82 results about "Remote sensing image fusion" patented technology

The invention relates to the field of remote sensing image processing and geological disaster monitoring, in particular to a goaf three-dimensional subsidence basin reconstruction system based on multi-source remote sensing image fusion, which comprises a heterogeneous data preprocessing module, a differential geometry-based data fusion module, a three-dimensional subsidence basin reconstruction module, a subsidence dynamic monitoring module and a virtual reality interaction module, the system innovatively introduces a differential geometry theory to solve the problem of heterogeneous fusion of a high-resolution optical satellite image, an SAR satellite image and airborne remote sensing Lidar point cloud data, maps multi-source data to a unified feature space through manifold learning, calculates an adaptive fusion weight through curvature analysis, constructs a multi-scale feature correlation matrix based on a geodesic line distance, and achieves the fusion of the high-resolution optical satellite image, the SAR satellite image and the airborne remote sensing Lidar point cloud data. A high-precision fusion image is generated, three-dimensional reconstruction is carried out in combination with subsidence basin geological parameters and a physical constraint method, it is ensured that a reconstruction result conforms to an actual subsidence physical rule, dynamic monitoring and virtual reality interaction of the subsidence process are achieved, and parameter adjustment and model optimization are supported.

The invention provides a multi-scale morphological gradient and NSST-PCNN image fusion method, and relates to the technical field of multi-source remote sensing image fusion. The method comprises the following steps: performing Gaussian curvature filtering decomposition and LEE filtering decomposition on an SAR image to obtain SAR decomposition features; decomposing the optical image by using non-subsampled shearlet transform (NSST) to obtain an NSST coefficient of the optical image; performing feature extraction on the SAR image after LEE filtering through various structural elements to obtain a multi-scale morphological gradient feature map; and inputting the SAR decomposition features and the NSST coefficient of the optical image into a pulse coupled neural network (PCNN) to obtain a preliminary fusion image, and reconstructing the preliminary fusion image through NSST inverse transformation to obtain a final fusion image. The fused image retains the spectral characteristics of the original optical image, enhances the spatial resolution and edge details, and is suitable for scenes needing to retain high-precision edges, such as road slope deformation monitoring.

The invention provides a random forest shallow sea sediment classification method based on multi-temporal remote sensing image fusion, and relates to the technical field of sediment information extraction. Comprising the following steps: 1, collecting and preprocessing multi-temporal image data to obtain remote sensing reflectivity; 2, the water depth of each single-time-phase image is inverted, and the optimal water depth is obtained; 3, calculating bottom reflectivity characteristics of blue and green wave bands based on the optimal remote sensing image; 4, respectively calculating topographic features and spectral features based on the optimal water depth and the optimal remote sensing image; and 5, in combination with the bottom reflectivity features, the topographic features and the spectral features, carrying out random forest feature optimization and classification model training, and generating a substrate classification result. On the basis, the method solves the problems that an existing remote sensing image substrate classification method is insufficient in feature consideration, noise in a single-time-phase image can cause low classification precision, and therefore negative effects can be generated on accurate acquisition of substrate information.

The invention discloses a diffusion model remote sensing panchromatic sharpening method based on hybrid convolution and controllable noise cooperation, and belongs to the field of image processing. According to the method, the generative diffusion probability model is introduced into the field of multi-source remote sensing image fusion, the forward noise adding process of the diffusion model is improved, and the image residual error is moved through the set displacement sequence and the noise scheduling strategy, so that the conversion process is effectively improved. And gradually recovering a fused image from a noise image under the condition constraint of a panchromatic image and a low-resolution multispectral image through a constructed hierarchical optimized model of a hybrid convolution architecture. A shallow layer of the encoder adopts depth separable convolution to efficiently extract space details; the deep layer adopts dynamic shape number convolution to improve the multi-scale ground feature characterization capability; the decoder adopts standard convolution to ensure reconstruction stability and spectrum consistency, and cross-level features are fused through jump connection. According to the method, the spatial details of diffusion model remote sensing panchromatic sharpening processing are remarkably enhanced, and the spectrum fidelity is effectively improved.

