1528 results about "Remote sensing image processing" patented technology

The invention relates to the field of remote sensing image processing, in particular to a high-resolution remote sensing image semantic segmentation method based on multi-scale feature fusion, which comprises the following steps: acquiring a public remote sensing image data set, preprocessing the image, and constructing a training and testing set of semantic segmentation; a CTMFNet is designed, an encoder is composed of a lightweight residual module and an MS-Transform, and local space details and global context information are extracted; rID is adopted to reduce spatial information loss, LSFE is introduced to improve spatial positioning capability, and feature calibration is carried out in space and channel dimensions through DecoderAttn to realize boundary fine segmentation; inputting the training sample into the network for training to obtain a converged optimal semantic segmentation model; and inputting the test set into the model to obtain a semantic prediction map, and outputting a fine segmentation result of the remote sensing image through multi-scale fusion and boundary restoration. According to the method, the precision and robustness of ground feature extraction are effectively improved, the calculation cost is remarkably reduced while high segmentation precision is kept, and the method has good practical value and popularization prospects.

The invention relates to a multi-source remote sensing image classification method based on a key band retrieval attention mechanism, and belongs to the technical field of remote sensing image processing. The method comprises the steps of firstly performing preprocessing and data set division on multi-source remote sensing data, then constructing a key band retrieval attention network, performing training and evaluation on a model by utilizing a training set and a verification set after division, and finally performing visual analysis on a model result by utilizing a test set. According to the method, the features of the hyperspectral image and the laser radar / synthetic aperture radar image are effectively extracted and fused, redundant information interference is effectively reduced, the retention rate of hyperspectral key information is improved, the complementary expression ability among multi-source data is enhanced, and the remote sensing image classification precision and calculation efficiency are remarkably improved. The method is suitable for application scenes of multi-source remote sensing data fusion and classification, can meet efficient intelligent processing requirements of complex earth surface information, and provides an accurate and efficient remote sensing image classification solution.

The invention relates to a multi-source remote sensing image classification method based on spectrum adaptive feature fusion, and belongs to the field of remote sensing image processing and analysis. Aiming at the problems of poor feature extraction flexibility, poor feature fusion strategy, low calculation efficiency and the like of the existing multi-source remote sensing image classification method, the method comprises the following steps: firstly, preprocessing and dividing a multi-source remote sensing data set, then constructing a spectrum adaptive feature fusion neural network model, and then carrying out model training and quantitative evaluation; and finally, carrying out model visualization result analysis. The features of a hyperspectral image and a laser radar / synthetic aperture radar image are effectively extracted and fused, classification of multi-source remote sensing data is achieved, the method is suitable for a multi-source data fusion and classification scene, the remote sensing data processing requirement of complex earth surface information can be met, the advantages in the aspect of improving classification precision and processing efficiency are remarkable, and the method is suitable for popularization and application. And a more efficient and accurate solution is provided for multi-source remote sensing image classification.

The invention discloses a real-time single-stage remote sensing image correction target detection method based on YOLOV8, and relates to the technical field of remote sensing image processing. According to the method, a deformable convolution dynamic prediction local geometric distortion parameter is embedded based on a YOLOv8 backbone network, an adaptive deformation field is generated, pixel-level real-time correction is realized, shallow details and high-level semantic features are fused through a bidirectional path aggregation network, and channel attention and a space gating mechanism are combined, so that the real-time correction of the image is realized. The small target detection capability is enhanced, background noise is suppressed, angle prediction is divided into discrete classification and continuous residual error regression tasks through a decoupling type rotation detection head, angle periodic errors are eliminated in combination with a direction sensitive loss function, and the rotation frame positioning precision is improved. And constructing a dynamic multi-task collaborative loss function, introducing gradient distribution consistency constraint to jointly optimize correction and detection tasks, and realizing feature semantic alignment and model self-enhancement through end-to-end closed-loop training. And the rotating target detection precision and the complex scene robustness are obviously improved.

