145 results about "Band selection" patented technology

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 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.

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 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 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.

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 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 tree species identification method based on multi-modal spectrum and texture features of a wood cross section. The method comprises the following steps: acquiring hyperspectral image data of the wood cross section; constructing a comprehensive similarity matrix based on a plurality of spectrum similarity indexes, and selecting a representative wave band subset from the hyperspectral image data by adopting a multi-strategy wave band screening mechanism; performing multi-scale wavelet fusion on the representative wave band subset to generate a single-channel fusion image with consistent spatial resolution, and extracting points of interest and spectral features thereof from the single-channel fusion image; generating a gray-scale base map based on the hyperspectral image data, extracting various complementary texture feature maps from the gray-scale base map, and screening out significant points of interest from the various texture feature maps; and fusing the spectral features and the texture features, constructing feature vectors for representing wood tree species, and inputting the feature vectors to a classification model to complete tree species identification. According to the invention, rapid, accurate and intelligent identification of wood tree species can be realized.

The invention discloses a quantum behavior multi-target waveband selection method for hyperspectral target detection, and the method comprises the steps: obtaining a hyperspectral image, and initializing particle populations represented by different hyperspectral waveband subsets in the hyperspectral image; constructing a multi-objective optimization model for hyperspectral image waveband selection, wherein the model is used for measuring the information amount of each particle, the independence between wavebands and the data space retentivity; determining a non-dominated solution through an inter-particle game based on a multi-objective optimization model, generating global and local optimal particles, and updating binary candidate waveband vectors; removing low-separability wavebands in the binary candidate waveband vectors through a real-time constraint target waveband selection method to generate a new particle population; and obtaining a final non-dominated solution based on particle evolution, and selecting a final waveband subset from the non-dominated solution by calculating a target background separation perception robustness score.

An illustrative surgical system may access a plurality of images captured outside a structure within a patient; detect a difference between spectral reflectances of scenes captured in the plurality of images; and identify, based on the detected difference between the spectral reflectances of the scenes captured in the plurality of images, pixels in at least one of the plurality of images that correspond to structure tissue of the structure.

The application discloses a swarm intelligence optimization wave band selection method for hyperspectral image target detection, and comprises the following steps: S1, reading hyperspectral image data X, target signal d, and the number K of wave bands needed to be selected; S2, establishing a multi-objective optimization model based on information entropy, joint spectrum-space similarity and CEM error; S3, starting iteration, determining a global optimal solution gbest from a non-inferior solution set rep by adopting a roulette wheel operator strategy, calculating an individual optimal solution pbest, and updating the position and speed of each individual in the population; S4, performing crossover and mutation operations on each individual in the population; S5, calculating the fitness of each individual in the population P t+1 , updating the non-inferior solution set; and S6, performing evaluation by adopting an optimal / suboptimal ratio criterion, and selecting a best wave band subset. The application overcomes the one-sidedness of wave band evaluation of a single criterion method, and can realize effective dimension reduction of a hyperspectral image.

The application discloses a hyperspectral band selection method based on LiDAR guidance and bidirectional cross-modal attention, relates to the technical field of hyperspectral band selection, and comprises the following steps: acquiring a hyperspectral image dataset and a LiDAR dataset of a target object; constructing a pre-trained band selection network, taking the hyperspectral image dataset and the LiDAR dataset of the target object as inputs of the pre-trained band selection network, and acquiring attention weights of each band of the hyperspectral image; performing descending order sorting according to the weights of each band of the hyperspectral image, and selecting the first N hyperspectral bands in the sorting result as a band subset. Through intelligent guidance of LiDAR features and deep refinement of a StarG module, the application can adaptively and intelligently filter out a band subset with the most discriminative power and information quantity from original HSI data, thereby effectively eliminating redundant and noise information, and being beneficial to subsequent hyperspectral image processing tasks.

