761 results about "Spatial spectrum" patented technology

The invention discloses a hyperspectral image classification method based on proxy attention and a VIT architecture, and belongs to the field of image classification, and the method comprises the following steps: preprocessing hyperspectral image data, and dividing the hyperspectral image data into a training set and a test set; constructing a hyperspectral image classification model based on proxy attention and a VIT architecture; training the hyperspectral image classification model by adopting the training set; and inputting the hyperspectral data in the test set into the trained hyperspectral image classification model, reasoning to obtain a prediction category, and evaluating the obtained prediction category through an evaluation index. According to the invention, the agent attention encoder based on agent attention is constructed, hierarchical feature extraction is carried out through the agent attention encoder of L-layer cascade, layer-by-layer fusion and enhancement of space-spectrum information in an initial feature coding matrix are realized, the calculation complexity is significantly reduced while the strong representation capability is maintained, and the method has the advantages of high efficiency, high resolution and the like. And good balance between the calculation efficiency and the feature expression capability is realized.

The invention discloses a hyperspectral remote sensing image abnormal target detection method based on a double-decoder double-Transform background enhancement reconstruction network. According to the method, a novel background feature purification decoding task is introduced and designed, and the influence of abnormal pixels in the reconstruction process is reduced by focusing on background features. And in consideration of the characteristic that the exception is different from the background, an exception stripping shielding auxiliary branch module is designed to weaken the exception feature, and richer non-exception background information is supplemented for the decoding stage. Besides, in order to further guide reconstruction of a pure background, a novel cross guidance Transform module is further designed and is used for globally integrating multi-scale background attribute features. And finally, by using a collaborative optimization loss function, carrying out double constraint learning on potential anomalies in a spatial spectrum and a frequency domain, and completing accurate detection of an abnormal target. Experimental results on a hyperspectral data set show that the accuracy and robustness of anomaly detection can be remarkably improved.

The invention discloses an intelligent steel pipe surface defect recognition system based on deep learning, and particularly relates to the technical field of pipe surface defect analysis. An annular polarization light source array and a high-frame-rate CMOS sensor are adopted to synchronously collect visible light and near-infrared multi-polarization images; a surface normal is calculated based on Stokes parameters, mirror surface suppression and diffuse reflection enhancement are realized, a defect candidate area is generated by fusing multi-scale Laplacian pyramid residual error and Renyi entropy segmentation threshold positioning, multi-physical quantity registration is completed through white light interference and infrared thermal imaging, a six-channel feature cube is constructed, and a three-dimensional image is obtained. According to the method, space, spectrum and thermal characteristics are jointly extracted in the multi-head attention convolutional neural network, the confidence coefficient is evaluated in combination with Jensen-Shannon divergence, and the polarization angle and the focal length are dynamically adjusted according to the confidence coefficient, so that closed-loop parameter self-optimization is realized, and the micro-scale pitting corrosion and millimeter-scale crack detection precision is remarkably improved.

The invention provides a frequency division multiplexing multi-beam anti-interference method for a shaped antenna, and the method comprises the steps: obtaining the signal data of each frequency band according to the multi-beam spatial domain data received by the shaped antenna and a filter of a frequency domain corresponding to the working frequency band of each beam; performing high-resolution spatial spectrum estimation on the signal data of each frequency band by adopting a MUSIC algorithm to obtain an incoming wave space direction of an interference signal; according to the incoming wave space direction of the interference signal, on the basis of a preset multi-beam zeroing anti-interference strategy, self-adaptive weights of all same-frequency beams are obtained in a spatial domain and frequency domain two-dimensional combined processing mode; the adaptive weights of the same-frequency beams are combined, a comprehensive directional diagram is obtained, and a multi-beam interference zeroing result is obtained, according to the method, the whole airspace is covered with the beams of different frequency points, and space-frequency combined suppression of airspace complex interference is achieved.