The invention discloses a self-distillation map attention-enhanced multi-source remote sensing image fusion classification method. The method comprises the steps of obtaining hyperspectral data and laser radar data of a to-be-classified earth surface region; inputting the hyperspectral data and the laser radar data into a pre-trained semi-supervised self-distillation diagram attention enhancement network for feature extraction and fusion to obtain fused features; and classifying and outputting the fused features through a classifier to obtain fusion prediction probability distribution, and taking a category corresponding to a maximum probability value in the fusion prediction probability distribution as a final classification result. According to the method, complementary information of different modes can be effectively utilized, the distinguishing capability of the classifier is remarkably enhanced under the condition that dependence on prior knowledge is reduced, and ground feature types can be better recognized.

The invention provides a ship detection-oriented multi-source remote sensing image fusion method, a ship detection-oriented multi-source remote sensing image fusion medium and a ship detection-oriented multi-source remote sensing image fusion system, and belongs to the technical field of remote sensing images. Constructing an electromagnetic scattering constraint model based on physical optical approximation and a geometric diffraction theory to calculate a predicted value of the backscattering cross section of the target, introducing a fusion cost function, generating a fusion weight matrix by adopting an adaptive feature weighting model, and performing multi-scale fusion on the image through hypercomplex wavelet transform; and iteratively optimizing the fusion result by using an information diffusion heat conduction equation, and performing physical consistency correction to output an enhanced fusion image, thereby solving the technical problem of poor physical consistency of the fusion result caused by lack of physical model constraint when the synthetic aperture radar image and the multispectral image are fused.

The invention relates to the technical field of image semantic segmentation, in particular to an intelligent landslide identification method based on multi-source remote sensing image fusion. Comprising the following steps: calculating a gradient matrix and a water flow convergence index based on a digital elevation model, searching and determining a physical prior interval of cohesive force and an internal friction angle based on geological lithology data, and constructing an input tensor; extracting a surface semantic feature map of the optical image by using visual perception branches through a double-branch coupling network; based on a physical prior interval, mapping branches by using physical parameters, and carrying out nonlinear mapping to obtain a limited rock-soil mechanical parameter diagram; and calling a micro infinite slope stability layer, executing forward derivable calculation based on a limit equilibrium equation, generating a slope stability coefficient field, constructing physical attention gating, executing Hadamard product operation, and generating a semantic feature tensor. Through deep coupling integration and collaborative optimization of the visual perception branch and the physical parameter mapping branch, the true effectiveness of the obtained position data is ensured.

The invention belongs to the technical field of target tracking, and provides a water surface target tracking method based on 4D millimeter wave radar and unmanned aerial vehicle remote sensing image fusion, electronic equipment and a storage medium. The method comprises the steps of data acquisition, preprocessing, dynamic foreground extraction, foreground optimization, radiation feature extraction, target identification, background point cloud elimination, motion data extraction, position prediction and instruction generation. Through alignment processing and filling processing, a basis is provided for foreground extraction, and the foreground analysis effect is improved; through dynamic foreground extraction, noise filtering and contour optimization, most background data irrelevant to a tracking target are reduced, and the input data quality of a recognition model is improved; through the improved Faster-RCNN network, the structure of the recognition model is optimized, and the recognition speed is increased; through background point cloud elimination and tracking object motion data extraction, the point cloud volume is reduced, and the input data dimension is increased.

The present disclosure provides a multi-temporal remote sensing image fusion method, device, equipment and storage medium. The method of the present disclosure comprises: reading and preprocessing multi-temporal remote sensing image data; performing linear normalization for each band of the multi-temporal remote sensing image data and saving global normalization parameters; initializing an adaptive Kalman filter for each pixel point in the multi-temporal remote sensing image data, the initialization of each adaptive Kalman filter comprising setting a forgetting factor of the adaptive Kalman filter; performing processing using the adaptive Kalman filter to determine the pixel value of each pixel point in the fused image data to obtain fused image data, the processing of the adaptive Kalman filter comprising a prediction operation and an update operation, the update operation comprising adjusting an observation noise covariance matrix using the forgetting factor; de-normalizing the fused image data according to the global normalization parameters; saving the fused image data and setting its geographic information. The present disclosure can improve the multi-temporal remote sensing image fusion quality.