The invention relates to the technical field of high-resolution optical remote sensing image processing, in particular to a remote sensing image building extraction method and system based on local-global features, and the method comprises the steps: obtaining a remote sensing image containing a building, and inputting the remote sensing image into a trained building extraction network model; the model adopts a double-branch remote sensing image building network fusing local-global features, and comprises a double-branch structure encoder, a cross-feature attention fusion module and a feature refinement enhancement module. Wherein the double-branch structure encoder is further divided into a CNN branch and a Transform branch, the CNN branch is responsible for extracting local features, the Transform branch is used for extracting global features, the cross-feature attention fusion module fuses the features extracted by the CNN branch and the Transform branch, the feature refining enhancement module further enhances the feature extraction capability of the network at a decoder end, and the feature extraction capability of the network at the decoder end is enhanced. Therefore, the building in the remote sensing image can be effectively extracted.

The invention discloses a coal mining subsidence area surface crack intelligent extraction method based on an improved Transform model, and belongs to the technical field of remote sensing image processing. Firstly, an unmanned aerial vehicle carrying a high-resolution optical camera is used for collecting images, the image overlapping rate of 70%-80% is guaranteed, and a training data set is constructed through professional labeling, cutting screening and data enhancement. The encoder of the innovative model is very distinctive, and the adaptive multi-scale patch mapping layer can dynamically adjust the patch size according to the local complexity of the image and efficiently extract features; double-attention fusion is combined with optimization position coding, and long-distance dependency capture is enhanced; and the calculation amount and the overfitting are reduced by the dynamic sparse connection full-connection layer. Residual attention enhancement pyramid pooling and a space-channel attention bottleneck mechanism are adopted, key features are highlighted, and noise is suppressed; and a breakpoint detection and connection rule determination module is utilized to realize complete restoration of the ground fracture. After the data set is used for training a model, deployment is carried out, and through preprocessing, encoding and decoding and post-processing, ground fracture information can be accurately obtained, and a data foundation is built for mining area safety management and geological disaster prevention and control.

The invention discloses a remote sensing image cultivated land segmentation method and system fusing context and boundary perception, and belongs to the technical field of remote sensing image processing and agricultural information. Constructing a cultivated land segmentation initial model composed of a backbone network, a feature enhancement module, a multi-scale feature fusion de-wharf module and a mask prediction module; training set data are input into the initial model, a composite loss function value is calculated, back propagation is executed, and a cultivated land segmentation model with boundary sensing ability is obtained through multi-round iterative optimization; and inputting the remote sensing image into the trained cultivated land segmentation model, and outputting a binary segmentation image representing the cultivated land position. Visual state space modeling and large receptive field convolution are combined, deep and shallow layer information is fused through feature injection, boundary perception supervision and composite loss are introduced, cultivated land boundary discrimination is improved, remote sensing image cultivated land high-precision extraction is achieved, and the method is suitable for agricultural interpretation and monitoring.

The invention discloses a semi-supervised multi-temporal satellite image time-varying information extraction method, and belongs to the technical field of remote sensing image processing. The semantic change detection performance of the model and the detection precision of complex shape change ground objects are improved. The method comprises the following steps: constructing a semantic change detection model, carrying out full-supervised training on the semantic change detection model by using a binary change detection supervised loss function and a semantic segmentation supervised loss function by using a small amount of labeled dual-temporal remote sensing images to obtain an initial model, and obtaining a semantic change detection prediction result of each pair of images; using a pseudo label optimization strategy to optimize the semantic change detection prediction result of each pair of images; combining the obtained pseudo label data with high confidence and a small amount of labeled dual-temporal remote sensing images into a new training set, using the new training set to perform semi-supervised training on the initial model in a semantic change detection model, and using a consistency regularization combination loss function to perform supervised training to obtain a new model; and until a preset number of iterations is reached.