The invention relates to the technical field of remote sensing hyperspectral images, in particular to a remote sensing hyperspectral image super-resolution reconstruction method based on a hybrid neural network, and the method comprises the steps: firstly carrying out the preprocessing of a remote sensing hyperspectral image data set, and obtaining a training set; constructing a hybrid neural network with a spectrum hypergraph branch, a space hypergraph branch, a semantic hypergraph branch, a sensitive wave band extraction branch and a recovery and reconstruction branch; training the hybrid neural network by using a training set to obtain a hybrid network model; and finally, inputting a to-be-reconstructed low-resolution remote sensing hyperspectral image into the hybrid network model to obtain a corresponding reconstructed remote sensing hyperspectral image. According to the method, space, spectrum and semantic feature information are fully combined, and a main body structure and detail change characteristics in an image are captured; the sensitive wave band extraction branch is provided to enhance cross guidance and fusion among multi-domain features, and the effect of super-resolution reconstruction is fully improved.

The invention relates to a multispectral image and hyperspectral image fusion method and system, and belongs to the field of deep learning and signal processing, and the method comprises the steps: carrying out the coding and feature extraction of a multispectral image and a hyperspectral image through employing a coder based on a spectrum self-attention mechanism; the hyperspectral image features and the multispectral image features obtained by the encoder are input into a space-spectrum fusion module, deep fusion of the spatial dimension and the spectral dimension is carried out, and unified feature representation is obtained; inputting unified feature representation obtained by fusion into a decoder based on a spectrum self-attention mechanism, inputting layered features passing through a graph-based spectrum sensing module into a corresponding decoder symmetric layer, and then decoding and outputting to obtain a spectrum image with high spatial resolution and a spectrum image with high spectral resolution; through the above strategy, full fusion and complementation of spectral image space information and spectral information are ensured, and the method has important value for research and application of MHIF.

The present invention proposes an electronic component appearance defect detection method and system based on multispectral imaging, which belongs to the field of optical measurement technology, including: obtaining an appearance image dataset of an electronic component to be detected, performing image preprocessing, extracting an area of ​​interest of the appearance of the electronic component to be detected, and obtaining a multispectral image of the area of ​​interest; extracting the spectral reflectance characteristics and spatial characteristics of the multispectral image of the area of ​​interest through a spectral reflectance reconstruction method combining a genetic algorithm and a neural network, and performing feature fusion to form a spatial-spectral joint feature dataset of the area of ​​interest, which is input into a trained appearance defect detection model to obtain the defect type and size of the appearance of the electronic component to be detected; and displaying the appearance defect detection results of the electronic component to be detected. The present invention improves the accuracy and efficiency of appearance defect detection of electronic components.

The invention provides a torreya grandis forest extraction method based on a deep learning network and a multi-temporal remote sensing image, and the method comprises the steps: carrying out the data preprocessing of a remote sensing image, constructing and obtaining a comprehensive feature image of each month, extracting the pixel samples of torreya grandis and non-torreya grandis types, calculating and obtaining a comprehensive class spacing distinguishing capability index of each month, and obtaining a torreya grandis forest extraction result. Obtaining an original wave band feature set of the similar hyperspectral structure; performing feature optimization by using a maximum correlation minimum redundancy algorithm to obtain an optimized waveband feature set; marking torreya grandis and non-torreya grandis areas according to the torreya grandis sample points and the high-resolution remote sensing image, and making classification labels for deep learning; and constructing a space-spectrum multi-scale feature fusion network model, inputting the optimal waveband feature set into a deep learning network for training, and outputting classification results of torreya grandis and non-torreya grandis. According to the method, the multi-temporal remote sensing image can be fully utilized, and the spatial and spectral features are effectively extracted and fused, so that the recognition precision and efficiency of the torreya grandis are improved.

The invention provides an open set cross-domain hyperspectral classification method and system based on multi-mode and optimal transmission, and relates to the technical field of hyperspectral image classification. The method specifically comprises the following steps: acquiring and utilizing an original hyperspectral image to construct a labeled source domain data set and an unlabeled target domain data set; utilizing a spatial-spectral feature extractor to respectively obtain spatial-spectral features of the source domain data and spatial-spectral features of the target domain data; and determining a source domain prototype, calculating a similarity matrix between the target domain data and the source domain prototype, solving an optimal transmission matching matrix, distributing a pseudo tag for each target domain data, and performing text correction on all the source domain data and all the target domain data by using a contrast learning loss function to obtain the target domain data. And the source domain prototype and the spatial spectrum feature extractor are optimized by constructing the total loss of minimizing hyperspectral image classification, so that classification prediction can be carried out on the hyperspectral image to be classified, and target domain data can be classified more accurately.