The invention belongs to the technical field of multi-source remote sensing image fusion, and relates to a structure-texture collaborative interference radar altimeter image and optical image fusion method and system. The method comprises the following steps: reading an optical image and a wide-swath interference radar altimeter image, and carrying out preprocessing and space-time registration; respectively decomposing the registered optical image and radar altimeter image into a structural layer and a texture layer by adopting a multi-scale local total variation decomposition method; fusing the structure layer by adopting a fusion rule based on weighted local energy and local gradient consistency to generate a fused structure layer; fusing the texture layer by adopting a fusion rule of combining partition filtering and maximum absolute value to generate a fused texture layer; adding the fusion structure layer and the fusion texture layer, and reconstructing to obtain a gray fusion image; and performing intensity-hue-saturation inverse transformation with the chromaticity and the saturation of the original optical image by taking the gray level fusion result as intensity to generate a final natural color fusion image.

The invention discloses a satellite remote sensing image fusion method based on Wrapping-Curvelet transformation, and the method comprises the steps: carrying out the spatial registration of a multispectral image and a panchromatic image, carrying out the resampling of the multispectral image according to bilinear interpolation, obtaining a multispectral image with the same pixel size as the panchromatic image, and carrying out the fusion of the multispectral image and the panchromatic image. The method comprises the following steps of: respectively performing histogram matching on a panchromatic image and three wave bands of a multispectral image to be fused to obtain a new panchromatic image, taking the three wave bands of the multispectral image and the corresponding panchromatic image as examples, respectively performing Wrapping-Curvelet transformation on the three wave bands of the multispectral image and the corresponding panchromatic image to obtain respective Curvelet coefficients, fusing the coefficients of each layer, and then performing inverse Wrapping-Curvelet transformation on the fused layers to obtain a new panchromatic image; and obtaining a fused image. The blocking effect caused by blocking is avoided, the number of parameters of the implementation method is small, redundancy is reduced, and the algorithm is simple, convenient and rapid.

The application provides a remote sensing image fusion method and system based on a double-coupled deep neural network, which comprises the following steps: obtaining a hyperspectral remote sensing image and a multispectral remote sensing image; inputting the hyperspectral remote sensing image and the multispectral remote sensing image into a double-coupled deep neural network model for image fusion; the double-coupled deep neural network model comprises a spatial information extraction module, a spectral information extraction module and a fusion module; the spatial information extraction module is used for extracting spatial information from the multispectral remote sensing image; the spectral information extraction module is used for extracting spectral information from the hyperspectral remote sensing image; and the fusion module is used for performing image fusion on the extracted spatial information and spectral information. The spatial information and the spectral information can be accurately fused, and the application can be used in remote sensing fields such as target identification, ground object classification and environment monitoring.

The present application belongs to the technical field of computer data processing, and provides a biodiversity inversion method based on multi-source remote sensing image fusion, comprising: remote sensing image data acquisition, image preprocessing, image fusion processing, multispectral resolution image data generation, spectral feature extraction, final frequency feature extraction, feature integration and target ecological variable prediction; the present application realizes differentiated fusion of high and low frequency components under different scales through wave base adaptive selection and multi-scale wavelet decomposition, taking into account spectral fidelity and spatial structure preservation, improves the complementarity of multi-source data and the quality of fused images; through spectral resolution refinement processing and multi-bandwidth scale simulation, the limitation of single resolution is broken through, and the ability to capture complex spectral features of vegetation is enhanced; through spatial neighborhood feature fusion, spatial correlation is enhanced; through the ecological variable inversion estimation model, multi-index synchronous prediction is realized, and the generality of the model is improved.

The application discloses a kind of multi-branch multiscale laplace progressive remote sensing image fusion method and system, input multi-branch multiscale laplace progressive remote sensing image fusion network to be fused and processed remote sensing image, carry out image fusion;The multi-branch multiscale laplace progressive remote sensing image fusion network includes the panchromatic branch f pb for extracting panchromatic image information, mb The multispectral branch f For extracting multispectral image and the fusion branch for fusing and reconstructing both information;The method proposed in the application can effectively alleviate the distortion and unreal phenomenon of fusion image, compared with other mainstream methods, the method proposed in the application is in the small order of magnitude range while obtaining good fusion effect, the parameter and running speed of model, has certain practical significance;Meanwhile, the method proposed in the application only uses basic convolution layer and ReLU activation function layer, with strong scalability and potential.