The invention relates to the field of remote sensing image processing, and discloses a high-quality HRHS image generation method based on a double-subnetwork structure, and the method comprises the following steps: receiving an input low-resolution hyperspectral image and a panchromatic image, which are respectively used for providing spectral information and spatial information; performing noise filtering on the low-resolution hyperspectral image through an anti-noise pre-fusion sub-network, and generating a pre-fused hyperspectral image by using the correlation between the high spatial resolution information of the panchromatic image and the spectral characteristics of the low-resolution hyperspectral image; performing high-frequency detail reconstruction and enhancement on the pre-fused image based on a conditional diffusion model through a high-frequency enhancement sub-network; and fusing the low-frequency information of the pre-fused image and the high-frequency detail information generated by the high-frequency enhancement sub-network through wavelet inverse transformation, and outputting a final high-resolution hyperspectral image. According to the method, spectrum and space information can be effectively fused in various noise environments, and a high-resolution hyperspectral image with hyperspectral fidelity and space details is generated.

The invention discloses a remote sensing image multi-scale semantic segmentation method based on a coding and decoding network, and relates to the technical field of computer vision and remote sensing image processing, and the method comprises the following steps: S1, obtaining image data, carrying out the preprocessing of an image, carrying out the normalization of the image to a specified size, and carrying out the data enhancement operation, the data enhancement operation comprises random rotation, overturning and zooming. According to the remote sensing image multi-scale semantic segmentation method based on the coding and decoding network, ResNeXt5032x4d is introduced to serve as a backbone network, grouping convolution is combined, multi-scale features are effectively extracted, the accurate recognition capacity of the model for the land cover type is improved, and through the fusion strategy of the self-adaptive feature cooperation module AFCM, the remote sensing image multi-scale semantic segmentation method based on the coding and decoding network is obtained. According to the method, local and global context information is fused, the segmentation capability of the model on a large target is enhanced through a parallel multi-scale convolution layer and cavity convolution, the accuracy and integrity of a segmentation result are ensured, and a segmented region is smoother and more complete.

The invention discloses a remote sensing image change detection method based on spatio-temporal feature interaction and feature difference enhancement, and belongs to the technical field of remote sensing image processing, and the detection method comprises the following steps: constructing a change detection data set containing a dual-temporal remote sensing image, respectively extracting multi-scale features through an encoder, and obtaining a change detection data set; inputting the data to a spatio-temporal feature interaction module to obtain interacted dual-time-phase features; fusing the interacted double-time-phase features and inputting the fused double-time-phase features into a decoder to generate four groups of same-resolution features with different semantic hierarchies; performing difference enhancement on the four groups of features through a feature differentiator, then performing channel splicing and fusion, and outputting a change detection result; and training the model by using the training set, adjusting and optimizing, and evaluating the precision. The beneficial effects of the invention are that the method can effectively capture the space-time dependency relationship between the double-time-phase images, enhances the discrimination capability of a change region, and meets the requirements of high-precision change detection in a complex scene.

The invention discloses a multi-source remote sensing image zero sample change detection method, and relates to the technical field of remote sensing image processing, and the method comprises the following steps: obtaining remote sensing images collected by two or more remote sensing sensors at different time points in the same geographic area, the image types including optical images and radar images; preprocessing each source image, unifying the spatial resolution and the registration precision, and denoising and standardizing the image; according to the method, the cross-modal shared semantic embedding space is constructed and unsupervised comparative learning is introduced, so that the semantic consistency of the multi-source remote sensing image is effectively improved, and the change recognition capability of the model under the zero sample condition is enhanced; and meanwhile, a difference fusion calculation and structure consistency constraint module is adopted, so that the boundary judgment precision of a change region and the overall structure consistency are improved, and the accuracy and stability of a detection result are remarkably improved.

The invention discloses a remote sensing image ground object segmentation method based on hierarchical feature extraction and decoding, which relates to the technical field of remote sensing image processing, and comprises the following steps: step 1, collecting and preprocessing a remote sensing image, and constructing a training sample set; 2, constructing an image segmentation network, wherein the image segmentation network comprises a hierarchical encoder, a receptive field module unit and a network decoder which are connected in sequence; 3, training and optimizing the image segmentation network by adopting the training sample set to obtain a remote sensing image ground object segmentation model; and 4, collecting a remote sensing image to be segmented, and inputting the remote sensing image to the remote sensing image ground object segmentation model to obtain a ground object segmentation result. According to the method, the advantages of the advanced visual basic model and the U-shaped network architecture are combined, so that rapid and accurate segmentation of the ground target is realized.