The invention discloses a hyperspectral image classification method based on an S2CFM-spatial spectrum convolution fusion Mama network model, and the method employs a parallel double-branch structure to extract the spatial context information and spectral sequence features of a hyperspectral image, and finally integrates the features through a dynamic convolution fusion module, thereby achieving the classification of the hyperspectral image. The method solves the problem of unbalanced utilization of space-spectrum information in a traditional method, introduces a space spectrum convolution fusion Mama network, and fuses a multi-scale convolution block (MCB), a dynamic convolution block (DCB) and a space spectrum Mama block (S2MB); the method solves the problems that in the prior art, the capacity of CNN for capturing spectral information is relatively limited, and a complex spectrum-space characteristic relation in hyperspectral data is difficult to fully represent; the problems that in HSI data, due to the fact that the dimensionality is high and the sample size is limited, an over-fitting problem is prone to occurring, and the generalization performance of the HSI data is limited are solved through a Transformers-based architecture.

The invention provides a coherent signal arrival direction estimation method and device based on a deep convolutional network, and belongs to the field of array signal processing. The method comprises the following steps: receiving a to-be-detected signal containing a coherent signal by using a uniform linear array antenna to obtain an array receiving data matrix and extract a covariance matrix; forming an input feature vector by right upper triangular elements divided from a diagonal line in the covariance matrix, inputting the input feature vector into a covariance estimation model formed by a deep convolutional network, obtaining an estimation value of the right upper triangular elements under an ideal incoherent condition, and reconstructing the estimation value to obtain a covariance matrix estimation value; and performing characteristic decomposition on the covariance matrix estimation value, and generating a spatial spectrum by using a MUSIC algorithm to obtain an estimation result of the signal arrival direction. According to the method, the noise-containing mixed signal covariance matrix is mapped into the ideal incoherent noise-free signal covariance matrix through a physical constraint supervised learning framework, so that the estimation precision and robustness of the MUSIC algorithm in a coherent scene are improved.

The invention provides a hyperspectral snapshot compression imaging method and system based on a space-spectrum prior decoupling model, high-quality reconstruction is realized through decoupling optimization and a deep expansion network, and the method comprises the following steps: constructing a training data set containing a compression measurement image and a corresponding reconstruction spectrum; establishing an objective function fusing space and spectrum prior, converting the objective function into constrained optimization, and converting the constrained optimization into three sub-problems of linear reconstruction, space prior and spectrum prior by adopting a semi-quadratic splitting method; a deep expansion network is designed to alternately solve sub-problems: a linear sub-problem is solved through analysis, and a space / spectrum sub-problem is subjected to implicit prior modeling through a private network, so that end-to-end reconstruction is realized; a mixed loss function is adopted to optimize model parameters, and images can be reconstructed in real time after training is completed. Space and spectrum prior decoupling is carried out, space structure details and spectrum features are respectively captured through an independent network architecture, the problem of mutual interference of joint modeling in a traditional method is solved, and high-quality spectrum image reconstruction is realized.

The invention belongs to the field of air pollutant prediction, discloses a multiple air pollutant prediction method based on a multi-scale spatio-temporal spectrum feature fusion framework, and solves the technical problem of low prediction accuracy of multiple air pollutants caused by single air pollutant prediction type and neglect of a dynamic dependency relationship among different pollutants in the prior art. The method comprises the following steps of: constructing an air quality characteristic data set by acquiring collected data of a plurality of air quality monitoring stations in a city; preprocessing the air quality characteristic data set; constructing a multi-scale space-time spectrum feature fusion model; inputting the preprocessed air quality feature data set into a multi-scale spatio-temporal spectrum feature fusion model for training; and predicting the pollutant concentration of each station in a future time period by using the trained multi-scale spatio-temporal spectrum feature fusion model. According to the method, the dynamic dependency relationship among various pollutants is explicitly modeled when the model is constructed, and the prediction precision of various air pollutants is effectively improved.

