38 results about "Hyperspectral image fusion" patented technology

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 invention relates to the technical field of vision assistance, and discloses an anesthesia puncture positioning method and system based on vision assistance, and the method comprises the steps: accurately positioning an anesthesia puncture point through multi-view image fusion, hyperspectral image processing, image preprocessing, illumination equalization, edge enhancement, depth feature extraction and the like. The method comprises the following steps: firstly, constructing an image coordinate system, collecting a plurality of camera images, and obtaining a fused clear image through a designed multi-view fusion algorithm and distance calculation; and in combination with a hyperspectral image fusion algorithm, the image quality is further improved. Then, residual mapping filtering denoising and local histogram enhancement are used for illumination equalization, and the image contrast and edge details are enhanced; a convolutional neural network is adopted, interest point detection is carried out, a Hessian matrix is utilized to describe image second-order changes, and local depth features are extracted. And accurate positioning of an anesthesia puncture point is realized through a weighted soft voting classifier and a dynamic threshold method.

The invention discloses a double-branch hyperspectral spatial super-resolution network model product, a training method and application, and belongs to the field of image processing. The double-branch hyperspectral spatial super-resolution network model comprises a demixing branch network, a non-demixing branch network and a fusion network; the unmixing branch network comprises an unmixing module, an abundance image super-resolution module and a linear mixing module, and is used for processing and outputting an unmixing super-resolution hyperspectral image; the non-unmixing branch network comprises a spectrum dimension reduction module and a hyperspectral image super-resolution module and is used for processing and outputting a non-unmixing super-resolution hyperspectral image; and the fusion network performs spectrum dimension raising on the non-unmixing super-resolution hyperspectral image, and performs cascading and grouping convolution dimension reduction corresponding to each wave band on the image after dimension raising and the unmixing super-resolution hyperspectral image to obtain a high-resolution hyperspectral image. And the combination effect of the spatial-spectral information is improved, so that the spatial super-resolution quality of the hyperspectral image is improved.

The invention relates to the technical field of hyperspectral image processing, and provides a hyperspectral image fusion method and system based on hyper-band aggregation and Dirichlet, and the method comprises the steps: obtaining an LRHS image through a remote sensing optical imaging system; fusing the LRHS image and the panchromatic image through a spatial spectrum data distribution fusion module to obtain a PANHS image; carrying out band aggregation on the LRHS through an aggregation algorithm, calculating a compression association graph according to a hyper-band, and extracting a current state feature according to a cyclic memory network; memory state characteristics are obtained through the Dirichlet auto-encoder module; fusion features are obtained through a cyclic memory network; integrating the fusion features into the PANHS image to obtain integrated features; and obtaining a hyperspectral image with high spatial resolution through the residual block. According to the method, the spatial resolution of the hyperspectral image is remarkably improved, meanwhile, the spectral consistency is kept, and technical support is provided for refined analysis and practical application of the hyperspectral image.

The invention relates to the technical field of image data processing, in particular to a multiband hyperspectral image fusion method for predicting surface moisture of coal, which comprises the following steps: acquiring visible light and near-infrared images and calculating reflection difference frequency, constructing a grade layer and then executing region mapping and brightness superposition, extracting pixel features and sorting and dividing threshold regions, and identifying boundary change and generating a graph block, and finally reconstructing a moisture distribution graph surface according to a gray scale. According to the method, pixel-level distinguishing is carried out through reflection differences of visible light and near-infrared images, hierarchical layers are constructed based on multi-band reflection changes, layer stacking and classification are completed in combination with characteristics such as brightness, edge changes and space concentration ratio, moisture region boundaries are delimited, and moisture level maps are generated according to gray scale sorting. Fine grading and boundary positioning of the moisture content are realized, the prediction precision is improved, the problems of single-band processing limitation and fusion information loss are solved, and the identification capability of the moisture area in the complex coal surface environment is enhanced.

A mining conveyor scanner including a scanning system that comprises a support for supporting the scanning system to scan ore carried by a conveyor. Advantageously, the scanning system safely scans the ore on the conveyor, as it moves in its normal course of operation, without flow interruption. Also, a method for detecting changes in the grade of ore carried by a conveyor in near real time, the method including the steps of: providing a scanning system having at least a hyperspectral imager, a LiDAR or range determination unit and computational resources; scanning the ore with the range determination unit to determine ore position information; scanning the ore with the hyperspectral imager to produce a corresponding ore hyperspectral image; utilising the computational resources to fuse together the ore position information and the corresponding ore hyperspectral image to produce a ore position and content information map of the ore.

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.