The invention relates to the technical field of remote sensing image processing, and discloses an agricultural condition remote sensing image change detection method based on time sequence analysis, which comprises the following steps: acquiring an agricultural condition remote sensing image sequence through a multi-temporal remote sensing sensor, and extracting multi-scale fusion features by using a multi-modal data fusion network after registration and radiation correction of a space-time alignment algorithm. The time sequence decomposition model decomposes features, extracts time dynamic change features, and detects an abnormal change area based on an abnormal detection model of a variational auto-encoder. And optimizing the change area by a multi-objective optimization algorithm, and constructing a visual model to generate a high-resolution agricultural condition change distribution diagram. According to the method, multi-sensor data are fused, various algorithms and models are integrated, the problems of data processing, feature analysis, anomaly detection, result presentation and the like in agricultural condition remote sensing image change detection are effectively solved, the detection accuracy and efficiency are improved, and a precise basis is provided for agricultural production management.

The invention belongs to the technical field of intelligent ocean and remote sensing image processing, and discloses an ocean remote sensing coastline segmentation method based on a text-guided semi-supervised pseudo tag, which comprises the following steps: collecting ocean remote sensing coastline image data, dividing into tag data and non-tag data, and constructing a text prompt; constructing a segmentation model comprising a shared image encoder, a text encoder, two decoders and a pseudo label calibration module, cooperatively extracting image features and text features by all the parts, generating a pseudo label and a prediction mask, and optimizing the pseudo label through uncertainty calibration; training the model in a supervised stage and an unsupervised stage, and adding loss function values of the two stages to a back propagation optimization model; and finally, based on the trained model, precise segmentation of the ocean remote sensing coastline image is realized. According to the method, the efficiency and the accuracy of cross-regional ocean remote sensing coastline segmentation are effectively improved by utilizing text guidance and semi-supervised pseudo labels, and the dependence on a large amount of labeled data is reduced.

The invention provides a crop planting area intelligent extraction method and system based on multispectral remote sensing, and relates to the field of remote sensing image processing, and the method comprises the steps: obtaining and correcting a multiband remote sensing image; calculating a vegetation index and constructing a crop growth characterization index to extract canopy features; obtaining a multi-period feature map, calculating a spatial distribution entropy, and establishing an evaluation function to determine a time sequence fusion weight; a spatial constraint function is established based on the spectral distance, and a segmentation criterion is constructed by combining the spatial constraint function with time sequence features for classification iteration. According to the method, the accuracy and the discrimination degree of crop planting area extraction are improved, and crop identification requirements in a complex agricultural environment are met.

The invention discloses a multi-modal fusion unmanned aerial vehicle remote sensing image target detection method and system, aims to improve the target detection precision under a low illumination condition and reduce the calculation overhead, and is particularly suitable for unmanned aerial vehicle remote sensing image processing. The method comprises the following main steps: S1, carrying out denoising, standardization and size adjustment on an input remote sensing image, ensuring image quality and consistency, and stabilizing subsequent processing steps; and S2, based on a Retinex principle, enhancing the low-illumination image through a double-branch network, and improving details and contrast of the image. And S3, carrying out feature interaction fusion on the enhanced RGB image and the infrared image by adopting a Transform model based on a self-attention mechanism, capturing complementary information of different modes, and generating fusion features for target detection. And S4, target regression and classification are carried out by adopting a multi-scale detection head based on FPN and PAN structures, and the detection precision of targets of different sizes is enhanced. Through multi-modal feature fusion, adaptive anchor frame generation and multi-scale detection, the target detection precision in low-illumination and complex environments is effectively improved, the calculation overhead is low, and the method is suitable for target detection tasks of real-time unmanned aerial vehicle remote sensing images. Experimental results show that the method is excellent in performance on VisDrone and LLVIP data sets, and the target detection precision is remarkably improved especially under the low illumination condition.

