63 results about "Hyperspectral image processing" patented technology

The invention discloses a hyperspectral unmixing method of a multi-scale collaborative attention network based on an initialized end member, and the method comprises the steps: an abundance estimation branch module extracts the spatial features and spectral features of an original hyperspectral image through a sequence module, and carries out the fusion of the spatial features and spectral features; the multi-scale collaborative attention module performs spatial enhancement and channel feature representation on the features output by the sequence module to obtain an estimated abundance matrix; the end member estimation branch module processes an original hyperspectral image through a vertex component analysis method to obtain an initialized learnable end member matrix, and optimizes the learnable end member matrix to obtain an estimated end member matrix; carrying out joint training on the estimated abundance matrix and the estimated end member matrix; in the training process, matrix multiplication is carried out on the estimated abundance matrix and the estimated end member matrix to obtain a reconstructed hyperspectral image, and a loss function is used to adjust the network parameter weight to optimize the network unmixing effect. According to the method, the problem of insufficient degree of freedom of flexibility of end member estimation can be solved, and spatial information and spectral information existing in the unmixing process are utilized.

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

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

The invention discloses a hyperspectral anomaly detection method based on background attention and anomaly suppression, and relates to the technical field of hyperspectral image processing, and the method comprises the following steps: S1, employing a pyramid similarity downsampling module to reduce the anomaly content in an image; s2, multi-scale background attention is calculated for the image after abnormity reduction; s3, performing pixel reconstruction on the obtained feature map through depth separable blind block convolution; and S4, using an l1 norm as a loss function, and using a reconstructed image difference to guide model training. And S5, reconstructing an image by using the trained model, and taking a reconstruction error as a detection result. According to the invention, a pyramid similarity down-sampling module is used for reducing the abnormal degree of an image; the multi-scale attention module is used for improving the attention of the network to the background; the depth separable blind block convolution shields the perception of the network on a central blind block, and reduces the possibility of abnormal reconstruction; the reconstructed image difference is used to determine an optimal training round.

The invention relates to a hyperspectral bacteria classification method based on lightweight deep learning and membership width learning, and belongs to the technical field of hyperspectral image processing and microbiological detection. The method comprises the following steps: collecting hyperspectral image data of food-borne pathogenic bacteria, and carrying out correction and region-of-interest extraction to obtain bacterial spectral data; the method comprises the following steps: preprocessing bacterial spectral data by using a fractional differential method, enhancing spectral features and suppressing noise to obtain enhanced spectral data, and inputting the enhanced spectral data into a lightweight deep network to extract multilevel space-spectral features to obtain depth features; inputting the depth features into a membership width learning module, and outputting a bacterial category classification result; wherein the membership width learning module comprises operations of defining a membership function, constructing a fuzzy score membership matrix and optimizing a width learning weight matrix. The objective of the invention is to solve the technical problems of insufficient feature extraction, outlier interference and high model complexity in hyperspectral bacterial classification in the prior art.

The invention discloses a perception method based on multi-semantic space attention and Daubechies wavelet double-branch Mama, and belongs to the field of hyperspectral image processing. The problem that in the existing Mama-based model feature extraction process, the capacity of capturing multi-scale local structures and direction sensing information is insufficient is solved. The method comprises the following steps: inputting a hyperspectral image; projecting the spectral vector to an embedding space through an embedding layer to obtain an embedding feature; inputting the embedded features into an encoder, wherein the encoder comprises an SMSAMama branch, a DWTMama branch and a self-adaptive feature fusion module; the SMSAMama branch is used for extracting spatial features; the DWTAMba branch is used for extracting spectral features; the adaptive feature fusion module performs weighted integration on the spatial features and the spectral features by using randomly initialized fusion weights; and inputting the integrated features into a segmentation head to generate a final perception result. The method is used in agricultural monitoring and urban planning fields.

The invention discloses a hyperspectral image segmentation method and device based on fusion point prompt and Markov diffusion, and relates to the technical field of hyperspectral image processing. The method comprises the following steps: performing spectrum-space dimension reduction processing according to hyperspectral initial data; on the basis of preset uniform distribution points, according to the spatial partitioning features, a spectrum-spatial feature adaptation module is used to carry out point prompt guided coarse segmentation; based on a cross double-attention mechanism, using a multi-modal fusion module to perform text-image feature fusion; performing multi-scale feature extraction by using a U-net encoder according to the hyperspectral initial data; diffusion reconstruction is carried out based on a symmetric codec convolutional network of a Markov diffusion model, and de-noised hyperspectral features and high-order fusion masks are extracted; and based on a cross entropy loss function, performing model optimization according to the segmentation prediction data. The hyperspectral image segmentation method is based on the text semantic features, fully considers the characteristics of the hyperspectral image, and is high in efficiency and robustness.