The application discloses an image sharpness evaluation method, comprising: obtaining an original image and an optical modulation transfer function; generating a multi-scale baseline sharpness response set, an information-physical sharpness kernel, an uncertainty measure and an evidence vector based on the original image and the optical modulation transfer function; estimating a failure posterior probability based on the evidence vector; generating a gating weight based on the failure posterior probability and the uncertainty measure; applying the failure posterior probability and the gating weight to perform a failure posterior coupling process on the multi-scale baseline sharpness response set and the information-physical sharpness kernel to generate a final sharpness evaluation result. The coupling process preferably includes applying an analytical bias correction and performing a cross-scale band selection. The application improves the evaluation robustness in a complex scene through a failure posterior coupling mechanism.

The invention provides a hyperspectral image aerial small target detection method and device, and the method comprises the steps: determining a plurality of wavebands with the maximum contrast based on a local contrast feature pattern of each waveband in a target hyperspectral image, and obtaining the target hyperspectral image after the waveband selection; calculating the enhanced mahalanobis distance of the spatial-spectral variance ratio of each spectral vector in the target hyperspectral image after the band selection; and determining a small target detection result of the target hyperspectral image based on the Mahalanobis distance with the enhanced spatial-spectral variance ratio. Therefore, small targets in the air can be monitored, wavebands with differences between the targets and the background can be well screened out, redundant background interference is reduced, the detection precision is improved, and the false alarm rate is reduced.

The application provides a hyperspectral image band selection method, device and electronic equipment. The method comprises: sampling each band image of a hyperspectral image in a three-dimensional space to obtain a sampling two-dimensional matrix; calculating the information entropy of each sampling band in the sampling two-dimensional matrix, grouping the information entropy, selecting a sampling band corresponding to the maximum information entropy in each group to form an initial clustering center matrix; performing bias processing on the initial clustering center matrix to obtain a target clustering center matrix; based on the sampling two-dimensional matrix and the target clustering center matrix, calculating the membership degree of each sampling band in the sampling two-dimensional matrix to each clustering center in the target clustering center matrix to obtain an initial membership matrix; calculating a kernel matrix based on the sampling two-dimensional matrix; and determining a target band selected in the hyperspectral image based on the kernel matrix and the initial membership matrix. The method is efficient in calculation and improves the local optimal solution problem.

The application provides a band selection method and device based on matrix calculation, equipment and medium, relates to hyperspectral image processing technical field, and includes obtaining a first ground object array and a second ground object array; calculating the channel value ratio of the first ground object array and the second ground object array under the same wavelength as a first adjustment factor matrix; after shifting the second ground object array to the right and left respectively, the obtained ratio is respectively taken as a second adjustment factor matrix and a third adjustment factor matrix; the first adjustment factor matrix, the second adjustment factor matrix and the third adjustment factor matrix are integrated into a target adjustment factor matrix; the wavelength with the highest similarity and the largest difference is determined from the evaluation value matrix.The application establishes the adjustment factor matrix by shifting the image of the ground object to the left and right, calculates the evaluation value matrix, and accurately determines the difference band and the similar band between the ground objects in a short time through the matrix resampling algorithm.

The application provides a fabric color difference grade determination method based on multi-dimensional spectral analysis, and belongs to the technical field of intelligent textile detection. The method comprises the following steps: acquiring a three-dimensional spectral data cube of a sock band under an integrating sphere diffuse light source environment by using a spectral imaging acquisition system, and converting an original image into spectral reflectance data through black and white board radiation correction; identifying a joint position by using the spectral response difference between the joint and the sock band, completing automatic segmentation of the sock band image, and acquiring effective detection areas of each segment; in combination with OCR-recognized character information, eliminating surface defects by using a deep learning target detection algorithm for a single sock band; separating texture shadows and background noise by using hyperspectral unmixing, and extracting pure spectra representing the true color of the sock band; and mapping the pure spectra to a CIELAB color space to calculate color differences and determine grades. The application realizes the intelligentization and objective evaluation of the sock band determination method by using spectral imaging and intelligent recognition technology.