The invention relates to the technical field of remote sensing image processing, and discloses a remote sensing image adaptive identification method and system for territorial space planning, and the method comprises the steps: obtaining a multi-source remote sensing image data set of a research region, feature extraction, cloud detection, quality evaluation and adaptive preprocessing are carried out; carrying out prototype network coding, calculating a category prototype and probability, and supporting fine tuning of a set; carrying out multi-scale cavity convolution and category scale attention fusion; evaluating the adaptability score of the comprehensive fusion feature map set, and carrying out weighted fusion, classification and normalization; change detection is carried out, stable and change regions are segmented, and time sequence context features are extracted and constrained optimization is carried out; entropy is fused, a boundary is decided, uncertainty is estimated, and weighted fusion is carried out according to a change area; conditional random field optimization, confidence level grading and connected domain identification are carried out; the automation level, the adaptive capacity and the recognition reliability of remote sensing monitoring of territorial space planning are improved.

The invention relates to the technical field of artificial intelligence and remote sensing image processing, and particularly provides a hyperspectral image and laser radar data classification method based on a dynamic fusion network. The method comprises the following steps: preprocessing acquired multi-modal data, and constructing multi-scale input; a dual-scale local attention module is designed, and context information of different scales is fused in a self-adaptive weighted mode through gating soft pooling; a dynamic down-sampling feature enhancement module is designed, the down-sampling rate is dynamically adjusted according to the complexity of the feature map, and deep multi-scale interaction is carried out based on a Mama backbone; constructing a directional interactive attention module, extracting features in horizontal, vertical and diagonal directions through directional gating convolution, and capturing an anisotropic structure of a linear ground feature; through the design of a double-path classifier, fusing shallow space details and deep semantic information; and the model is trained, optimized and reasoned to obtain data classification, and the method improves the classification precision and the calculation efficiency.

The invention relates to the technical field of remote sensing image processing, in particular to a chain diffusion remote sensing hyperspectral image super-resolution system and method, in a dynamic hypergraph diffusion branch, dynamic hypergraph learning and a diffusion model are combined, an iterative optimization process is guided through a time-varying weight space, and joint modeling of a denoising process and feature evolution is realized; in the difference-frequency collaborative attention branch, a difference-frequency collaborative attention mechanism is constructed, the limitation of traditional spectrum-space separation modeling is broken through, and balance is achieved between global trend modeling and local form keeping; according to the method provided by the invention, by constructing the semantic constraint loss function and through a semantic-driven local constraint mechanism, the reconstruction quality of the complex region is remarkably improved.

The invention discloses a high-resolution remote sensing image semantic segmentation method based on multidirectional parallel selective scanning, and belongs to the technical field of high-resolution remote sensing image processing. According to the method, a multidirectional parallel selective scanning model is provided, direction perception modeling is carried out through 8-direction serialization scanning in combination with a state space model SSM, and the multidirectional long-distance dependence capture capability is enhanced while the linear calculation complexity is kept. A pyramid encoder-decoder structure is constructed, multi-level feature extraction is realized through a four-stage OSSBlock module, and local details and global semantics are dynamically fused in cooperation with SE attention jump connection of a decoder. A selective scanning mechanism is adopted to replace self-attention, and linear complexity calculation is realized through a state space parameter matrix; and designing a mixed loss function, and improving the small target segmentation precision in combination with the class balance of Dice Loss and the difficult sample mining capability of Focal Loss.