The invention relates to the technical field of hyperspectral image processing, and particularly discloses a hyperspectral image fusion method and system based on variance guidance and heavy tail estimation, and the method comprises the steps: obtaining a low-spatial-resolution hyperspectral image and a panchromatic image; performing up-sampling on the low-spatial-resolution hyperspectral image to obtain an up-sampled image; obtaining an absolute difference value weight according to the channel-by-channel variance of the low-spatial-resolution hyperspectral image and the up-sampling image; performing channel-by-channel spatial fusion by means of absolute difference weight, and injecting high-frequency information of the panchromatic image into the up-sampling image to obtain a preliminary fusion image; performing spectral correlation correction learning on the preliminary fusion image through an attention mechanism to obtain spectral mixed output; through a feedforward network, obtaining an up-sampling image subjected to variance guide processing; and obtaining a high-spatial-resolution hyperspectral image through a residual block and convolution. According to the invention, accurate selection and fusion of the spatial-spectral features are realized.

In order to explore the effectiveness of the functional data analysis method in the hyperspectral image processing, the application proposes a classification method combining the Gaussian regression mixture model and the MRF hyperspectral function data; first, the polynomial regression is used to fit the hyperspectral image pixel spectrum curve, so as to express the pixel spectrum information in the form of function; then, the neighborhood relationship is introduced to establish the Markov random field model, and the neighborhood Gaussian regression mixture model is established in combination with the Gaussian regression mixture model; finally, according to the maximum posterior probability criterion, the final hyperspectral image classification result is obtained. Since the spatial-spectral information of the hyperspectral image is fully combined, the algorithm has high-precision classification result, and effectively improves the classification performance of the hyperspectral image.

The present application relates to a hyperspectral image segmentation method based on spectral reflectance curve correlation difference, belonging to the field of hyperspectral image processing, comprising the following steps: black and white calibration of original crop hyperspectral data; using the method of logarithmic transformation combined with first derivative to pretreat the data; screening out the feature band with representative information based on information gain method; based on the correlation characteristics of pixel spectral reflectance curve, the image is segmented to obtain a binary image; the binary image after full-band spectral image segmentation is superimposed with the binary image after feature band spectral image segmentation; based on the closed operation processing, the segmentation burrs and gaps in the binary image are eliminated; the final segmentation image is obtained by mask processing with the result after closed processing as a mask. The present application improves the background segmentation accuracy of crop canopy hyperspectral image containing complex light interference, and can more accurately and quickly obtain hyperspectral target information.

The invention discloses a hyperspectral compression imaging method and system, a terminal and a storage medium in the technical field of hyperspectral image processing, and aims to solve the problem of low reconstruction precision caused by loss of high-frequency details, weak spectral correlation and poor anti-noise capability in a compression imaging process in the prior art. The method comprises the following steps: acquiring compression measurement and a sensing mask of a coded aperture snapshot spectral imaging system, and acquiring an initial feature map according to the compression measurement and the sensing mask; inputting the initial feature map into a pre-constructed U-Net model to obtain a decoded feature map; performing convolutional mapping on the decoded feature map, and obtaining a reconstructed hyperspectral image in combination with the initial feature map; according to the method, long-sequence data are efficiently processed through the spectrum-space state synchronizer, local and global features of the image can be effectively captured through multi-scale decomposition of the wavelet variance modulation block, and the problem that reconstruction precision is not high due to loss of high-frequency details, weak spectrum correlation and poor anti-noise capacity in the traditional compression imaging process is solved.

The application discloses an unsupervised hyperspectral image super-resolution method based on a matrix decomposition network and belongs to the technical field of hyperspectral image processing. The application is used for processing a hyperspectral image, generating a simulated low spatial resolution hyperspectral image Y and a high spatial resolution multispectral image Z; first, inputting the generated data pair (Y, Z) into a designed auto-encoder network, training iteration to obtain a point spread function and a spectral response function; for a target high spatial resolution hyperspectral image X, the target high spatial resolution hyperspectral image X can be assumed to be a linear combination of a terminal member matrix A and a corresponding abundance matrix S, that is, X = AS, a spectral and spatial degradation model is combined to model, a deep CP decomposition module is designed to calculate A, A and S are iteratively solved, and finally a fusion result is obtained. The application can obtain more rich spectral and spatial features, obtain a better fusion result, and has good performance in practice.