The application discloses a kind of intraoperative tumor rapid classification system based on hyperspectral technology, comprising: image pre-processing module, for the hyperspectral image of several frequency bands of acquired tumor lesion tissue is pre-processed;Tumor lesion area identification module is used to select the hyperspectral image corresponding to multiple preferred frequencies respectively, fusion obtains fusion spectrum image, tumor target detection is carried out based on the image, and tumor lesion area is identified;Tumor lesion type identification module is used to carry out multi-pixel sampling to the tumor lesion area identified, obtains the frequency characteristics of each preferred frequency of each sampling point, and respectively input benign and malignant tumor identification model, respectively output benign and malignant possibility matrix, to identify tumor lesion type.The present application discriminates tumor benign and malignant based on tumor lesion tissue hyperspectral image, realizes the rapid classification of intraoperative tumor lesion type, reduces the rapid identification time, improves the accuracy of identification result while improving the identification efficiency.

The invention relates to the technical field of hyperspectral image processing, and provides a hyperspectral image fusion method and system based on non-uniform features and dynamic convolution, and the method comprises the steps: extracting the image features of a PAN image and an up-sampling LRHS image through a convolution module; performing feature extraction on PAN image features through spatial curvature dynamic convolution, and performing feature extraction on up-sampling LRHS image features through spectral curvature dynamic convolution; performing feature fusion through a non-uniform feature integration unit, and performing feature fusion on the output feature and the spectral convolution feature to obtain a current scale fusion feature; performing multi-scale feature extraction and fusion on the current scale fusion features and the spatial convolution features through a non-uniform fusion module and jump connection; and performing residual operation on the multi-scale fusion features to obtain a hyperspectral image. According to the method, the spatial resolution of the hyperspectral image is remarkably improved, and technical support is provided for fine analysis and practical application of the hyperspectral image.

The present invention discloses a data structure for a hyperspectral image fusion algorithm based on data-oriented integration, which includes the following main data layers: a low-resolution hyperspectral and multispectral input data layer; a spectral dictionary data layer; a spatial coefficient matrix data layer; and a solution space data layer representing fusion result parameters. Each data layer interacts in sequence and has global compatibility. This data structure is used in a data-based fusion algorithm for improving the spatial resolution of hyperspectral images. The algorithm optimizes the spatial coefficients based on the data attributes of the hyperspectral and multispectral images in combination with external additional data, learns the spectral dictionary using a non-negative matrix dictionary learning method, and performs algorithmic fusion on the hyperspectral and multispectral images to obtain a hyperspectral image with a target high spatial resolution.

This invention proposes a hyperspectral image fusion and reconstruction method based on cross-modal interaction and difference perception. The steps are as follows: acquiring training and testing sample sets; constructing a reconstruction network model based on cross-modal interaction and difference perception; iteratively training the reconstruction network model; and obtaining the reconstruction results of the hyperspectral image. The residual module of this invention utilizes multiple cross-modal interaction modules (CIMB) to perform cross-modal collaborative interaction on the spectral-spatial feature maps of LR-HSI and HR-MSI, enabling the mining of interaction features and contextual features between the two modalities. The difference perception fusion module (DPFM) performs difference perception and fusion on the cross-modal interaction features output by CIMB, effectively enhancing key details while mitigating distortion caused by differences between the two modal features, thus significantly improving reconstruction accuracy.

The application discloses a multispectral and hyperspectral image fusion method based on a spatial frequency collaborative network, relates to the technical field of remote sensing, and discloses a multispectral and hyperspectral image fusion method based on a spatial frequency collaborative network, which comprises the following steps: acquiring a multispectral image and a hyperspectral image of a remote sensing image; based on a CSAM module in a preset fusion model, capturing a spatial dependence relationship of spatial branch features of the multispectral image and the hyperspectral image to obtain spatial features; based on an SFBM module in the preset fusion model, determining frequency dependence of the spatial branch features to obtain frequency features; and based on a convolution layer in the preset fusion model, fusing the spatial features and the frequency features to obtain a fusion image of the multispectral image and the hyperspectral image. Through integration of spatial domain and frequency domain features, the model can more accurately capture and fuse complex information in the multispectral and hyperspectral images, and the quality of image fusion is significantly improved.

The invention discloses a cultivated land planting purpose change monitoring and processing method based on hyperspectral remote sensing, and the method comprises the steps: sequentially detecting the membership condition of a certain crop for cultivated land planting in a certain permanent basic farmland according to a planted crop detection index; and if the membership condition corresponding to a certain crop is greater than or equal to the planting condition membership degree, outputting and reporting the crop as a currently planted crop, otherwise, detecting the membership conditions of different farmlands in non-farming states. According to the invention, the conventional monitoring processing of the change of the cultivated land planting purpose of the permanent basic farmland can be met, so that the conditions of long image processing time, low response speed and the like after the panchromatic image and the hyperspectral image are fused in the prior art are relieved, and the calculation cost is effectively reduced.