The invention provides a semantic information reasoning method guided by a remote sensing scene graph, which can be applied to the technical field of remote sensing image processing. The method comprises the following steps: segmenting a remote sensing image to generate a scene segmentation image; executing target detection to generate an image block set; performing fine-grained analysis on the image global features and the image block set to generate a scene description text; performing grammar analysis, extracting a triple of objects, object attributes and relation information among the objects, and generating a remote sensing scene graph related to the problem; encoding the problem text into an embedded vector, projecting the embedded vector to a visual feature space, processing the embedded vector through a frozen self-attention layer and a learnable gating layer, and outputting an image-text interaction feature; carrying out attention fusion and double gating balance on the question coding features, and outputting a text code guided by a scene graph; and fusing the interaction features and the text codes, and reasoning local semantic information of the target area. The invention further provides a semantic information reasoning device and equipment guided by the remote sensing scene graph and a medium.

The invention relates to the technical field of remote sensing image processing, and particularly discloses a remote sensing hyperspectral image super-resolution fusion algorithm and system based on quaternion, and the method comprises the steps: employing a panchromatic image quaternion feature extraction module, a hyperspectral image quaternion feature extraction module and a self-attention fusion module, constructing a double-U-Net structure network based on a multi-scale residual link and a jump link; and inputting a panchromatic image and a low-resolution hyperspectral image, and calculating to obtain a high-spatial-resolution hyperspectral image. According to the method, a plurality of spectral channels of a hyperspectral image and high-resolution spatial information of a panchromatic image are embedded into four components of a quaternion, and spectral and spatial characteristics are modeled uniformly by means of algebra of the quaternion; and then feature extraction, fusion and reconstruction are carried out through a deep learning network, and finally a fusion image with both high spectral fidelity and high spatial resolution is generated, so that the spatial resolution of the hyperspectral image can be significantly improved, and the spectral consistency of the hyperspectral image can be maintained at the same time.

The invention relates to the technical field of image extraction, and provides a remote sensing image processing system, which comprises a server, an unmanned aerial vehicle, a data acquisition module, a feature extraction module, an image fusion module, an evaluation module and a result output module, the image fusion module preprocesses the satellite image and the image data to generate a multispectral image and a high-resolution image respectively, and performs fusion processing on the multispectral image and the high-resolution image to form a fused image, and the feature extraction module extracts spectral features, texture features and shape features from the fused image; the evaluation module evaluates the abnormal position according to the multi-dimensional feature description data to form an evaluation result, and the result output module generates an early warning prompt according to the evaluation result.

The invention discloses an ecological environment detection method and system based on multispectral remote sensing fusion, and relates to remote sensing image processing. The method comprises the following steps: collecting multispectral remote sensing image data of a target area; performing multiband joint atmospheric correction processing on the multispectral remote sensing image according to the scattering coefficient, the atmospheric light value and the transmissivity of each band; performing foreground and background analysis on the corrected multispectral remote sensing image; fusing the vegetation area and the non-vegetation area of each wave band image by adopting different weight strategies to generate a multispectral fusion image; and extracting spectral features of the multispectral fusion image, constructing a standard vegetation spectral feature library, and identifying regions deviating from a standard vegetation spectrum through an anomaly detection algorithm according to the extracted spectral features to obtain various vegetation coverage rates. In view of low vegetation identification precision caused by direct foreground and background division of a multispectral remote sensing image under an atmospheric interference condition, vegetation division is performed after a clear image is obtained, so that the detection precision is improved.

The invention relates to the technical field of ecological environment monitoring, and discloses a regional ecological environment monitoring method and system based on remote sensing image processing, and the method comprises the following steps: collecting remote sensing image data and climate factor data of a target region; performing spatial gridding and standardization processing on the data; establishing a nonlinear regression model to analyze a relation between climate factors and vegetation indexes; extracting spatio-temporal topological characteristics of the climate factor data and identifying a change mode; long-term prediction of regional vegetation productivity is carried out through time sequence model training; the regional ecological environment is analyzed and evaluated based on the prediction result, and a scientific basis is provided for ecological environment monitoring; the system comprises a data acquisition module, a data processing module, a nonlinear regression modeling module, a spatio-temporal topology analysis module, a time sequence prediction module and an ecological evaluation module. According to the method, the problems of small data coverage, low prediction precision and insufficient capture of nonlinear influence of climate change in the existing method are solved.