The invention discloses a single hyperspectral image super-resolution method based on a multi-scale cross-spectrum Transform network, and belongs to the technical field of hyperspectral image processing, and the method comprises the steps: providing a multi-scale cross-spectrum Transform network composed of a branch network and a backbone network; the method comprises the following steps: dividing low-resolution input into overlapped spectrum groups, extracting multi-scale spatial spectrum features through a branch network, performing up-sampling, and splicing to obtain local features; the backbone network expands and deepens features through a cross-channel Transform module, and obtains global features in combination with self-attention and convolution; and adding local and global feature residuals, performing up-sampling, fusing with a bicubic up-sampling result, and finally performing convolution to generate a super-resolution image. According to the single hyperspectral image super-resolution method based on the multi-scale cross-spectrum Transform network provided by the invention, the spatial resolution of the hyperspectral image is remarkably improved while the spectral consistency is ensured.

The application provides a hyperspectral image unmixing method, system, device and storage medium, belonging to the technical field of hyperspectral image processing, comprising: obtaining an original hyperspectral image; processing the original hyperspectral image by using a spectral unmixing network architecture, carrying out mixed pixel decomposition on the hyperspectral image, obtaining an endmember spectrum and an abundance map; in the spectral unmixing network architecture, a dynamic image block number allocation strategy is adopted to dynamically extract patches from the input original hyperspectral image, the patch feature sequence after dynamic allocation is converted into tokens of a unified dimension, and the tokens are input into a stacked Transformer Block module, and enhanced features with global semantic and structural features are output; the enhanced features are input into a decoder, and are restored into the endmember spectrum and the abundance map corresponding to the space of the input hyperspectral image. In a complex mixed pixel scene, the unmixing precision is significantly improved, unnecessary calculation processes are reduced, the inference efficiency of the model is improved, and the balance between precision and efficiency is realized.

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 invention discloses a hyperspectral end member extraction method based on leading edge surface exploration, relates to the technical field of hyperspectral image processing, and solves the technical problem of low accuracy of end member extraction in an existing end member extraction method. An original hyperspectral image is obtained, and an initial population is constructed; establishing a multi-objective function of initial population evolution, and generating a Pareto leading edge surface; performing end member extraction based on a Pareto leading edge surface to obtain an end member set; guiding the non-Pareto frontier individual to evolve to a reliable region where a high-quality end member is located by adopting a learning strategy based on double targets; meanwhile, integrating end members in the Pareto frontier individuals to construct an end member pool based on a solution generation strategy of the end member pool, generating a super individual by using a pure forming long algorithm, and disturbing the super individual to generate diversified offspring individuals with similar characteristics; improving the quality of the Pareto frontier individuals obtained in the global search stage by adopting a disturbance strategy; and the accuracy of end member extraction is improved.

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 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.

This invention relates to the field of medical hyperspectral image processing technology, and discloses a medical hyperspectral image classification method based on removing undesirable bands. The method includes: converting a three-dimensional medical hyperspectral image into a two-dimensional image; calculating the matched filter weight and information entropy weight for each band of the two-dimensional image; obtaining a fusion weight based on the matched filter weight and information entropy weight; sorting all bands of the two-dimensional image based on the fusion weight and target contribution, and filtering out undesirable bands; and performing classification operations on the two-dimensional image after removing undesirable bands. Based on the principles of matched filters and information entropy, the method calculates the average value of the absolutely normalized matched filter weights, and simultaneously calculates the average weight of the information entropy. The two weights are fused to evaluate each band for filtering, thus reducing the computational resource requirements and improving computational efficiency while ensuring classification accuracy.

This invention relates to a method for synchronous detection of road surface anomalies based on hyperspectral imaging, belonging to the field of hyperspectral image processing and remote sensing detection technology. It solves the problems of low data processing efficiency, limited detection functions, and insufficient real-time performance in existing technologies. The method includes: acquiring hyperspectral images of various road surfaces, extracting key features, generating hyperspectral fingerprints, labeling road surface categories, and constructing a hyperspectral fingerprint database; training a fingerprint comparison model based on the hyperspectral fingerprint database to obtain a pre-trained fingerprint comparison model; acquiring road hyperspectral images in real time, extracting key features, generating a hyperspectral fingerprint to be tested, and obtaining the road surface category of the hyperspectral fingerprint to be tested based on the hyperspectral fingerprint database, the pre-trained fingerprint comparison model, and a hierarchical comparison strategy. This enables simultaneous detection of multiple anomalies such as icing, water accumulation, and road surface deterioration on an UAV-borne terminal.

