17 results about "Remote sensing image fusion" patented technology

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 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 application provides a remote sensing image fusion method and device, electronic equipment and medium, and relates to the technical fields of image processing and deep learning. The specific implementation scheme comprises: acquiring a reference image and a to-be-processed image of the same observation area, the reference image and the to-be-processed image are homologous remote sensing images, the reference image comprises a plurality of first spectral bands with a first spatial resolution, the to-be-processed image comprises a plurality of second spectral bands with a second spatial resolution, and the second spatial resolution is lower than the first spatial resolution; performing up-sampling processing on the to-be-processed image to obtain an intermediate image with a size consistent with that of the reference image; inputting the reference image and the intermediate image into a fusion model to obtain a target image corresponding to the to-be-processed image, the target image comprises a plurality of second spectral bands with the first spatial resolution, and the fusion model is obtained by performing model training on an initial fusion model by using training data.

This invention belongs to the field of image fusion technology and discloses a multi-source remote sensing image fusion method and apparatus based on cross-modal interactive enhancement. It acquires OPT images, SAR images, and DEM images; extracts basic feature maps of the OPT, SAR, and DEM images at different scales based on dynamic feature extraction blocks, and then enhances and fuses them sequentially to obtain a unified fused feature map; wherein, the dynamic feature extraction block includes a cascaded normalization layer, an SS2D module, an adaptive channel redundancy removal sub-block, a learnable descriptive convolution sub-block, and a bidirectional attention gate; this invention can effectively adapt to the feature differences of multimodal images, achieve collaborative and accurate extraction of global and local features, and remove feature redundancy, thereby improving the overall quality of multimodal remote sensing image fusion.

The application discloses a kind of geographic information acquisition method and system, it is related to geographic information acquisition technical field, to initial space grid remote sensing image set is carried out image projection consistency processing, output image slice set, calculate comprehensive quality score, construct fusion model, construct sample set and establish ground surface change evolution model, output virtual ground surface evolution sequence, calculate change intensity atlas and identify change area, extract change feature, generate structured semantic label, the present application is through multi-source remote sensing image fusion and ground surface evolution modeling, realize the prediction and reconstruction of occlusion area and unobserved period ground surface state, comprehensive meteorological data, solar elevation angle and occlusion rate assess image quality, improve the effectiveness of fusion image, construct ground surface change evolution model and change driving classification mechanism, generate structured semantic label, automatically update geographic information system database, enhance the integrity, timeliness and applicability of geographic information.

This invention relates to hyperspectral and LiDAR remote sensing image fusion and recognition technology. Considering the natural complementarity between hyperspectral images and LiDAR data in terms of spectral features and elevation information, and the problems of existing methods introducing redundant interference through simple feature stitching, this invention provides a collaborative recognition method for hyperspectral and LiDAR images based on Mamba attention fusion. This invention constructs a dual-branch convolutional feature extractor to extract shallow spatial-spectral features of both modalities; introduces a spatial Mamba module for multi-scale global-local long-range dependency feature mining; and designs a dual-attention interactive guided fusion module to achieve deep interaction and fine fusion of cross-modal complementary information through a parallel mechanism of channel attention and spatial attention. This invention effectively suppresses redundant information interference, significantly improves the accuracy and robustness of ground feature fusion classification, and provides high-quality ground feature classification support for remote sensing applications such as urban planning.

This invention relates to the field of remote sensing image processing and multi-source image fusion, and discloses a high-fidelity remote sensing image fusion method based on PAN-RWKV. The method takes panchromatic (PAN) and multispectral (MS) images as inputs, and includes: upsampling the MS images and aligning them with the PAN; performing convolutional coding and multi-scale feature extraction in the panchromatic and multispectral branches respectively; introducing a panchromatic sharpening receptive gated weighted key (PRWKV) module to model long-range spatial dependencies with linear complexity and perform dynamic channel-gated modulation; employing a Hybrid Context Space-Spectral Attention Fusion (HCSAF) module in the fusion branch to achieve cross-modal adaptive information interaction and selective fusion; and finally fusing global consistency and local details through a Global-Local Attention Weighted Reconstruction (GLAWR) module to output a high-resolution multispectral (HRMS) image. Compared to global attention structures that require explicit construction of attention matrices, this invention significantly reduces computational and storage overhead while maintaining high spatial detail and spectral consistency, making it suitable for efficient panchromatic sharpening processing of high-resolution remote sensing images.

This invention discloses a method for spatiotemporal dynamic prediction of power outage areas during typhoon disasters based on satellite remote sensing image fusion. The method includes: acquiring meteorological, geographical, and electrical equipment data of the target area and dividing the target area into several grid units; extracting the extent of water bodies in the disaster area based on satellite remote sensing images and calculating the newly added water area in each grid unit; using the coordinate information of transmission towers, distribution towers, and transformers, selecting and optimizing candidate lines through a binary particle swarm optimization algorithm to generate a grid topology and extracting its topological features; fusing meteorological data, geographical data, electrical equipment data, newly added water area, and grid topology features to construct a multi-source dataset; and constructing a typhoon power outage prediction model based on a dynamic spatiotemporal graph neural network. By performing spatiotemporal correlation analysis on the grid units, the power outage status of each grid unit at future times is dynamically predicted, resulting in a predicted power outage area. This invention enables dynamic prediction of power outage areas.