The embodiment of the invention relates to the technical field of remote sensing image processing, and provides a panchromatic sharpening method and device for a satellite remote sensing image, electronic equipment and a medium, and the method comprises the steps: obtaining a to-be-processed satellite remote sensing image in a panchromatic mode and a multispectral mode; performing up-sampling processing on the multispectral modal image until the target output size of the target panchromatic sharpening image is reached, and obtaining a multispectral modal first satellite remote sensing image; performing multi-scale feature extraction on the panchromatic modal image to obtain different scale features of the panchromatic modal satellite remote sensing image; performing frequency selectivity enhancement processing on image features of the first satellite remote sensing image and the panchromatic modal image with the same scale as the first satellite remote sensing image to obtain high-frequency features and low-frequency features with different scales; and carrying out feature fusion on the high and low frequency features with the same scale, and carrying out image reconstruction to obtain a target panchromatic sharpening image. Therefore, the detail richness and the visual coordination of the reconstructed image can be improved.

The invention discloses a cross-model knowledge distillation method for a remote sensing semantic segmentation task, and relates to a cross-model knowledge distillation method. The method aims at solving the problems that an existing remote sensing image semantic segmentation model is large in parameter quantity and large in resource consumption. The method comprises the steps of selecting a student model and a teacher model which are complementary in ability, selecting a data set to train the teacher model, and using a feature map output by the trained teacher model as knowledge to be input into the student model; carrying out attention weighting on the feature map input into the student model according to channels, and calculating loss; the knowledge transmitted by the teacher model is decoupled into a target area and a non-target area, the two independent target areas are subjected to prediction and knowledge distillation, and loss is calculated; and carrying out weighted summation on the loss of the semantic segmentation of the student model and the loss of the above steps, calculating the total loss, updating the parameters of the student model through the total loss, and finally completing the training of the student model. The invention belongs to the technical field of remote sensing image processing.

The invention discloses a multi-modal remote sensing image matching method, particularly relates to the technical field of remote sensing image processing, and is used for solving the problem of multi-modal image matching. The method mainly comprises the following steps of: 1, improving a phase consistency model, and constructing a phase-moment weighted joint direction feature in combination with a maximum moment and a minimum moment to replace the feature expression of the traditional image gradient; 2, implementing a point product fusion strategy on the phase-amplitude characteristics extracted by the phase consistency model and the maximum moment, and constructing phase-moment weighted joint amplitude characteristics; and 3, on the basis of the steps 1 and 2, identifying the direction of the feature points and screening local peak values to determine the main direction. Three values adjacent to a peak value are selected, and the peak value position is interpolated through parabola fitting so as to improve the matching precision; and step 4, constructing a logarithm polar coordinate descriptor based on regularization non-uniform partition to generate a feature description vector. Through the mode, high-precision and high-efficiency matching of the multi-mode remote sensing image can be realized.

The invention discloses a remote sensing video segmentation method and segmentation system based on text guidance, belongs to the crossing field of remote sensing image processing and computer vision, and relates to a remote sensing video segmentation method and segmentation system. The invention aims to solve the problems that the existing remote sensing video segmentation technology is poor in flexibility, cannot interact with natural languages, is insufficient in generalization ability for new categories or complex targets, and cannot meet the requirement of quickly and accurately extracting semantic information in a dynamic remote sensing scene. The method comprises the following steps: 1, acquiring a video frame sequence, and acquiring a key frame based on the video frame sequence; 2, obtaining an initial segmentation mask; 3, obtaining an optimized mask; 4, calculating a minimum bounding rectangle of the optimized mask, obtaining a bounding box of the minimum bounding rectangle, and obtaining an expanded bounding box; and 5, inputting the expanded bounding box and the video frame sequence obtained in the step 1 into an improved SAM2 video segmentation model, and outputting a frame-by-frame segmentation result of the region of interest by the improved SAM2 video segmentation model.