The invention discloses a multi-scale spatial-spectral interaction remote sensing image fusion method based on Mamb-Transform, and solves the problem that the existing fusion method is difficult to give consideration to spatial detail recovery and spectral consistency maintenance in a complex scene. According to the method, a double-branch U-shaped network is adopted to extract hyperspectral and multispectral image features respectively, and a scale adaptive fusion strategy is constructed: a modal guided Mamba module is introduced in a high-resolution feature stage, and dynamic parameters generated by one modal are utilized to guide state space evolution of the other modal to recover space details; a cross-modal interaction Transform module is designed in a low-resolution feature stage, and global semantic alignment and feature depth interaction are realized through a bidirectional attention mechanism; and finally, the output is calibrated by the spectrum attention module. According to the method, the spatial detail recovery capability and the spectrum fidelity of the fused image can be remarkably improved, high-quality spatial spectrum information fusion is realized while the calculation efficiency is ensured, and the requirement of high-precision remote sensing observation is met.

The present disclosure provides a kind of remote sensing image fusion method and system based on semi-supervised deep neural network, including obtaining high spatial resolution panchromatic image and low spatial resolution multispectral image to be fused, and it is preprocessed;The panchromatic image and multispectral image are input into double branch network, respectively extract the spatial information of panchromatic image and the spectral information feature of multispectral image;The feature map of the extracted spatial information and spectral information is stacked first after resolution perception, then the stacked feature map is fused and reconstructed, and the resolution perception result is injected, to obtain fusion image.

The invention provides a POI (Point of Interest) and remote sensing image fused urban functional area identification method, which comprises the following steps of: firstly, acquiring POI original data and a remote sensing image, and converting the POI data into a three-dimensional semantic point cloud; thirdly, constructing a multi-modal fusion model containing a double-branch feature extraction sub-network, an attention fusion sub-network and a functional area classification sub-network; after the image and the three-dimensional semantic point cloud are input into the model, the double-branch feature extraction sub-network extracts global image features and POI local features respectively; the attention fusion sub-network fuses the two types of features to generate a fusion feature matrix; and finally, outputting a functional area identification result by the functional area classification sub-network. According to the POI and remote sensing image fused urban functional area identification method provided by the invention, the POI data is converted into the three-dimensional semantic point cloud, and the multi-modal fusion model including double-branch feature extraction, prompt attention fusion and classification sub-networks is constructed, so that the accuracy and practicability of urban functional area identification are effectively improved.

This invention discloses a remote sensing image fusion method combining ratio transformation and distribution transformation, comprising: performing mean filtering on a panchromatic image and an upsampled multispectral image to obtain high-frequency components of the panchromatic image and the upsampled multispectral image; based on this, obtaining the missing high-frequency details in the multispectral image, denoted as the first high-frequency details; performing standard normalization on the first high-frequency details to obtain the second high-frequency details; calculating the mean and standard deviation of each pixel in each channel of the upsampled multispectral image; concatenating the upsampled multispectral image and the first high-frequency details and inputting them into a convolutional network to generate two affine transformation parameters; injecting the obtained mean and standard deviation into the second high-frequency details to generate high-frequency details with the same distribution as the upsampled multispectral image; and combining this with the upsampled multispectral image to obtain the final fused image. This method solves the problems of spectral distortion and detail distortion in existing remote sensing image fusion algorithms.

The invention relates to the technical field of remote sensing image fusion, in particular to an image feature optimization fusion method, a panchromatic sharpening method and a product. According to the method, the low-frequency structural features of the PAN low-level features are enhanced through the FMB path in the HCB, the high-frequency detail features of the PAN low-level features are enhanced through the OLFAB path in the HCB, and the enhanced PAN features are generated from the enhanced low-frequency structural features and the enhanced high-frequency detail features at the tail end of the paths through addition. Similarly, the enhanced LRMS features are generated through the HCB. Through N times of HCB optimization, enhanced PAN features and LRMS features meeting preset requirements are obtained, high-frequency details are reconstructed and high-frequency response is enhanced while the continuity of a low-frequency space structure is kept, and spectrum fidelity and space detail recovery are considered; therefore, the technical problem that a traditional feature optimization fusion method is difficult to give consideration to spectrum fidelity and space detail recovery is solved.

