131 results about "Remote sensing image fusion" patented technology

The invention belongs to the technical field of computer data processing, and provides a biodiversity inversion method based on multi-source remote sensing image fusion. Comprising the steps of 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. According to the invention, through wave basis adaptive selection and multi-scale wavelet decomposition, spectrum fidelity and space structure maintenance are considered, differential fusion of high and low frequency components under different scales is realized, and multi-source data complementarity and fusion image quality are improved; through spectral resolution refinement processing and multi-bandwidth scale simulation, the limitation of single resolution is broken through, and the capability of capturing complex spectral features of vegetation is enhanced; the spatial correlation is enhanced through spatial neighborhood feature fusion; and through an ecological variable inversion estimation model, multi-index synchronous prediction is realized, and the universality of the model is improved.

The invention discloses a three-source remote sensing image fusion classification method based on a hybrid Mama network, and the method comprises the steps: extracting dominant features from different modes through a convolutional neural network and a plurality of Mama models, and synchronously modeling the spectrum sequence information of a hyperspectrum from the forward direction and the reverse direction through a bidirectional spectrum Mama module, the global spectrum dependency relationship and the local detail features in the hyperspectral image are captured; meanwhile, a central sensing Mama module is adopted to extract global spatial features of the multispectral image, and the core position of a central pixel in a remote sensing classification task is highlighted; finally, deep interaction and adaptive fusion of three-source data features are realized through a three-modal fusion Mamba module, the discrimination capability of joint features is significantly improved, reliable feature representation is provided for high-precision land cover classification, and the final classification effect is effectively improved.

The invention relates to the technical field of vegetation remote sensing recognition, in particular to a multispectral and visible light remote sensing image fusion segmentation method and system and a medium. The method comprises the following steps: constructing a double-branch model comprising a visible light branch encoder, a multispectral branch encoder, a multi-scale feature fusion module and a shared decoder; inputting the visible light data into a visible light branch encoder to obtain a spatial feature map; inputting the multispectral data into a multispectral branch encoder to obtain a spectral feature map; the multi-scale feature fusion module applies a channel-space joint attention weight to an input feature map, then performs channel splicing according to scales, compresses the feature map to an original channel number by adopting 1 * 1 convolution, and then applies a channel attention weight to obtain a multi-scale fusion feature sequence; and the shared decoder analyzes the multi-scale fusion feature sequence and outputs an invasive plant segmentation mask image of the target detection area, so that high-robustness invasive plant identification under a complex background is realized.

The invention belongs to the technical field of image processing, and relates to a KAN-based space-frequency double-domain implicit guide sampling remote sensing image fusion method, which comprises the steps of constructing a training sample, creating a KAN-based space-frequency double-domain implicit guide sampling network, training the network and inputting an image to be fused into the trained network. And a fused image with high spatial resolution and high spectral resolution after fusion is generated through mapping. The network comprises a coding unit, a space implicit guiding unit, a frequency implicit guiding unit and a decoding unit, the coding unit is composed of parallel KANs and maps hyperspectral and multispectral remote sensing images to a depth potential space, and the space implicit guiding unit and the frequency implicit guiding unit conduct guiding implicit sampling on HSI in the space domain and the frequency domain respectively. And the decoding unit fuses the space-frequency domain sampling features and generates a final fused image. According to the invention, a pure KAN-driven architecture is adopted, the dependence on a multi-layer sensor in the prior art is replaced, and the lightweight degree is relatively high.

The invention belongs to the technical field of digital image processing and computer vision, and particularly relates to a remote sensing image fusion method based on local-global spatial-spectral correlation prior. Comprising the following steps: performing feature mapping on a panchromatic image and a multispectral image by using an image mapping layer; a two-dimensional convolutional neural network and a spatial Transform network are used to carry out local and global spatial feature extraction on the panchromatic image; respectively carrying out local and global spectral feature extraction on the multispectral image by using a three-dimensional convolutional neural network and a channel Transform network; performing multi-level spatial-spectral feature fusion on the panchromatic image and the multispectral image by using adaptive weight; reconstructing the spatial-spectral fusion feature into a double-high remote sensing image by using an image reconstruction layer; and performing parameter optimization on the model under a supervised condition by using double loss constraints of content loss and style loss. According to the method, the sensitivity of the model to a dynamic scene is effectively reduced, and more accurate remote sensing image fusion is realized.