The embodiment of the invention discloses a hyperspectral image processing method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining crop images of a target crop, wherein the crop images comprise a first color image and a first depth image collected by a color depth camera and a first hyperspectral image collected by a hyperspectral camera; obtaining target joint calibration parameters of the color depth camera and the hyperspectral camera; and performing three-dimensional point cloud reconstruction on the first color image and the first depth image by using the target joint calibration parameter, and determining depth information corresponding to the first hyperspectral image based on first point cloud data obtained by reconstruction, the depth information being used for representing a first distance between the hyperspectral camera and the target crop when the hyperspectral camera collects the first hyperspectral image. According to the scheme, information of the color depth camera and information of the hyperspectral camera can be fused, and the first distance between the hyperspectral camera and the target crop is accurately determined when the hyperspectral camera collects the first hyperspectral image.

The application relates to a low-resolution hyperspectral image processing method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring shallow features of each grouping image of a low-resolution hyperspectral image; processing the shallow features of each grouping image based on a processing network; performing pixel addition processing on the shallow features of each grouping image and the processed shallow features of each grouping image; and obtaining global deep features of each grouping image. The processing network comprises a plurality of self-attention mechanism models. The global deep features of each grouping image are subjected to sub-pixel convolution processing, and the global deep features of each grouping image after the sub-pixel convolution processing are subjected to first convolution processing and cascade processing to obtain spectral features of the low-resolution hyperspectral image. According to the spectral features of the low-resolution hyperspectral image, a high-resolution hyperspectral image corresponding to the low-resolution hyperspectral image is obtained. The method can improve the target recognition accuracy.

The invention discloses a hyperspectral image classification method, system and device and a medium, and relates to the technical field of hyperspectral image processing. The invention aims to solve the problems that the utilization rate of spectral information is low, the internal relevance between a spectrum and a space is not fully utilized, and an existing feature fusion method cannot effectively integrate potential relevance between spectral features and spatial features, so that information interference is caused. The method comprises the following steps: extracting multi-scale spectral features of a to-be-classified hyperspectral image, and carrying out weighted fusion on the multi-scale spectral features; dividing the spectral features into a plurality of sub-blocks, applying a local block attention mechanism in the sub-blocks, and applying an inter-block attention mechanism between the sub-blocks; performing multi-scale shallow spatial feature extraction on the spectral features, and capturing global spatial features by using a sliding window; performing cross attention re-calibration and integration on the spectral features and the global spatial features to obtain fusion features; and carrying out global average pooling on the fused features, and realizing category prediction through a full connection layer and a Softmax classifier.

The application discloses a hyperspectral image classification method and system, an electronic device and a medium, and relates to the field of hyperspectral image processing. The method comprises the following steps: applying factor analysis to dimension reduction on a hyperspectral image to be classified, and extracting a plurality of three-dimensional pixel blocks from the dimension-reduced hyperspectral image to be classified with each pixel point as a center point in a preset area; and inputting the plurality of three-dimensional pixel blocks into a classification model to obtain a classification result of the hyperspectral image to be classified. The application can improve the accuracy of the classification result of the hyperspectral image.

The application discloses a large-scale hyperspectral image clustering method, system and equipment based on superpixel enhanced anchor point graph, and relates to the technical field of hyperspectral image processing. The hyperspectral image has the characteristics of large data scale and containing complex noise. In view of the problems of high calculation complexity, insufficient precision and robustness of the existing method, the application provides a large-scale hyperspectral image clustering method, system and equipment based on superpixel enhanced anchor point graph. The application improves the clustering efficiency and precision of large-scale hyperspectral image by integrating the superpixel enhanced anchor point graph of superpixel-level spatial-spectral information. The superpixel enhanced anchor point graph is decomposed under the correlation entropy criterion, and the robustness of the hyperspectral clustering task containing complex noise is improved. A large number of experiments show that the method of the application is significantly better than the prior art in clustering efficiency, precision and robustness, and provides an effective solution for large-scale hyperspectral image clustering, and can be used in the fields of precision agriculture, environmental monitoring, military reconnaissance, mineral exploration and the like.