The invention discloses a cross-scene hyperspectral image classification method combined with channel-space attention improvement, and belongs to the field of remote sensing image classification. According to the method, the problems of poor classification precision, to-be-improved robustness and limited feature extraction capability caused by insufficient space-spectral feature relevance modeling and weak cross-scene generalization capability of a traditional model are solved. A domain generalization method is introduced to construct a feature alignment module, data distribution differences between different scenes are reduced through a distribution adaptation algorithm, and the model generalization ability is improved; acquiring local space and global spectral features of the hyperspectral data by using a space-spectrum feature extraction module, and enhancing discriminative spectral feature extraction through a channel-space attention fusion network; through fusion of a Lion optimizer and a cosine annealing strategy, global optimization is realized, training stability is guaranteed, and a feature learning effect is improved. The method can be applied to remote sensing image classification.

The invention belongs to the field of artificial intelligence and fundus image enhancement, and discloses a fundus image enhancement method and system based on machine learning, electronic equipment and a storage medium, and the method comprises the steps: obtaining an original fundus spectral image, and carrying out the preprocessing of the original fundus spectral image, and obtaining a preprocessed spectral image; constructing a backbone network based on a residual network, and extracting multi-scale features of the preprocessed spectral image in combination with cavity convolution; introducing a channel-space-spectrum multi-attention module into the backbone network, and performing multi-attention fusion on the multi-scale features to obtain an enhanced feature map; and performing adversarial training on the backbone network by using the generative adversarial network and the enhanced feature map, and performing image enhancement on the collected fundus spectral image by using the trained network to obtain an enhanced fundus spectral image. According to the method, more-dimensional image support is provided for medical diagnosis, the quality of the fundus image can be effectively improved, the diagnosis accuracy of a doctor on fundus lesions is improved, and the method has important clinical application value.

The invention provides a frequency domain Mama multi-scale hyperspectral image classification method and device, and relates to the technical field of hyperspectral image classification. The method comprises the following steps: collecting hyperspectral image data, preprocessing the hyperspectral image data, and segmenting the hyperspectral image data into a plurality of image blocks; inputting the image blocks into a space spectrum interaction feature extraction module, and extracting interaction features through a space branch and a spectrum space fusion branch; inputting the interaction features into a frequency domain disturbance embedding module to obtain features after frequency domain processing; inputting the features after frequency domain processing into an adaptive bidirectional Mama module, and extracting forward and backward spatial features and spectral features; and performing multi-level integration on the spectral features and the spatial features in different stages to obtain global features, and realizing final classification. According to the hyperspectral image classification method, the accuracy and robustness of hyperspectral image classification can be remarkably improved, and higher generalization ability and stability are shown in different data sets and complex scenes.

The invention relates to the technical field of pipeline detection, and provides a non-contact pipeline fluid flow velocity measurement method based on a frequency wavenumber domain spatial spectrogram, and the method comprises the steps: collecting a turbulence signal in a pipeline through a piezoelectric film sensor; carrying out snapshot number segmentation on the turbulence signal to obtain a plurality of segmented turbulence time domain signals; fourier transform and narrowband signal processing are carried out on the segmented turbulence time domain signals, and a spatial spectrum corresponding to each narrowband signal component is obtained; constructing a three-dimensional frequency-wave number domain spectrogram according to the spatial spectrum function; and according to the three-dimensional frequency-wavenumber domain spectrogram, calculating the flow velocity of the pipeline fluid. According to the method, a three-dimensional spectrogram characteristic space is constructed through conjoint analysis of the frequency and the wave number, the angle, distance and frequency characteristics of the turbulence signals are decoupled through spatial spectrum estimation methods such as the MUSIC algorithm, multipath interference and noise are effectively restrained, the flow speed of fluid in a pipeline can be accurately estimated, and limitation in a traditional method is overcome.