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 discloses a multi-modal remote sensing image fusion classification method under a modal missing condition, and the method comprises the steps: constructing a multi-modal remote sensing image model image fusion classification model, taking the data of three remote sensing image modals as input, carrying out the feature extraction of the data of each modal through a respective specific feature encoder and a shared feature encoder, and carrying out the feature extraction of the data of each modal; the method comprises the following steps: firstly, generating a specific feature, then fusing the specific feature and the shared feature, generating a single-mode fusion feature by adopting a jump link, and finally, fusing the fusion features of the three modes again, and generating a final fusion feature for model prediction. Two auxiliary tasks are introduced into the model, one is a domain classification task based on specific features, and the other is a comparative learning task for shared features. The model is not only suitable for full-modal training, but also effectively extracts and fuses the multi-modal features under the condition that partial modals are missing, and the classification performance of the model is improved.

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 multi-modal remote sensing image fusion method based on a multi-scale Mama framework, and belongs to the field of image processing methods, and the method comprises the steps: S1, obtaining Ar and Br; s2, performing gradient calculation on Br to obtain Dr; s3, performing normalization to obtain # imgabs0 # S4: Br and Dr, and adding a single channel dimension to obtain # imgabs1 # and # imgabs2 #; S5, segmenting # imgabs3 # and # imgabs4 # into A1, B1 and D1 by using s1 * s1; the # imgabs5 # and the # imgabs6 # are divided into A2, B2 and D2 by using s2 * s2; the # imgabs7 # and the # imgabs8 # are divided into A3, B3 and D3 by using s3 * s3; s6, A ''k, B'' k and D ''k are obtained; s7, carrying out up-sampling processing; s8, splicing the images; s9, performing dimension reduction to obtain an image; s10, obtaining a sequence # imgabs9; S11, obtaining an elevation information feature sequence and an edge contour information feature sequence; s12, acquiring local-global features of the spectral-spatial features, the elevation information feature sequence and the edge contour information feature sequence; and S13, outputting a final fusion feature image. According to the method, image fusion can be carried out based on a Mama framework.

The invention provides a remote sensing image fusion method and system for improving image color naturalness, and the method comprises the steps: carrying out the geometric registration of a multispectral image and a panchromatic image, and resampling the multispectral image to the same size as the panchromatic image; averaging B, G, R and NIR wave bands of the multispectral image to obtain a simulated low-resolution panchromatic image; enhancing the green wave band of the multispectral image by adopting a vegetation enhancement method, and replacing the original green wave band; taking the simulated low-resolution panchromatic image as a first component of GS transformation, and carrying out GS positive transformation on each wave band of the multispectral image in sequence; performing band matching on the panchromatic image to obtain an adjusted panchromatic image; and replacing the GS transformation first component with the adjusted panchromatic image, and carrying out GS inverse transformation to obtain a high-resolution multispectral image. According to the method, the vegetation is enhanced by using the near-infrared band, the color naturalness of the image is effectively improved while the spatial resolution of the image is improved, and the visual effect of the image is 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 discloses a Mask2Former landslide identification method and a Mask2Former landslide identification device based on topographic features, and provides a deep learning landslide identification method which is established based on an improved Mask2Former and considers the topographic features, so that the uncertainty of landslide identification can be calculated; and the influence of objects such as rivers and river channels in a research area on the landslide identification accuracy is solved. The method comprises the following steps: collecting and converting related topographic feature data; fusing the topographic features with the remote sensing image; performing cutting, rotation and data enhancement operation on the processed remote sensing image; labeling a landslide sample and converting a label into json; related data are input into an improved Mask2Former model for training; selecting a training model with the highest API index, and applying the training model to landslide identification; and automatically extracting the identification mask to obtain an identification result, and automatically calculating the uncertainty of an identification area and an identification evaluation index.

