618 results about "Change detection" patented technology

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 relates to the technical field of data processing, and discloses a knowledge graph incremental updating and consistency checking method and system. The method and the device are used for solving the problem of low incremental updating and consistency checking efficiency of a large-scale knowledge graph. The method comprises the following steps: collecting a to-be-updated data source, sorting and separately storing data, and marking priority labels; performing change detection on the marked data source, identifying change items by comparing entities and relationships, and generating a log; incremental updating is executed based on the log, and nodes and edges are processed in a hierarchical fusion mode; preliminary consistency verification is carried out, and attribute uniqueness and relation directivity are checked; expanding a verification range, traversing an association path through cascade check, and recording problems; and optimizing storage according to the record, merging the update area, updating the index and cleaning the log. The method solves the problem of low efficiency of incremental updating and consistency verification of the large-scale knowledge graph, improves the response speed and the data accuracy of the system, and is suitable for a high-frequency dynamic data environment.

A system and method for root cause analysis in incident processing, including root cause change detection, is presented. The method includes processing a plurality of event records, including a plurality of alert records and a plurality of change records, each event record generated based on an event in a computing environment; parsing each alert record based on a predetermined data field; extracting from each predetermined data field a data value; correlating a group of alert records of the plurality of alert records based on at least an extracted data value; generating an incident data record based on the extracted data values of the correlated group of alert records; detecting a change record of the plurality of change records related to the incident data record; determining that the change record is a root cause change of the incident data record; and initiating a mitigation action based on the root cause change.

The invention discloses a remote sensing image pseudo label generation method based on prototype perception learning, and a weak supervision change detection model training and detection method, and belongs to the field of remote sensing image change detection. The invention aims to solve the problem that a category activation graph is incomplete in existing remote sensing image weak supervision change detection and the problem that an obtained detection model is unstable in change recognition. According to the method, feature maps of different scales before and after change are extracted from remote sensing images before and after change through a pseudo label generation model, change features are obtained, a class activation mapping map and a class activation mapping map of an unchanged class are obtained, and the class activation mapping map is segmented into response area masks through a threshold value and used for performing weighted aggregation on fusion features to obtain a class prototype; generating an enhanced class activation mapping graph based on the fusion feature and the class prototype, and selecting the class with the highest similarity as a pseudo tag of the pixel; and training based on the pseudo label data of the pixels to obtain a change detection model, wherein the model is used for remote sensing image change detection.

The invention provides a wheat stripe rust remote sensing change information-driven early disease prediction method and system, and the system comprises a multi-source data progressive fusion module which enables a natural image and a multispectral image to be spliced step by step into corresponding hierarchical features, and carries out the fusion and outputting of deep fusion features; the frequency domain decoupling-based change detection module is used for outputting a change probability graph of adjacent moments; constructing a plurality of groups of training samples and inputting the training samples into a conditional diffusion prediction model for training; to-be-predicted wheat remote sensing image data and corresponding meteorological data are collected, a change probability graph is obtained, the change probability graph, the corresponding meteorological data and diffusion mode prior data are fused to serve as a condition vector, the condition vector and random Gaussian noise are input into the trained condition diffusion prediction model together for denoising, and prediction denoising data are obtained; then a prediction change probability graph is obtained through a visual decoder; and on the basis, obtaining a prediction result of the severity and distribution range of the wheat stripe rust disease on the d-th day. The method can be used for predicting the early wheat stripe rust.

The invention relates to a high-resolution remote sensing image change detection method and system based on decoupling representation learning, and solves the defects of false change and non-ideal detection precision caused by inconsistent imaging conditions of different time phases compared with the prior art. The method comprises the following steps: obtaining a training sample; constructing content branches; constructing style branches; training a multi-branch parallel framework; obtaining a high-resolution change result; and separating image styles and obtaining a recovery result. According to the method, a reliable high-resolution remote sensing image change detection result can be obtained under the condition of multi-temporal complex imaging condition interference.

The invention relates to the technical field of image data processing, and discloses an AI target change detection method fusing a time sequence remote sensing image, which comprises the following steps of: calculating a difference information entropy value of a remote sensing image on a satellite-borne edge calculation unit, and comparing the difference information entropy value with a trigger threshold value; only when the information entropy value is higher than the threshold value, the change area is extracted to form an incremental information packet to be transmitted to the ground; and after analyzing the incremental information packet, the ground station generates an instruction according to the identified environmental interference characteristics and regional stability, feeds back the instruction to the satellite-borne unit, and bidirectionally adjusts a trigger threshold value. According to the invention, an information screening decision is preposed to a data generation source, so that invalid transmission of massive invariable background data is avoided; therefore, the acquisition delay of the key change information is obviously reduced; the established satellite-ground closed-loop feedback mechanism enables the whole monitoring method to continuously optimize the judgment standard during use, and the applicability and reliability of the method are improved.