The invention discloses a self-supervised hyperspectral image classification method suitable for a low-annotation sample scene, and relates to the technical field of hyperspectral remote sensing image processing, comprising a self-supervised category sensing network oriented to the low-annotation scene; in the pre-training stage, a grouping spectrum enhancement module, a spectrum self-attention module and mask reconstruction are adopted, and the model is guided to focus on category-sensitive space-spectrum features under the label-free condition by minimizing the difference between a reconstructed image and an original shielded area; in the fine tuning stage, pre-trained network parameters are used as initialization parameters, and feature expression is further refined through classification loss. Therefore, by adopting the self-supervised hyperspectral image classification method suitable for the low-label sample scene, the lossless transmission of difficult sample features is realized, the distinguishing feature expression of mixed pixels is enhanced, and the classification balance of few sample categories is improved.

The invention provides a self-encoding hyperspectral anomaly detection method based on local and global dual-branch cooperation, and mainly solves the problem that the detection effect is poor due to the fact that an existing method is interfered by abnormal pixels. Comprising the following steps: 1) acquiring an original hyperspectral image; 2) constructing a self-encoding hyperspectral anomaly detection model comprising an encoder, a multi-direction mask convolution MDMC module which destroys anomaly spatial correlation along each direction, an attention-driven multi-scale grouping convolution AMGC module, a cascade type spatial spectrum attention CSSA module and a reconstruction module, wherein the self-encoding hyperspectral anomaly detection model comprises the encoder, the multi-direction mask convolution MDMC module, the attention-driven multi-scale grouping convolution AMGC module, the cascade type spatial spectrum attention CSSA module and the reconstruction module; 3) using an L1 norm and a spectral angular distance LSAD as a joint loss function, and guiding the model training to converge; and 4) inputting the original hyperspectral image into the trained final detection model to obtain a reconstructed hyperspectral image, and calculating to obtain an anomaly detection result. According to the method, the background reconstruction capability of the model can be improved, the reconstruction of the model on the anomaly is remarkably weakened, and the hyperspectral anomaly detection performance is effectively improved.

The invention provides synthetic aperture sonar equipment towed by an unmanned aerial vehicle and a method, and belongs to the technical field of underwater sonar detection. The equipment comprises an unmanned aerial vehicle, a sonar towed body, a towing cable and a water surface control unit, the method comprises: establishing a measurement reference; based on the measurement basis, acquiring state data of the synthetic aperture sonar under the dragging of the unmanned aerial vehicle, and resolving in real time to obtain pose data; extracting a track direction velocity component from the pose data, performing displacement integration on the track direction velocity component, and generating a pulse trigger signal when the displacement integration quantity reaches a sound wave half-wavelength threshold value; synchronously latching current pose data and collecting acoustic echoes based on the pulse trigger signal, and generating a timestamp binding data packet; geometric phase compensation is applied to the timestamp binding data packet, an aliasing-free image is reconstructed through wave number domain resampling, and spatial spectrum aliasing caused by carrier motion disturbance is eliminated.

The invention relates to the technical field of hyperspectral image processing, and provides a deep-expanded spatial spectrum sparse memory hyperspectral sharpening fidelity method and system, and the method comprises the steps: carrying out the priori knowledge coding of a high-resolution hyperspectral image through a regularization technology, solving a target function of a hyperspectral panchromatic sharpening task through a semi-quadratic splitting method, and carrying out the processing of a hyperspectral image. A residual module is adopted to simulate a degradation operator, spectral fidelity and spatial fidelity sub-problems are solved through gradient descent iteration, and a sparse prior sub-problem is solved based on an iterative shrinkage threshold algorithm; the high-fidelity and high-resolution hyperspectral images are corrected through the spatial spectrum prior features, and the multi-stage reconstructed high-resolution hyperspectral images are fused through the cross-stage memory fusion network to obtain the high-resolution hyperspectral images. According to the method, the reconstruction precision of the image is improved, the calculation complexity is reduced, the extraction of the spatial spectrum combined prior features of the original image is realized, and lossless information transmission is realized.