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

A mining track scanner including a scanning system that comprises a support for supporting the scanning system to scan ore carried by a truck. Advantageously, the scanning system safely scans the ore on the truck, as it moves about the mine, without interruption to truck fleet movement. Also, a method for detecting changes in the grade of ore carried by a truck in near real time, the method including the steps of: providing a scanning system having at least a hyperspectral imager, a LiDAR or range determination unit and computational resources; scanning the ore with the range determination unit to determine ore position information; scanning the ore with the hyperspectral imager to produce a corresponding ore hyperspectral image; utilising the computational resources to fuse together the ore position information and the corresponding ore hyperspectral image to produce a ore position and content information map of the ore.

The invention provides a hyperspectral image fusion method and device for image adaptive registration, and relates to the technical field of image processing, and the method comprises the steps: taking a first hyperspectral image as a reference, carrying out the feature point extraction and matching based on the first hyperspectral image and a first multispectral image, and obtaining a projection matrix; performing projection transformation processing on the first multispectral image based on the projection matrix to obtain a second multispectral image; determining a spectral response function based on the second multispectral image and the first hyperspectral image; estimating a spectrum dictionary in the first hyperspectral image based on a VCA algorithm; estimating a sparse coefficient matrix in the first multispectral image based on the spectral response function and the spectral dictionary; constructing a second hyperspectral image based on the spectral dictionary and the sparse coefficient matrix; wherein the spatial resolution of the first multispectral image is higher than that of the first hyperspectral image. Therefore, the spatial resolution of the fused image is remarkably improved while the spectral information integrity of the hyperspectral image is kept.

The present invention relates to the technical field of hyperspectral image processing, and provides a hyperspectral image fusion method and system based on superband aggregation and Dirichlet. The method comprises acquiring a LRHS image through a remote sensing optical imaging system; fusing the LRHS image with a panchromatic image through a spatial spectral data distribution fusion module to obtain a PANHS image; performing band aggregation on the LRHS through an aggregation algorithm, calculating a compressed correlation graph based on superbands, and extracting current state features based on a recurrent memory network; obtaining memory state features through a Dirichlet autoencoder module; obtaining fusion features through a recurrent memory network; integrating the fusion features into the PANHS image to obtain integrated features; and obtaining a high-spatial-resolution hyperspectral image through a residual block. The present invention significantly improves the spatial resolution of hyperspectral images while maintaining spectral consistency, providing technical support for the refined analysis and practical application of hyperspectral images.

This invention provides a hyperspectral image fusion method based on a dual-resolution residual mutual guidance network, comprising: Step 1, establishing a training dataset of hyperspectral images and visible light images; downsampling all visible light images in the training dataset and forming low-resolution data pairs with the hyperspectral images in the training dataset; upsampling all hyperspectral images in the training dataset and forming high-resolution data pairs with the visible light images in the training dataset; Step 2, obtaining the fusion result of the hyperspectral images and visible light images in the high-resolution and low-resolution data pairs datasets through a dual-resolution residual mutual guidance network model; Step 3, training the dual-resolution residual mutual guidance network model using the training dataset using a stochastic gradient descent algorithm to obtain a fusion model. Finally, testing on data in a test dataset shows that this invention effectively improves the final image fusion result.

The invention belongs to the technical field of image processing, and relates to a deep Kalman filtering-based high-multispectral image fusion method, which comprises the steps of constructing a training sample, creating a deep Kalman filtering-based high-multispectral image fusion network, carrying out network training, and carrying out high-multispectral image fusion by utilizing a trained network model. And taking the preprocessed hyperspectral image and multispectral image as joint input, and generating a fusion result image with both high spatial resolution and hyperspectral information through a nonlinear mapping relation of a network. The network is composed of an encoder module, an implicit feature fusion module, an optical flow estimation module, a Kalman gain estimation module and a Kalman recursion fusion module. According to the method, the spectral dimension dynamic evolution relation between wavebands is explicitly modeled through Kalman filtering, and the expression consistency of the fused image in the spectral dimension is improved; according to the method, the spatial detail reconstruction capability can be effectively enhanced; and the model parameter scale and the redundancy calculation overhead are remarkably reduced while the spatial spectrum modeling precision is ensured.