This invention relates to a method for missing modal remote sensing image fusion based on a shared prototype space, belonging to the field of multimodal remote sensing image fusion technology. The method includes the following steps: acquiring a training sample set containing existing modalities and missing modalities; constructing a multi-candidate inference and adaptive fusion model based on a shared prototype space; supervising the training of the fusion model; acquiring an image to be processed containing only existing modalities; inputting the image to be processed into the trained model, and outputting the final fusion result image. Compared with existing technologies, this invention achieves cross-modal interpretable inference through a shared prototype space, and combines multi-candidate generation and credibility constraint mechanisms to reduce the uncertainty of single inference results, thereby improving the stability, interpretability, and ability to preserve target information and detail textures in complex scenes.

This invention discloses a method, system, and apparatus for multi-source remote sensing image fusion, relating to the field of remote sensing image processing. The scheme includes acquiring multiple original remote sensing images and performing standard preprocessing to obtain standard remote sensing images; processing these images according to a preset downsampling strategy to obtain degraded remote sensing images at a first spatial resolution; performing adaptive robust smoothing processing on these images using a temporal robustness processing strategy to obtain robust remote sensing images corresponding to each time point and at the first spatial resolution; and performing upsampling processing on these images according to a preset fusion strategy to obtain a fused remote sensing image at a second spatial resolution. This scheme ensures geometric stability and spectral physical consistency through standard preprocessing, and reliably achieves robust processing, adaptive smoothing, and information preservation of degraded remote sensing images through a temporal robustness processing strategy, improving the continuity and consistency of robust remote sensing images over long time scales, and ultimately determining the fused remote sensing image, which is beneficial for anomaly monitoring.

The application belongs to the technical field of image fusion, and discloses a multi-source remote sensing image fusion method and device based on cross-modal interaction enhancement, which acquires an OPT image, a SAR image and a DEM image; basic feature maps of the OPT image, the SAR image and the DEM image under different scales are extracted based on a dynamic feature extraction block, and are sequentially enhanced and fused to obtain a unified fusion feature map; wherein the dynamic feature extraction block comprises a normalization layer, an SS2D module, an adaptive channel de-redundancy sub-block, a learnable description convolution sub-block and a bidirectional attention gate connected in series; the application can effectively adapt to feature differences of multi-modal images, realize collaborative and accurate extraction of global and local features, remove feature redundancy, and improve the overall quality of multi-modal remote sensing image fusion.

The invention provides a remote sensing image fusion method and device, electronic equipment and a storage medium, and relates to the technical field of remote sensing image fusion. The method comprises the steps of obtaining a low-frequency approximate component and a plurality of high-frequency detail components according to image data, and extracting directional texture feature information corresponding to the image data based on connectivity analysis; determining a spatial offset parameter of the image data according to the directional texture feature information, performing pixel-level position calibration on the image data, performing weighted fusion on the low-frequency approximate component and each high-frequency detail component after calibration is completed to obtain the fused low-frequency approximate component and each high-frequency detail component, and performing weighted fusion on the fused low-frequency approximate component and each high-frequency detail component; and reconstructing and generating a fused target image. The method aims at solving the problems that in an existing remote sensing image fusion method, texture dislocation is caused by space deviation between images, and the fusion result lacks details and is distorted, and fusion accuracy and stability can be improved.

This invention provides a remote sensing image fusion method, apparatus, electronic device, and storage medium, relating to the field of remote sensing image fusion technology. The method includes the following steps: obtaining low-frequency approximation components and multiple high-frequency detail components from image data, and extracting directional texture feature information corresponding to the image data based on connectivity analysis; determining spatial offset parameters of the image data based on the directional texture feature information, and performing pixel-level position calibration on the image data; after calibration, weighted fusion of the low-frequency approximation components and each high-frequency detail component to obtain the fused low-frequency approximation component and each high-frequency detail component, and reconstructing to generate the fused target image. The method of this invention aims to solve the problems of texture misalignment caused by spatial offset between images and the lack of detail and distortion in the fusion results in existing remote sensing image fusion methods, thereby improving the accuracy and stability of the fusion process.

The application discloses an island remote sensing extraction method based on an attention mechanism and feature optimization, and belongs to the technical field of remote sensing image fusion.The method constructs a TransUNet encoder framework with a fusion double attention mechanism, improves the sensitivity and noise resistance of the model to specific image features, and introduces a double attention module before the Transformer encoder layer, so that the parallel position attention module and channel attention module capture the spatial dependence and channel dependence of the image respectively.The mechanism makes up for the defect that the pure Transformer architecture lacks image-specific inductive bias, thereby enhancing the differentiated expression ability of the island reef ontology and the complex marine background, and solving the problem that the existing method is difficult to accurately identify the island reef features under strong noise interference.

This invention discloses a method for constructing a self-attention mechanism for remote sensing rotating targets based on geometric compatibility perception, belonging to the fields of computer vision and deep learning technology. First, the invention performs feature analysis on the input image, extracting rotation bounding box parameters for candidate target regions to construct semantic tokens and geometric tokens. Then, it calculates the geometric consistency constraint between any two geometric tokens using a geometric relationship function and optimizes the attention weight model with a geometric compatibility perception matrix. Finally, it completes feature collaborative iteration and weighted aggregation through a Transformer update mechanism, thereby improving the ability to model the orientation consistency of rotating targets. This invention overcomes the limitations of pure semantic modeling, taking into account both long-distance semantic dependencies and local geometric consistency. It can alleviate problems such as feature confusion and localization offset in rotating target detection and remote sensing image fusion, improving the geometric fidelity and task accuracy of the model, and is applicable to various geometrically sensitive visual engineering scenarios.