The invention discloses a high-frequency shallow water bottom reverberation signal modeling method, and belongs to the technical field of underwater acoustic engineering and signal processing. The method comprises the following steps: setting a modeling hypothesis; a cylindrical array geometric structure is constructed, scattering units are divided, and channel parameters and scattering functions are obtained in combination with a Bellhop tool and a GABIM model; generating a reverberation signal time domain expression, deducing an array receiving signal spatial domain expression and calculating a spatial spectrum; the model can also adjust underwater environment parameters to realize simulation under different conditions, and performs time domain and space domain theoretical analysis on reverberation signals based on a cylindrical array. Multi-dimensional underwater environment parameters are fused, and the modeling precision is improved; a receiving and transmitting combined cylindrical array is adapted, and spatial gain is embodied; experiments prove that the average error is within 3dB, the practicability is outstanding, support can be provided for design and optimization of the sonar system, and the method is suitable for underwater reverberation signal simulation and characteristic analysis of the sonar system in the high-frequency shallow water environment.

The invention discloses an intermediate infrared polarization spectrum imaging method and device. The method comprises the following steps: carrying out joint coding on an intermediate infrared light beam carrying to-be-measured target space, spectrum and polarization information by adopting an intermediate infrared polarization spectrum modulator consisting of metasurface units which are arranged in an array and have non-correlation response characteristics; projecting the coding light field to a two-dimensional detector array to generate a two-dimensional coding image under single exposure; and decoding the two-dimensional coding image through a reconstruction unit so as to reconstruct and obtain a space-spectrum-polarization four-dimensional data cube of the target to be measured. The problem that polarization information cannot be rapidly obtained in the prior art is solved, target full information can be obtained under single exposure, and the system has the advantages of being high in imaging speed, simple in structure, easy to miniaturize and the like.

The invention relates to the technical field of earthquake prediction, in particular to a three-dimensional earthquake motion field rapid prediction method based on deep learning and physical constraint, and the method comprises the following steps: S1, data collection: obtaining original data related to an earthquake event in real time; s2, data preprocessing and multi-modal data fusion: preprocessing the original data, and fusing the preprocessed original data into multi-modal data; s3, spatial interpolation: realizing seismic oscillation feature interpolation prediction of the epicentral region, and outputting spatial spectrum data; s4, time sequence prediction and physical information optimization: generating a seismic oscillation time domain prediction sequence, and correcting errors by using an SVM model; s5, real-time reasoning and emergency response: realizing real-time minute-level response under the GPU architecture, and outputting three-dimensional seismic motion field data; according to the method, high-precision minute-level prediction of the three-dimensional seismic motion field is realized, and the efficiency and reliability of post-earthquake emergency response are remarkably improved.

The invention provides a hyperspectral remote sensing image compression method based on attention and quantization coding optimization, and the method comprises the steps: carrying out a network model training process: carrying out the processing, cutting and enhancement of hyperspectral remote sensing image data, and constructing a sample set for training; extracting low-dimensional feature representation of the sample data by using a lightweight encoder, wherein the encoder integrates a convolutional layer and a spectrum multi-head self-attention module; a decoder fusing a space-spectrum attention mechanism is adopted to gradually reconstruct a hyperspectral remote sensing image from low-dimensional features; optimizing coding and decoding model parameters through a combined loss function; a quantization coding two-stage compression process: performing adaptive quantization on the features, and mapping the floating point type features into discrete integers based on a logarithm mapping strategy; performing two-stage coding compression on the quantized features; and recovering feature representation through decoding and inverse quantization, and inputting a trained decoder network to reconstruct a hyperspectral remote sensing image.