The application discloses a hyperspectral image fusion method based on multi-scale multi-head attention, comprising the following steps: step 1, data preprocessing of the LR-HSI image; step 2, connecting the RGB image and the preprocessed LR-HIS image in the channel dimension to obtain the connected features; step 3, extracting the shallow features from the connected features through the multi-scale convolution module; step 4, inputting the shallow features into the feature interaction module to realize the information fusion and interaction between the features; step 5, obtaining the residual of the HR-HSI through the information fusion module from the output of the feature interaction module; and step 6, adding the residual of the HR-HSI and the LR-HSI image to obtain the high-resolution hyperspectral image. The multi-scale feature extraction module is used to obtain rich and multi-scale feature information from the input data, the calculation resource demand is significantly reduced while the reconstruction quality is ensured, and a more practical and efficient technical path is provided for the hyperspectral image processing.

The present invention relates to the field of spectral measurement technology, and specifically to an ultra-high grayscale intelligent hyperspectral imaging method and device, wherein the ultra-high grayscale intelligent hyperspectral imaging method includes the following steps: S1: initializing the intelligent hyperspectral imaging device; S2: splitting the captured light signal image into at least two beams according to a preset light intensity distribution ratio before focusing, and focusing them on different slits for shaping, and then pre-processing each split light beam; S3: performing multiple exposure acquisition on several light beams to obtain hyperspectral image data for each light beam; S4: fusing several frames of hyperspectral image data according to anchor points to complete ultra-high grayscale hyperspectral image fusion imaging. The present invention realizes multi-band hyperspectral image imaging with a common imaging window by means of light splitting, greatly expanding the hyperspectral band range, and simultaneously adopts a multiple exposure and fusion method to obtain ultra-high grayscale hyperspectral images.

The application discloses a kind of dual-branch hyperspectral spatial super-resolution network model product, training method and application, belong to image processing field.Dual-branch hyperspectral spatial super-resolution network model includes unmixing branch network, non-unmixing branch network and fusion network;Unmixing branch network includes unmixing module, abundance map super-resolution module and linear mixing module, and process output unmixing super-resolution hyperspectral image;Non-unmixing branch network includes spectral dimension reduction module and hyperspectral image super-resolution module, and process output non-unmixing super-resolution hyperspectral image;Fusion network carries out spectral dimensionality to non-unmixing super-resolution hyperspectral image, and after image and unmixing super-resolution hyperspectral image are upgraded, each waveband corresponding cascade and grouping convolution dimension reduction are carried out, and high-resolution hyperspectral image is obtained.The combination effect of air spectrum information is improved, so as to improve the quality of hyperspectral image spatial super-resolution.

The present invention relates to the technical field of hyperspectral image processing, and provides a hyperspectral image fusion method and system based on non-uniform features and dynamic convolution. The method comprises extracting image features of a PAN image and an upsampled LRHS image through a convolution module; extracting features of the PAN image through spatial curvature dynamic convolution, and extracting features of the upsampled LRHS image through spectral curvature dynamic convolution; performing feature fusion through a non-uniform feature integration unit, fusing output features with spectral convolution features to obtain current-scale fusion features; performing multi-scale feature extraction and fusion of current-scale fusion features and spatial convolution features through a non-uniform fusion module and a jump connection; and performing a residual operation on the multi-scale fusion features to obtain a hyperspectral image. The present invention significantly improves the spatial resolution of hyperspectral images, and provides technical support for the refined analysis and practical application of hyperspectral images.

The application belongs to the technical field of image processing, and relates to a hyperspectral image fusion method based on deep Kalman filtering, which comprises the following steps: constructing a training sample, creating a hyperspectral image fusion network based on deep Kalman filtering, training the network, using the trained network model, taking the preprocessed hyperspectral image and multispectral image as joint input, and generating a fusion result image with high spatial resolution and high spectral information through the nonlinear mapping relationship of the network. The network of the application is composed of an encoder module, an implicit feature fusion module, an optical flow estimation module, a Kalman gain estimation module and a Kalman recursive fusion module. The application explicitly models the spectral dimension dynamic evolution relationship between bands through Kalman filtering, thereby improving the expression consistency of the fusion image in the spectral dimension. The method can effectively enhance the spatial detail reconstruction capability, and significantly reduce the model parameter size and redundant calculation cost while ensuring the accuracy of the space-spectrum modeling.