The invention relates to a change detection method based on multi-source remote sensing data. The method comprises the following steps: obtaining a plurality of radar images according to a ground-based radar image; based on the radar images before and after the change, registering the radar images with the radar images at the corresponding moments; after registration, the radar image and the optical image are fused to obtain a fusion difference chart, and a change detection result is obtained through segmentation and clustering. According to the embodiment of the invention, effective data reference is provided for comprehensive monitoring and analysis of the target area, so that accurate positioning and qualitative monitoring of the change area are realized.

The invention discloses an intelligent change detection method for non-aligned two-time-phase images of an inspection unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle image processing and change detection, and the method comprises the steps: obtaining two-phase images collected by a low-altitude unmanned aerial vehicle under a fixed route and same sensor parameters; a lightweight registration model is constructed and trained, feature point matching is utilized to predict matching point pairs, a homography matrix is calculated, and accurate registration of non-aligned images is achieved; and constructing and training a change detection model based on image pair interaction feature fusion, analyzing the aligned image after registration, and outputting a change information binary image. The method can effectively solve the problem of non-alignment caused by position and angle differences during two-time-phase image acquisition of an unmanned aerial vehicle, and the technical problems of low precision, poor robustness and insufficient calculation efficiency of a traditional method in change detection, effectively improves the automation level of low-altitude safety monitoring of ground highways and railways, reduces the maintenance cost, and improves the safety of the unmanned aerial vehicle. The important application value is realized.

The invention discloses a multi-temporal remote sensing image automatic change detection method based on deep learning, and the method comprises the following steps: collecting remote sensing image data obtained in different periods in a target region, carrying out the standardization processing of the remote sensing image data, and obtaining a standardized multi-temporal remote sensing image data set; based on the standardized multi-temporal remote sensing image data set, carrying out ground feature category segmentation by adopting a semantic segmentation network to obtain a ground feature semantic priori graph; inputting the standardized multi-temporal remote sensing image data set into the improved U-shaped twin network, and outputting a change detection probability graph and a binary change mask; based on the change detection probability graph and the binary change mask, attribute discrimination is carried out on the change area in combination with the ground feature semantic prior graph, and a change area result is output; and integrating the change area result to a geographic information service platform. The improved U-shaped twin network is adopted, and automatic change detection of the multi-temporal remote sensing image is achieved.

The invention provides an optical remote sensing image island building change detection method based on a deep learning technology, and the method comprises the steps: taking a dual-temporal remote sensing image which is marked with a building change region after registration as training data, and inputting the training data to two branches of a weight sharing twin network; each branch network extracts deep features by using a CNN basic feature extraction module, realizes a channel-space synergistic effect by using a mixed attention module, and performs cross-regional global relationship modeling together with a dynamic feature enhancement module to realize feature fusion; carrying out difference calculation on the output characteristics of the two branches by utilizing a change detection decoder, and taking the marked building change area as output; forming a mixed loss function by using a mixed loss function consisting of edge perception loss and focus loss, so as to obtain an island building change detection model; and detecting a building change area by using the island building change detection model. According to the invention, the detection capability of small-scale island buildings can be improved.

The invention discloses a Mama-based edge-refined remote sensing image semantic change detection method, and belongs to the technical field of remote sensing image change detection. In order to solve the problem of rough prediction edge caused by insufficient optimization of boundary region details in the feature extraction and fusion process of the existing method, the invention provides the following technical scheme: firstly, extracting multi-level features of a dual-temporal remote sensing image by using a twin Mama encoder backbone network; secondly, cross-time-phase feature interaction and difference feature extraction are carried out through a difference module based on Mamba; then, an edge-refined visual state space decoder is adopted, and expansion and corrosion operation and an attention mechanism are fused to reinforce edge information; meanwhile, the learning ability of the model to edge details is improved by combining a loss function strategy of depth boundary supervision and change region supervision. Experiments are verified based on a SECOND data set, the method is superior to an existing mainstream method in the aspects of precision, intersection-to-union ratio, F1 score and other indexes, the boundary precision and semantic segmentation effect of change detection are remarkably improved, and the method is suitable for urban planning, disaster assessment and other high-precision demand scenes.