The invention discloses a satellite edge hyperspectral image processing method and system, relates to the technical field of communication, and aims to solve the problems of resource limitation, communication bottleneck and unreasonable task scheduling faced by hyperspectral image processing in satellite edge calculation. The method comprises the following steps: taking a future data transmission rate of a satellite as a communication feature vector, and constructing a task unloading matrix in combination with a graph attention mechanism to realize accurate matching of tasks and satellite resources; the task unloading matrix is converted into task features, the task features and the hyperspectral image are fused to form a fusion tensor, a high-dimensional feature vector is generated based on the fusion tensor, key wavebands are screened, and a waveband selection matrix is obtained; and constructing a joint loss function containing a task unloading loss function and a band selection loss function, and iteratively optimizing the double matrixes until the loss is minimum to obtain an optimal matrix. Through collaborative optimization of task scheduling and image processing, system energy consumption, communication constraint and processing precision are balanced, the satellite resource utilization rate and image processing real-time performance are improved, and the method is suitable for low-orbit satellite edge calculation scenes.

A communication system incorporates a method and a computer program product that provide selecting and camping on primary band in idle mode in preparation for supporting an anticipated data connection for demanding application(s) to a network system. The communication system scans, using a radio frequency (RF) communication subsystem, available network node(s) to assess communication capacity. The communication system identifies a combination of a primary band and secondary band(s) of the available network node(s) that satisfies a communication demand requirement of data throughput, data latency, and / or data quality. The communication system selects and camps on the primary band in preparation for activation of the demanding application(s) and configuring the RF communication subsystem to use the secondary band(s) to operate in carrier aggregation and / or dual connection mode to satisfy the communication demand requirement in preparation for activation of the demanding application(s) and establishment of the data connection.

The application provides a fixed-point blackbody-based radiation parameter calibration device and method, which comprises a fixed-point blackbody assembly, a standard variable-temperature blackbody assembly, a radiation transfer standard component, a waveband selection device, a displacement assembly and a control assembly; the fixed-point blackbody assembly comprises a first fixed-point blackbody, a second fixed-point blackbody, a third fixed-point blackbody and a fourth fixed-point blackbody; the standard variable-temperature blackbody assembly comprises a standard normal-temperature variable-temperature blackbody radiation source and a standard medium-temperature variable-temperature blackbody radiation source; and the waveband selection device comprises a precision diaphragm assembly and a spectrum selection assembly. The application transfers the reference radiation brightness and the reference radiation intensity of the fixed-point blackbody assembly to the radiation brightness and the radiation intensity of the standard variable-temperature blackbody assembly by using the radiation transfer standard component, and calibrates the calibrated infrared radiometer at different infrared spectral wavebands by using the standard variable-temperature blackbody assembly, thereby solving the calibration problem of the existing infrared radiometer.

The application belongs to the technical field of plant disease nondestructive detection, and discloses a nondestructive detection method and system for citrus Huanglongbing based on hyperspectral reflection and transmission fusion, which comprises the following steps: acquiring hyperspectral reflection spectrum data and hyperspectral transmission spectrum data of a to-be-detected citrus leaf, obtaining single reflection characteristic band data and single transmission characteristic band data after pretreatment and band selection; based on the single reflection characteristic band data, the single transmission characteristic band data and the spectrum data after feature-level fusion of the two, first, second and third prediction calculation results are obtained through respective optimal classification models; the corresponding weights are determined according to the classification accuracies of the optimal classification models, and the first, second and third prediction calculation results are subjected to decision-level fusion calculation to obtain the Huanglongbing grade of the citrus. The method introduces feature-level and decision-level fusion, has the discrimination ability of sample surface physical form and internal structure composition, and can quickly and nondestructively detect the Huanglongbing of the citrus.