The invention relates to the technical field of remote sensing image processing, in particular to a hyperspectral and multispectral remote sensing image fusion method, electronic equipment and a storage medium, and the hyperspectral and multispectral remote sensing image fusion method comprises the following steps: grouping a hyperspectral image and a multispectral image to be fused according to a spectral range; wherein the spectral range of the hyperspectral image in each group is matched with the spectral range of the multispectral image; for each group, fusing the hyperspectral image and the multispectral image in the group to obtain a fused image; and overlapping the fusion images corresponding to the groups to obtain a final fusion result. According to the scheme, the hyperspectral image and the multispectral image are grouped according to the spectral range, so that the spectral range of the hyperspectral band and the spectral range of the multispectral band in each group are matched, and the problem of spectral distortion caused by inconsistent spectral ranges is avoided.

The application discloses a kind of based on double-dimension RWKV mechanism's remote sensing image fusion method and system, the present application method includes the high-resolution panchromatic remote sensing image and low-resolution multispectral remote sensing image to be fused input depth fusion network RWPNet, to obtain high-resolution multispectral remote sensing image, the depth fusion network RWPNet includes up-sampling operation, shallow feature extraction stage, deep feature extraction stage, double-branch fusion stage and reconstruction stage, deep feature extraction stage utilizes double-dimension RWKV module to extract global and local features to panchromatic remote sensing image and multispectral remote sensing image, double-dimension RWKV module includes spatial mixing unit, local detail perception unit and channel mixing unit.The present application aims to solve the deficiency of existing method in spectral consistency maintenance, spatial detail recovery, panchromatic-multispectral long-range dependence relationship construction and computational efficiency, improve the spectral fidelity of fused image, structure sharpness.

The invention discloses a panchromatic image and multispectral remote sensing image fusion method, which comprises the following steps: S1, acquiring remote sensing image data of a target area and preprocessing the remote sensing image data to obtain preprocessed remote sensing image data; the remote sensing image data comprises a panchromatic image and a low-resolution multispectral image; s2, inputting the preprocessed remote sensing image data into the trained kernel estimation network to obtain an estimation fuzzy kernel of the low-resolution multispectral image; and S3, sequentially inputting the preprocessed remote sensing image data and the estimated fuzzy kernel into the trained fusion network to obtain a fusion image.

The invention provides an image cloud removal method and device based on optical SAR remote sensing image fusion, and the method comprises the steps: constructing an image cloud removal network, and sequentially setting an image fusion shallow feature extraction module, a two-dimensional deep feature extraction module and a cloudless image reconstruction module; the image fusion shallow feature extraction module performs preliminary feature interaction and fusion on the cloud optical image and the SAR image; the two-dimensional deep feature extraction module is used for extracting depth features by using a stacked double sparse Transform residual group, and the residual group comprises a sparse space Transform module and a sparse channel Transform module which are alternately connected; the cloudless image reconstruction module reconstructs a cloudless optical image through a channel attention mechanism and a deconvolution operation; and adopting a multi-perception loss function to supervise and train the image cloud removal network, and extracting a cloud-free optical image reconstruction result based on the trained image cloud removal network. According to the method, the cloud removal visualization effect and the quantitative index result of the cloud-containing optical remote sensing image are improved, and the spectral characteristics and the structure detail characteristics of the reconstructed image are ensured.

The invention discloses a surface water net extraction method and system based on multi-source remote sensing image fusion, a storage medium and electronic equipment. The method comprises the following steps: data preprocessing: processing an SAR image and an optical image; extracting features by using a double-feature extraction branch to obtain backscattering features and spectral features of different scales; utilizing a channel space feature fusion module to fuse back scattering features and spectral features of the encoder part layer by layer; through a depth separable spatial pyramid pooling module, a receptive field is expanded, and richer context information is captured; fusing back scattering features and spectral features of the decoder part layer by layer by using an adaptive feature fusion module; and carrying out up-sampling and image size recovery to obtain an extraction result, and carrying out precision evaluation. By means of the method, accurate extraction of the surface water net under the complex terrain can be achieved, the labor amount of manual extraction work can be effectively reduced, reference is provided for water resource management, ecological protection and disaster early warning and coping, and a new view angle is provided for developing a more advanced water net extraction method in the future.