The invention discloses a hyperspectral image restoration method, which belongs to the technical field of hyperspectral imaging, and comprises the following steps: inputting a single hyperspectral image to be restored into a hyperspectral image restoration network for denoising / super-resolution reconstruction, the features are respectively input into a multi-scale space-spectrum fusion module to obtain branch features, local multi-scale space-spectrum features after dimension raising are obtained through splicing and dimension raising, and then the local multi-scale space-spectrum features are input into a self-adaptive space feature aggregation group to obtain global space-spectrum features; and after the noisy hyperspectral image passes through a convolutional layer, fusing the noisy hyperspectral image with the global space-spectral features to obtain a final de-noised image. The extended denoising method can obtain a super-resolution reconstruction method, and the method comprises the steps: carrying out the up-sampling after the global space-spectrum features are obtained; a low-resolution hyperspectral image to be restored is up-sampled before passing through a convolutional layer. According to the invention, the problems of noise introduction and spatial resolution reduction in the hyperspectral imaging process in the prior art are solved.

The invention discloses a hyperspectral and multispectral image fusion method and system based on Reform residual iteration. The method comprises the following steps: acquiring an image data set of a hyperspectral remote sensing image and a multispectral remote sensing image in the same region and preprocessing the image data set; performing spatial down-sampling on the preprocessed data set to generate a low-resolution hyperspectral image and a low-resolution multispectral image; an image priori network model is constructed, the image priori network model comprises a space priori module and a spectrum priori module, the space priori module captures a long-range spatial dependency relationship through a Reformer layer, and the spectrum priori module extracts spectrum features through a depth separable convolutional layer; establishing a multi-level iteration model, setting a residual connection reconstruction function, and training the model by adopting an optimization algorithm and a space-spectrum joint loss function to obtain a trained model; and finally, generating a hyperspectral image with high spatial resolution. According to the method, the parameter quantity required by fusion can be effectively reduced.

The invention discloses a construction method of a multi-scale double attention network for hyperspectral image classification. The construction method mainly comprises four key stages of input processing, multi-scale feature extraction, double attention enhancement and output classification. Wherein the multi-scale feature extraction module captures features of different scales through five parallel branches, and the dual attention mechanism cooperatively completes feature enhancement of a channel dimension and a space dimension. The invention provides an innovative hyperspectral image classification deep learning framework, and the core of the framework is to organically combine multi-scale feature learning and a dual attention mechanism. According to the network, aiming at the characteristics of a hyperspectral image, extraction of space-spectrum joint features and identification requirements of targets with different scales are fully considered in the aspect of architecture design, and meanwhile, the expression ability of key features is enhanced through an attention mechanism; therefore, the network can pay attention to local details and global semantic information at the same time, and the feature expression capability and the classification accuracy are remarkably improved.

The invention discloses a target trajectory tracking method and product based on azimuth statistical analysis and prediction, and the method comprises the steps: converting a horizontal array manifold vector according to a set interval, carrying out the array signal processing of the array manifold vector at each angle according to a set scanning interval, and obtaining the beam width at the angle; performing spatial spectrum synthesis on the horizontal array receiving data according to a plane wave array signal processing method to obtain a horizontal array data synthesis azimuth spectrum; presetting an initial azimuth angle of a to-be-tracked target, and solving a target azimuth angle at the current moment in combination with a target operation attribute corresponding to the azimuth angle, an array azimuth estimation error and a beam width; within the statistical time, adopting a least square method to predict a target azimuth angle, and adopting a target azimuth angle track compensation value to compensate a target azimuth angle prediction value; the target azimuth angle at the current moment is extracted according to the target azimuth angle predicted value, and then continuous and stable tracking of the target azimuth trajectory is completed.

The invention discloses a lightweight long-term spectrum prediction method based on a multi-scale mixed structure, and relates to the technical field of electromagnetic spectrum prediction. The method comprises the following steps: acquiring spectrum time sequence data, preprocessing, and dividing into a training set and a test set; a spectrum prediction model is constructed, the spectrum prediction model is composed of a trend decomposition module, a multi-scale coding module and a hybrid decoding module, the macroscopic trend and short-term fluctuation characteristics of spectrum data are captured through trend decomposition, multi-scale feature representation is generated through multi-scale coding, and a prediction value is generated through hybrid decoding fusion; expanding the model to spatial data, constructing a spatial spectrum data set, preprocessing, and inputting into the model for prediction. The long-term spectrum use condition can be accurately and efficiently predicted, the prediction precision is improved, the computing resource consumption is reduced, the generalization ability is high, and important support is provided for spectrum management.