630 results about "Spatial spectrum" patented technology

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 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 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 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 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 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 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.

According to the method, firstly, SAR data are mapped to an optical image domain by using a conditional diffusion model, pseudo-optical images with consistent spatial spectrums are generated, and fusion distortion caused by difference of SAR imaging mechanisms in a traditional method is overcome; secondly, a refined cloud region detection mechanism of a Fmask cloud mask is introduced, a cloud pollution region and a cloudless region are dynamically distinguished in combination with an adversarial training strategy, and the problem that the cloudless region is mistakenly changed in the reconstruction process of an existing method is effectively solved; besides, a multi-source integrated sample data set is constructed in stages, and a training normal form of multi-index joint optimization of PSNR, SSIM and the like is adopted, so that texture detail recovery and spectrum fidelity of a thick cloud coverage area are realized in a complex scene. In downstream application tasks such as land utilization classification and disaster dynamic monitoring, the visual quality and the quantitative index of the cloud removal result have good effects, and reliable technical support is provided for high-precision reconstruction of remote sensing information of a multi-cloud area.

The invention relates to the technical field of spectrum detection, and discloses a quality control method for traditional Chinese medicine capsules, which comprises the following steps: continuously scanning by using a micro light spot probe during capsule movement to obtain a spatial spectrum response sequence; performing adjacent micro-area differential operation on the sequence, and filtering a shell background and retaining a particle scattering signal by utilizing a microstructure difference between a capsule shell continuous film and a powder content discrete accumulation; according to the method, a traditional path of a shell standard model is established, through a spatial frequency domain decoupling mechanism, reference errors caused by batch drifting of shell physical attributes are avoided, and the detection sensitivity of trace component fluctuation and foreign matter mixing is improved.

The invention discloses an unmanned aerial vehicle hyperspectral image object-level target detection method based on spatial-spectral decoupling and double-flow interactive fusion, belongs to the technical field of remote sensing image processing and computer vision, and particularly relates to an object-level target detection method of a hyperspectral image. The objective of the invention is to solve the problems of low detection precision and robustness and the like caused by pixel-by-pixel detection, insufficient spatial spectrum information fusion and insufficient complex scene adaptability in an existing unmanned aerial vehicle hyperspectral target detection method. The method comprises the following steps: step 1, acquiring a hyperspectral image of an unmanned aerial vehicle; step 2, inputting the hyperspectral image into a hyperspectral decoupler, and outputting spatial features and spectral features by the hyperspectral decoupler; 3, inputting the spatial features and the spectral features output by the hyperspectral decoupler into a spatial-spectral feature extraction and fusion module, and outputting the features by the spatial-spectral feature extraction and fusion module; and 4, inputting the features into a detection head, and outputting a detection result by the detection head.

The invention belongs to the technical field of spectrum separation identification and spatial information analysis, and discloses a flame environment-oriented multi-depth spatial spectrum light ray measurement system, which comprises a light ray collection module used for converging dynamic flame radiation light rays; the spectrum separation module is used for spatially separating a plurality of different wavelengths of the flame radiation light converged by the light collection module to form multi-wavelength radiation light with spatially separated wavelengths; the space light splitting module is used for uniformly splitting the multi-wavelength radiation light subjected to wavelength space separation into a plurality of light beams and transmitting the light beams to the multi-spectral imaging unit; the multispectral imaging unit comprises a plurality of imaging modules, and each imaging module is used for receiving radiation information of one light beam and performing imaging; and the light analysis module is used for calculating and obtaining multi-wavelength original radiation information of different depth positions of the flame. According to the invention, multi-wavelength radiation information of different depths in a flame three-dimensional space can be obtained, and a basis is provided for establishing a flame environment temperature field model.

The invention discloses an underwater sound environment sensing method based on multi-array-element sparse channel estimation. An underwater sound receiving end receives signals transmitted through an underwater sound multipath channel through a multi-array-element array; performing Hilbert transform on the receiving signal of each array element to obtain an analysis signal, and calculating a cross-correlation function of the analysis signal and the transmitting signal; based on the cross-correlation function, sparse channel parameters, including path amplitude and time delay, of each array element are estimated by adopting an orthogonal matching pursuit algorithm combined with a constant false alarm detection dynamic threshold value; the method comprises the following steps: constructing an array response vector by using sparse channel parameters of a multi-array element array, and searching and estimating angles of arrival, including a direction angle and a pitch angle, of a multipath signal through a spatial spectrum peak value; and based on the arrival angles, amplitudes and time delays of the direct path and the reflection path, inverting an underwater environment structure through a ray acoustic theory, including a reflection point distance and a reflection surface normal vector, so as to realize three-dimensional perception of the underwater reflector. According to the invention, multipath resolution can be improved, and false alarm and missing detection can be effectively reduced.

