing time and the imaging cost are accurately calculated, and the image fusion quality evaluation is combined, so that the optimal allocation of the imaging ratio under limited resources is realized.

The invention provides a panchromatic sharpening joint optimization method and system based on three-stage progressive fusion. The method comprises three stages of coarse fusion, deblurring detail enhancement and fine fusion. The method comprises the following steps: firstly, performing feature extraction and preliminary fusion on a low-resolution multispectral image and a high-resolution panchromatic image through a dual-path mutual enhancement network; secondly, a multi-layer stacked deblurring module is introduced to perform deep enhancement on fusion features, and the details and definition of the image are effectively improved in combination with partial large kernel convolution, channel mixing and an element-level attention mechanism; and finally, realizing fine optimization of spectrum consistency and a space structure, and outputting a fused image with high spatial resolution and high spectral fidelity. According to the method, the stability and reconstruction quality of panchromatic sharpening under a complex degradation condition are effectively improved through multi-source remote sensing image collaborative modeling and progressive feature fusion, and the method has good practical value and popularization prospect and is superior to a current panchromatic sharpening method based on deep learning.

The invention belongs to the technical field of surveying and mapping and geographic information processing, and discloses a geographic entity intelligent identification and reconstruction system based on multi-source surveying and mapping data. The system is composed of a multi-source data acquisition and preprocessing module, a cross-modal feature coding and fusion module, a structural atlas construction and spatial logical reasoning module, a deformable neural modeling module and a physical prior guided collaborative prediction and closed-loop optimization module. According to the method, a cross-modal feature coding and fusion module is arranged, and a modal attention mechanism is introduced to dynamically weight multi-source data, so that heterogeneous information such as a laser point cloud, an inclined image and a multispectral image is fused into a unified coding vector in a high-dimensional space; compared with feature extraction performed by using a static deep network in a comparison file, the method of the invention adopts a minimum residual function to perform modal weight training, has an adaptive feature integration capability, and effectively improves the accuracy of geographic entity recognition and the robustness of boundary segmentation in different scenes.

The invention discloses a hyperspectral image classification method for cross-domain small sample learning based on diffusion enhancement prototype knowledge distillation, and belongs to the technical field of hyperspectral image processing. The method comprises the following steps: extracting a neighborhood data cube, aligning spectrums, dividing a support set and a query set, and applying a mask and enhancing noise; executing domain adversarial denoising and reconstruction tasks, aligning feature distribution, and outputting a pre-training encoder; decoupling features, capturing spectrum-space global and local dependency relationships, and calculating similarity between a query set and a category prototype; constructing a distillation framework to realize knowledge migration; optimizing model parameters, and introducing a signal-to-noise ratio to enhance loss suppression noise; and performing feature extraction by using the optimized student model to generate a hyperspectral image classification result. According to the method, the problems of domain offset, intra-class feature dispersion, inter-class boundary fuzziness, noise interference and the like are solved, and the classification accuracy in a small sample scene is improved.

The invention relates to the technical field of image classification, and discloses a hyperspectral image domain generalization classification method, system and device and a medium. The method comprises the following steps: acquiring a plurality of hyperspectral images of different ground feature types; obtaining a mean value and a variance of each hyperspectral image in a channel dimension, performing random disruption, and determining a spectral variation parameter of the hyperspectral image according to the mean value and the variance before and after random disruption so as to generate a corresponding spectral variation image; separating the center and the background of each hyperspectral image to obtain respective center image and background image, randomly disorganizing the center image and the background image, and generating a corresponding spatial variation image by combining the disorganized center image and background image of each hyperspectral image; a hyperspectral image classification model is obtained by training an enhanced hyperspectral image obtained by fusing a plurality of corresponding spectral variation images and spatial variation images, so that ground feature classification is performed on a to-be-classified hyperspectral image, and the domain generalization performance of the hyperspectral image classification model is improved.

The invention provides a spectral image demodulation method, spectral image demodulation equipment and a computer readable storage medium. The spectral image demodulation method comprises the following steps: S1, acquiring a first spectral image; s2, processing the first spectral image to obtain a second spectral image; s3, performing spectrum correlation region division on the second spectrum image to obtain a plurality of spectrum correlation regions; s4, obtaining a plurality of spectrum curves according to the plurality of spectrum correlation areas; and S5, obtaining a spectral image of a target wave band according to the target wave band, the plurality of spectral curves and the first spectral image. The spectral image demodulation method provided by the invention can break through the limitation of the prior art, can realize spectral image demodulation and spectral image inversion, has the advantages of high precision, high accuracy, high reliability and the like, and can be widely applied to various fields and various scenes.

The invention relates to the technical field of precision agriculture and grape cultivation, and discloses a vineyard fertilization method based on unmanned aerial vehicle multi-source remote sensing data, and the method comprises the steps: obtaining a canopy multi-spectral image and three-dimensional structure data through an unmanned aerial vehicle; carrying out image preprocessing and finely extracting a grape canopy region; fusing the extracted spectral vegetation index, texture features and three-dimensional structure features, and constructing a multi-source feature vector; constructing and optimizing a nitrogen nutrition inversion model through a machine learning algorithm by utilizing actually measured nitrogen nutrition parameters; generating a nitrogen content distribution diagram based on a model inversion result, calculating the nitrogen deficiency amount and the recommended dressing pure nitrogen amount of each space unit in combination with critical nitrogen concentration diagnosis and target yield, and converting the nitrogen deficiency amount and the recommended dressing pure nitrogen amount into the use amount of a foliage spraying working solution; and generating a variable fertilization prescription map, and converting the variable fertilization prescription map into a nozzle flow control instruction executable by the unmanned aerial vehicle to realize on-demand accurate variable fertilization. According to the invention, closed-loop management from nitrogen nutrition monitoring to variable rate fertilization is realized, and the nitrogen fertilizer utilization efficiency and the fertilization accuracy are improved.