The application relates to the technical field of hyperspectral image processing, and more particularly to a hyperspectral image classification model. The model aims to solve the calculation complexity and performance bottleneck in hyperspectral image processing. The model can be applied to the analysis of hyperspectral images in the fields of remote sensing, environmental monitoring, resource exploration and the like. Efficient dimension reduction processing is carried out by adopting random Fourier feature nonlinear principal component analysis (RFF-NLPCA), so that redundant information is effectively reduced and the calculation speed is accelerated. The model introduces light-weight spatial SSM Block and spectral SSM Block modules, and the feature modeling of the spatial dimension and the spectral dimension improves the image classification accuracy and robustness. The gating fusion module (GFM) optimizes the fusion capability of the spatial and spectral features, and improves the discriminant capability of the feature expression. Finally, the model can efficiently process high-dimensional hyperspectral data, provide more accurate classification results, has lower calculation cost and higher performance, and is particularly suitable for processing large-scale hyperspectral image data.

The application discloses a hyperspectral image anomaly detection method, device and medium, relates to the technical field of hyperspectral image processing, and comprises the following steps: acquiring a hyperspectral image, performing preliminary detection on the hyperspectral image to obtain a preliminary detection result, replacing the pixel value of each target pixel point in the hyperspectral image and the pixel value of a neighborhood pixel point to obtain an initial background image, taking the initial background image as input, training an anti-similarity optimization coding network under the constraints of reconstruction loss and anti-similarity loss to obtain a trained model, and reconstructing the initial background image by using the trained model to obtain a reconstructed background image, calculating the difference between the hyperspectral image and the reconstructed background image to obtain a hyperspectral anomaly detection image. The application can significantly improve the accuracy and robustness of target detection, and better cope with detection requirements in various complex scenes.

The present application relates to hyperspectral image processing technical field, disclose a kind of fast medical hyperspectral image classification method, comprising: extracting several pixels as training sample from the medical hyperspectral image to be classified;Set initial neighborhood window scale;According to initial neighborhood window scale, randomly select joint training area from the medical hyperspectral image to be classified;The similarity result between each pixel in joint training area and training sample is obtained by joint classification algorithm calculation;By k times iteration cross-validation algorithm, the initial neighborhood window scale is optimized, and the best neighborhood window scale is obtained;Using the best neighborhood window scale and joint classification algorithm, all pixels of the medical hyperspectral image to be classified are classified.Cotangent mapping cosine similarity can effectively reduce the interference of heterogeneous pixels, and local similarity data gravity can adaptively balance the contribution of minority and majority, to ensure the full use of MHSI spatial spectral information.

The invention discloses a hyperspectral image anomaly detection method and device and a medium, and relates to the technical field of hyperspectral image processing, and the method comprises the steps: obtaining a hyperspectral image, carrying out the preliminary detection of the hyperspectral image, obtaining a preliminary detection result, and for each target pixel point in the hyperspectral image, carrying out the detection of the hyperspectral image; replacing the pixel value of the target pixel point and the pixel value of the neighborhood pixel point to obtain an initial background image, taking the initial background image as an input, training the reverse similarity optimization coding network under the constraint of reconstruction loss and reverse similarity loss to obtain a trained model, and obtaining a target image; and reconstructing the initial background image by using the trained model to obtain a reconstructed background image, and calculating a difference value between the hyperspectral image and the reconstructed background image to obtain a hyperspectral anomaly detection image. According to the method, the accuracy and robustness of target detection can be remarkably improved, so that detection requirements in various complex scenes can be better met.

The application discloses a hyperspectral compression imaging method, system, terminal and storage medium in the technical field of hyperspectral image processing, and aims to solve the problem of low reconstruction accuracy caused by high-frequency detail loss, weak spectral correlation and poor noise resistance in the compression imaging process in the prior art. It comprises obtaining the compression measurement and the perception mask of the coded aperture snapshot spectral imaging system, obtaining the initial feature map according to the compression measurement and the perception mask; inputting the initial feature map into the pre-constructed U-Net model to obtain the decoding feature map; and performing convolution mapping on the decoding feature map to obtain the reconstructed hyperspectral image in combination with the initial feature map; the spectral-space state synchronizer efficiently processes long sequence data, and the wavelet variance modulation block can effectively capture the local and global features of the image through multi-scale decomposition, thereby solving the problem of low reconstruction accuracy caused by high-frequency detail loss, weak spectral correlation and poor noise resistance in the traditional compression imaging process.