The invention provides a hyperspectral image reconstruction method based on space-spectral characteristic fusion. The method comprises the following implementation steps: acquiring a training sample set and a test sample set; constructing a reconstruction network model based on space-spectral characteristic fusion; performing iterative training on the reconstructed network model; and obtaining a reconstruction result of the hyperspectral image. According to the invention, the spatial-spectral characteristic guiding network in the encoder carries out spatial-spectral characteristic extraction on input characteristics, the spatial-spectral characteristic guiding network in the bottleneck layer carries out spatial-spectral characteristic fusion, the spatial-spectral characteristic guiding network in the decoder carries out spatial-spectral characteristic detail recovery, spatial structure information and spectral distribution information in the input characteristics are fully extracted, and the spatial-spectral characteristic guiding network in the decoder carries out spatial-spectral characteristic fusion. The spatial features and the spectral features cooperatively participate in the reconstruction process, and cooperative recovery of the spatial information and the spectral information of the hyperspectral image is realized, so that the reconstruction precision of the hyperspectral image is effectively improved.

The invention discloses a coarse cereal aflatoxin detection method combining hyperspectral imaging and deep learning, and belongs to the field of agricultural product quality safety detection. The method comprises the following steps: firstly, acquiring hyperspectral imaging data of coarse cereal grains, extracting space and spectral information, forming basic data, and performing preprocessing, space-time registration and feature alignment; building a hyperspectral space-spectrum double-branch feature fusion convolutional neural network deep learning model, and importing the processed data to complete iterative training; and finally, performing same-standard data acquisition and processing on to-be-detected coarse cereals, importing the trained model, and realizing aflatoxin detection and pollution area positioning through pixel-level analysis. According to the method, deep fusion of space and spectral features is realized, the detection accuracy and generalization ability are effectively improved, the detection result is stable, reliable and traceable, and the fine detection requirement of aflatoxin in coarse cereals is met.

The invention relates to a wound surface mixed bacteria unmixing method and system based on a double-branch attention mechanism, and belongs to the technical field of biomedical engineering and image processing. The method comprises the following steps: S1, acquiring hyperspectral images of a single strain culture dish, a blank culture dish and a mixed strain wound sample; s2, performing data extraction and preprocessing on the sample hyperspectral image, enhancing the data based on a spectral linear hybrid model and an adversarial generative network, and generating and synthesizing mixed bacteria hyperspectral data; s3, constructing a mixed bacteria unmixing model based on a space-spectrum double-branch attention mechanism; s4, inputting the synthesized mixed bacteria spectrum data into a mixed bacteria unmixing model for training, and optimizing parameters of the model according to the minimum total loss function of the model; and S5, inputting the hyperspectral data of the mixed bacteria wound sample to be unmixed into the trained mixed bacteria unmixing model, and outputting the unmixing abundance of the sample and the corresponding end metainformation.

The invention discloses a farmland boundary automatic identification method for an agricultural unmanned aerial vehicle, and the method comprises the steps: comprehensively collecting multi-mode remote sensing data of a visible light image, a near-infrared image and a laser point cloud, combining airborne real-time positioning and attitude determination information, and carrying out the deep fusion through spatial spectrum features and elevation features, thereby obtaining a multi-modal remote sensing image; accurate recognition and vectorization output of farmland boundaries are achieved, and the problems that in the prior art, recognition precision is insufficient, the anti-interference capacity is weak, and universality of complex land parcels is poor are effectively solved.