The present invention relates to a remote sensing image fusion method based on spatial-spectral tensor subspace representation, which belongs to the field of remote sensing image fusion. The method represents the input LR-HSI, HR-MSI and HR-HSI as three-dimensional tensors. A spatial observation model and a spectral observation model based on super-Laplacian prior are constructed to effectively characterize the relationship between the input image and the target HR-HSI. The spatial-spectral tensor subspace representation model is used to decompose the HR-HSI into a low-dimensional coefficient tensor, a spatial subspace tensor and a spectral subspace tensor. The spatial subspace tensor and the spectral subspace tensor are estimated from the HR-MSI and LR-HSI respectively by the tensor singular value decomposition algorithm T-SVD, and the coefficient tensor is optimized and solved using sparse regularization constraints and proximal alternating minimization algorithm. The estimated subspace tensor and coefficient tensor are input into the tensor subspace representation model to reconstruct the HR-HSI.

The application discloses a dual-mode remote sensing image fusion classification method based on local-global feature bidirectional mutual checking, and relates to the technical field of remote sensing image processing. First, the hyperspectral (HSI) and LiDAR data are aligned by the input preprocessing unit. Then, the multiscale spectral feature extraction module captures fine-grained spectral features through parallel deep separable 3D convolution. The deformable guided spatial feature extraction module generates a sampling offset field based on the inter-modal mutual guidance mechanism to realize adaptive extraction of irregular object spatial features. The global attention feature extraction module converts LiDAR elevation information into geometric position encoding to construct a long-range dependence model with height perception. The local-global feature mutual checking module realizes bidirectional calibration of CNN and Transformer extracted features. Finally, a three-level multi-stage fusion strategy is used to eliminate modal heterogeneity and output the classification result. The application effectively solves the problems of high-dimensional spectral redundancy, object geometric deformation and modal representation mismatch, and significantly improves the object classification accuracy in complex scenes.

The invention relates to a water body extraction method and system based on remote sensing image fusion and multistage confirmation, and belongs to the technical field of water conservancy remote sensing and image fusion processing. Acquiring and preprocessing a satellite remote sensing image and an unmanned aerial vehicle image of the target area; generating a fusion image through a fusion method based on dynamic physical field modeling; constructing and training a water body extraction model based on a Unet network, inputting the fused image into the trained Unet network model, and outputting a preliminary water body extraction result; and performing multi-stage confirmation of threshold verification, spatial relationship verification and artificial interaction confirmation on the preliminary water body extraction result to obtain a final water body extraction result. According to the method, the accuracy and reliability of the extraction result are effectively improved, and the misjudgment and missed judgment rate is reduced.

The invention discloses a multi-source remote sensing image fusion surveying and mapping data processing system, relates to the technical field of image processing, and aims to solve the problem that high-precision surveying and mapping requirements are difficult to independently meet in the prior art. The system comprises an image acquisition module, a preprocessing module, a feature extraction module, a fusion module and a data output and visualization module. The image acquisition module is used for synchronously acquiring underwater multi-source remote sensing image data, and the multi-source remote sensing image data comprises an optical remote sensing image, a synthetic aperture radar remote sensing image and a sonar remote sensing image; the preprocessing module is used for preprocessing the collected multi-source remote sensing image data, and the preprocessing sequentially comprises radiation correction, geometric correction, de-noising processing and image registration. The method has the advantage of improving surveying and mapping accuracy.

The application belongs to the technical field of image fusion, and particularly relates to a remote sensing image fusion method and system based on local variance image mutual information. The method comprises the following steps: firstly, using a multivariate linear regression component replacement fusion method to perform fusion to obtain an initial fusion image; then, on the basis of the initial fusion image, similarity between an intensity component of a high-resolution multispectral image and an intensity component of the initial fusion image is calculated with the aim of obtaining an optimal Gaussian filter; finally, the original high-resolution panchromatic image is filtered by using the estimated optimal Gaussian filter to obtain a low-resolution panchromatic image, and image fusion is performed on the basis of the low-resolution panchromatic image. The application makes the fused image more conducive to the preservation of spectral characteristics and reduces spatial distortion in the spatial scale.