The present invention relates to a method for detecting broken plastics and microplastics based on RGB and hyperspectral image fusion, comprising the following steps: obtaining broken plastics and microplastics; mixing them with solid waste to obtain a solid-phase matrix; pre-treating the obtained solid-phase matrix to obtain a material; drying the material to remove some moisture, coating it on a quartz window, drying it until the moisture is completely removed, and flattening it with another quartz window to obtain a test material; using a high-resolution color image scanner and a hyperspectral camera to obtain an RGB image and a hyperspectral image of the obtained test material, respectively; fusing the obtained RGB image and hyperspectral image; and automatically classifying and identifying broken plastics and microplastics using a supervised classification model. Compared with existing technologies, the method provided by the present invention does not require separation steps such as density flotation, which can reduce the loss of broken plastics and microplastics during pre-treatment; it shortens detection time and can achieve high-throughput analysis of large sample sizes; and it can effectively broaden the size range of broken plastics and microplastics and improve recognition accuracy.

The present invention proposes a method for detecting small targets in complex sea conditions based on multimodal fusion, belonging to the technical field of maritime target detection. The method includes: Step S1, in the image fusion stage, based on the adaptive-Laplacian fusion strategy, fusing infrared images, visible light images, and hyperspectral images into an adaptive image; Step S2, in the target detection stage, calculating a feature fusion layer through the downsampling layer of the encoder and the convolutional layer of the dense block to achieve hierarchical feature fusion for the adaptive image; Step S3, in the target detection stage, performing target detection on the fused features through a target detection model; wherein, excluding other pixels that have no intersection with the positive pixels of the positive label and excluding misdetected pixels in the background from the target detection results; Step S4, performing average weighted summation between pixels to update the label and gradually expanding the point label to improve the detection accuracy; Step S5, outputting the generated detection image.

The present invention relates to a macro plastic and micro plastic detection method based on RGB and hyperspectral image fusion, which includes the following steps: obtaining macro plastics and micro plastics; mixing with solid wastes to obtain a solid-phase substrate; pretreating the obtained solid-phase substrate to obtain a material; drying to remove part of moisture and coating on a quartz window sheet, drying until the moisture is completely removed, and flattening by using another quartz window sheet to obtain a material to be detected; obtaining an RGB image and a hyperspectral image of the material to be detected respectively by using a high-resolution color image scanner and a hyperspectral camera; fusing the obtained RGB image and hyperspectral image; and automatically classifying and identifying the macro plastics and the micro plastics by using a supervised classification model.

The present invention discloses a method and device for detecting small targets based on panchromatic and hyperspectral fusion images, belonging to the field of image processing technology. The method comprises: obtaining panchromatic and hyperspectral remote sensing images with high matching accuracy; sequentially connecting an image fusion network and a target detection network into a hierarchical supervision integrated network; performing calculations on the image fusion network and the target detection network respectively through their own loss functions, and at the same time calculating the mutual learning loss based on the output result of the target detection network, and feeding it back to the image fusion network as a loss function for supervised training of the fusion network; then inputting the panchromatic and hyperspectral remote sensing images into the fusion network, performing panchromatic and hyperspectral image fusion, and then inputting the fused image into the target detection network, extracting the spatial and spectral joint features of the fused image, and performing small target feature enhancement, and finally outputting a high-precision target detection result; the method can improve the accuracy of small target detection in remote sensing images.

The present invention discloses a hyperspectral image fusion method, system, device, and medium based on a dual-branch spectral unmixing network, relating to the field of hyperspectral imaging technology. The method comprises: obtaining an original hyperspectral image; inputting the original hyperspectral image into a dual-branch spectral unmixing network to obtain a target hyperspectral image; the spatial resolution of the target hyperspectral image is higher than the spatial resolution of the original hyperspectral image; the dual-branch spectral unmixing network comprises a fusion branch and a residual branch, and a total branch connected to both the fusion branch and the residual branch; wherein the fusion branch is used to combine the endmember matrix and resolution abundance of the original hyperspectral image to obtain a linear high-resolution hyperspectral image; the residual branch is used to obtain a residual image; and the total branch is used to reconstruct the linear high-resolution hyperspectral image and the residual image to obtain the target hyperspectral image. The present invention can effectively improve the spatial resolution of hyperspectral images.

The application discloses a cultivated land planting purpose change monitoring processing method based on hyperspectral remote sensing, wherein the method comprises detecting the membership of a certain crop in a certain piece of permanent basic farmland in sequence according to a crop planting detection index, and if the membership of a certain crop is greater than or equal to the planting condition membership, the current planted crop is output and reported, otherwise, the membership of different farmland uncultivated states is detected. The application can meet the conventional monitoring processing of the cultivated land planting purpose change of the permanent basic farmland, thereby relieving the long image processing time and slow response speed of the current technology after the fusion of the panchromatic image and the hyperspectral image, and effectively reducing the calculation cost.