The invention relates to the field of array signal processing, and particularly discloses a DOA joint estimation method based on a quaternion polarization sensitive array. Electromagnetic wave dual polarization components are captured through the orthogonal dipole and the loop antenna, and a quaternion observation matrix is constructed; calculating a quaternion covariance matrix by adopting a sliding window mechanism, and separating a signal / noise subspace by adopting quaternion singular value decomposition; and constructing a spatial spectrum function in combination with a quaternion steering vector, and realizing joint estimation of an azimuth angle and a pitch angle through two-dimensional search. According to the method, the unified characterization capability of quaternions on polarization-airspace information is fully utilized, and the DOA estimation precision and the anti-interference performance of the multi-polarization signal are remarkably improved.

The invention relates to the technical field of multispectral image fusion, and provides a multispectral image fusion method and system based on spatial spectral difference prior guidance, and the method comprises the steps: carrying out the up-sampling of an LRMS image, carrying out the subtraction and splicing of the LRMS image and a panchromatic image in a waveband-by-waveband manner, and obtaining a spatial difference input feature map; extracting a multi-scale spatial feature map through spatial difference prior guide branches; subtracting the up-sampling LRMS image from the degraded panchromatic image band by band, and splicing with the up-sampling LRMS image to obtain a spectral difference input feature map; extracting a multi-scale spectral feature map through a spectral difference prior guide branch; and fusing the multi-scale spatial feature map and the multi-scale spectral feature map through the spatial spectral feature fusion branch to obtain an HRMS image. According to the method, the spectral and spatial difference information of the panchromatic image and the LRMS image at the pixel level is used as priori, the extraction and fusion of complementary information are guided, and the accurate reconstruction of the high-resolution multispectral image is realized.

The invention provides a remote sensing image classification method based on a spatial spectrum attention network, and relates to the technical field of remote sensing image processing, and the method comprises the steps: obtaining a hyperspectral remote sensing satellite image, carrying out the data preprocessing, and constructing a hyperspectral remote sensing image classification data set; constructing a classification model DSAFNet based on a double-branch spectrum-space attention fusion network, and taking the classification model DSAFNet as an initial hyperspectral classification model; training the initial hyperspectral classification model through the hyperspectral remote sensing image classification data set to obtain a trained classification network model; obtaining a to-be-classified hyperspectral remote sensing image; and inputting a to-be-classified hyperspectral remote sensing image into the trained classification network model to obtain an image classification result. According to the technical scheme, generation of the hyperspectral remote sensing image classification model with high recognition accuracy and efficiency is realized.

The invention discloses a FDA-MIMO radar multi-target distance angle joint super-resolution method and system, and mainly solves the problem that the existing phased array only depends on the angle to identify multiple targets, resulting in low resolution ability, and the implementation scheme of the method comprises the steps of obtaining FDA-MIMO radar echo data, and performing separation aliasing on the FDA-MIMO radar echo data to obtain a data matrix; performing conversion stacking on the data matrix to obtain NM * L-dimensional data, and calculating a covariance matrix of the NM * L-dimensional data; performing reverse array decoherence processing on the covariance matrix to obtain a new source signal covariance matrix; constructing a two-dimensional spatial spectrum function of a target distance and a target angle for the new source signal covariance matrix through a MUSIC algorithm; and performing spectrum peak search on the two-dimensional spatial spectrum function, and detecting a local maximum value of the two-dimensional spatial spectrum function to obtain a distance and angle joint estimation value of a plurality of targets. The method can improve the angle resolution capability of the target when the target angle interval is lower than the traditional resolution, reduces the influence of the amplitude-phase error on the resolution capability when the target distance interval is large enough, and can be used for target recognition.

The invention discloses a vector hydrophone array orientation estimation method for performing tensor decomposition by using a propagation operator, relates to the technical field of vector hydrophone array orientation estimation, and discloses a vector hydrophone array orientation estimation method for performing tensor decomposition by using a propagation operator. The method comprises the following steps: firstly, constructing a three-dimensional array manifold tensor composed of an array direction matrix and a vector hydrophone output matrix; respectively expanding received signal tensors according to three modes, solving a propagation operator based on a column block covariance matrix, constructing a normalized signal subspace, establishing a spatial spectrum function with a noise subspace, and obtaining a pitch angle and an azimuth angle of a sound source through spectrum peak search; according to the method, high-order singular value decomposition is avoided, the operand is greatly reduced, meanwhile, high resolution and low sidelobe direction finding performance are kept, and the method is suitable for a real-time underwater acoustic direction finding system of a ship-borne platform, a buoy platform and an unmanned platform.