According to the multi-source vector grid big data city remote sensing image fusion registration method, firstly, on a high-resolution remote sensing image, an object feature analysis method is adopted to extract ground features, and proper parameters are set for specific images and interested target ground features to optimize the extraction effect; and 2, extraction of a ground object target: not only is inherent characteristics of the object considered, but also comprehensive analysis is carried out by combining context and topology of the object and global characteristics of an image, and for different ground objects, a method of combining DSM data is adopted, and corresponding appropriate characteristic combination extraction is selected. And 3, searching homonymous elements between the extracted result and the existing vector data, matching the vector elements by adopting an approximation degree measurement model integrating position, shape and area characteristics, and performing image registration by utilizing the homonymous elements obtained by matching, thereby realizing accurate and efficient fusion registration of the multi-source vector grating big data urban remote sensing image.

The invention discloses a method for identifying an epimedium koreanum planting area based on multi-source remote sensing image fusion in the technical field of under-forest planting, and the method comprises the steps: obtaining multi-source remote sensing data of a target area, carrying out the preprocessing, and carrying out the recognition of an epimedium koreanum planting area based on the preprocessed multi-source remote sensing data; extracting multi-source feature data related to under-forest planting of the Korean epimedium, fusing the extracted multi-source features, inputting the fused multi-source features into a deep neural network classification model of a cross-modal attention mechanism, processing the fused multi-source features, and obtaining a deep neural network classification model of the cross-modal attention mechanism; according to the method, multi-source remote sensing data advantages of optics, radars, laser radars and the like are integrated, the limitation of a single data source in under-forest environment monitoring is overcome, and the method has the advantages of being high in accuracy, high in accuracy and high in reliability. And aiming at special growth requirements of the epimedium koreanum, key parameters such as canopy density and gradient are quantified, and accurate evaluation of ecological suitability is realized.

The invention discloses a deep learning model remote sensing image fusion method based on dense residual errors, and belongs to the field of image fusion, and the method comprises the steps: obtaining a Gaofen-6 remote sensing image training data set, carrying out the preprocessing of the Gaofen-6 remote sensing image training data set, obtaining a Transform-based basic module, and obtaining a dense residual error-based image fusion module. Performing model training by using the preprocessed training data set; the method comprises the following steps: acquiring a Gaofen-6 remote sensing image test data set, constructing an image recovery module, constructing a loss function optimization generation process, and finally testing and evaluating a generated high-resolution multispectral image based on a trained deep learning model; compared with results of different fusion methods, the deep learning model remote sensing image fusion method based on the dense residual has excellent comprehensive performance on a high-resolution No.6 test data set, and the generated fusion image presents stronger richness in the aspects of spatial details and texture features; and a remarkable advantage is obtained on the retention of the spectral information.

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 invention provides an SAR and optical remote sensing image fusion cloud removal method based on edge feature enhancement, and belongs to the technical field of cross-modal remote sensing image processing, and the method comprises the following steps: S1, inputting a cloud coverage optical remote sensing image into an embedded layer, and obtaining optical shallow layer features; s2, inputting the SAR image into an edge feature extraction module to obtain SAR edge features; s3, enabling the optical shallow layer features to pass through a fusion encoder to obtain fusion encoder features of different scales; s4, obtaining a strong characterization capability feature through a bottleneck layer by the fusion encoder feature of the last layer; s5, the strong characterization capability features and output features of the fusion encoder feature extraction module pass through a fusion decoder to obtain fusion decoder features of different scales; s6, the fused decoder features of the last layer pass through the mapping layer to obtain mapping features, the features and the input cloud coverage optical remote sensing image are added to obtain a fused cloud-removed image, the pixel restoration precision of the cloud-removed area can be effectively improved, and the high-quality optical remote sensing image is obtained.

The present invention is applicable to the field of image fusion technology, and in particular relates to a multi-source remote sensing image fusion method and system based on deep learning, the method comprising: acquiring all remote sensing images from different sources; preprocessing all remote sensing images from different sources, and statistically analyzing the position distribution of each pixel; dividing feature regions according to the position distribution of the pixels, and identifying regional features; matching remote sensing images from different sources according to the regional features to complete image fusion. An embodiment of the present invention provides a multi-source remote sensing image fusion method based on deep learning, which identifies multiple groups of remote sensing images from different sources, determines the features contained therein, and then determines the relative position relationship between the features and the shape features of the features themselves, thereby quickly completing the matching between features in remote sensing images from different sources, ensuring the accuracy of feature recognition and improving the efficiency of feature matching.

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.