The invention relates to a multi-modal remote sensing image change detection method and system and a readable storage medium, and the method comprises the steps: carrying out the normalization of a multi-modal remote sensing image, carrying out the superpixel segmentation through employing simple linear iteration clustering, and employing the pixels of superpixels as nodes to construct an original image structure; inputting the original graph structure into a graph attention network to extract graph structure features of superpixels; performing style regularization on the graph structure features, and performing style randomization on the graph structure features and adjacent superpixels to obtain new style features of the superpixels; inputting the new style features into a graph structure decoder to obtain a super-pixel reconstructed graph structure; training a graph attention network by using the content consistency of the reconstructed graph structure and the original graph structure to obtain a target graph attention network; and inputting an original image structure obtained by processing a to-be-detected multi-modal remote sensing image before and after change into the target image attention network to obtain image structure features before and after change, and obtaining a change binary image by using an Otsu method. The change detection precision is effectively improved.

The invention discloses an automatic generation method and system for an intelligent adaptation layer based on API change. The method comprises the steps that API change detection is conducted according to new and old version API interface definitions, adaptation codes are generated according to an API change detection result and compiled into a compatible adaptation layer library, and the compatible adaptation layer library is packaged into an installation package; in the installation stage, the new version target library and the compatible adaptation layer library are installed in a target directory; and loading the compatible adaptation layer library prior to the new-version target library in the running stage, taking over and processing the old-version API interface through the compatible adaptation layer library, and transferring the new-version API interface in the new-version target library. The invention aims to solve the compatibility problem caused by the API change of the software library, realize the automatic generation of the intelligent adaptation layer to ensure the seamless compatibility of the application program with different version libraries under the RPM and other software package management systems, and reduce the maintenance workload for solving the incompatibility problem caused by the API change.

Systems and methods are provided for change detection in low-resolution video streams, which can be used for applications such as high resolution video restoration and processing. The techniques effectively detect changes by leveraging a large receptive field and lightweight computation, which are achieved by working with low-resolution images. In particular, the techniques include extracting features from a change detection model and a semantic segmentation model, and integrating the extracted feature outputs from the models to produce a robust change detection map. A pre-processing phase can be employed to optimize the input for each model, ensuring minimal complexity and enhanced performance. The change detection model can be implemented as a deep neural network, and methods are provided for generating ground truth (GT) data, which semantically guides the change detection neural network to perform change detection inpainting during training.

The invention belongs to the technical field of track safety monitoring, and more particularly relates to a multi-mode-based track foreign matter intrusion event capturing system and a multi-mode-based track foreign matter intrusion event capturing method. The method comprises the following steps: regularly acquiring an image sequence of a track line; performing change detection on each pair of adjacent images in the image sequence, and identifying a change area; carrying out multi-modal feature extraction on a change region in the image; performing change type judgment by using the image features and the text features extracted in a multi-modal manner; then generating a text answer and a corresponding visual mask, and further verifying a change type; and the event capturing module determines whether a foreign matter invasion event exists according to the change type, and if yes, an alarm is triggered and event information is recorded. The problem that change types cannot be accurately distinguished when foreign matter invasion is captured in the prior art is solved.

The invention belongs to the technical field of computer security, and relates to a dynamic detection architecture, strategy, system and method for attack variants, and the multi-layer risk detection architecture is composed of a behavior change detection layer, a code structure detection layer and a data flow detection layer. The dynamic adjustment detection strategy is formed by respectively introducing a behavior change detection layer, a code structure detection layer and a data flow detection layer in a multi-layer risk detection architecture into a self-learning model; the dynamic detection system comprises a system interface module, a risk preliminary screening module and a variation detection module; the dynamic detection method comprises the following steps: primarily screening data input through the system interface module by using the risk primary screening module; inputting the primarily screened data into a variation detection module for detection and analysis, and configuring the access authority of the input data according to an output result; according to the method, the comprehensiveness of variation feature detection is improved, and the detection performance of the model is improved so as to adapt to continuously changing attack features.

The invention relates to a target level change detection method, device and equipment based on a visual large model, and a medium, and the method comprises the steps: carrying out the precise extraction of a large-range remote sensing image through employing an RD-YOLO rotation target detection network, and obtaining a dual-time-phase region of interest ROI comprising a large-scale target scene; carrying out the positioning detection of the dual-temporal region of interest ROI through employing an OD-YOLO target detection model, and generating a target region image comprising a candidate target bounding box; segmenting the candidate target bounding box by adopting a GE-SAM model of a visual large model SAM to generate a segmentation mask; and carrying out matching and intersection-to-union ratio calculation on the segmented masks, judging through a preset threshold value, and finally outputting a change detection result of the small-size target. According to the method, target-level change detection can be achieved without a large amount of specific scene annotation data, the cross-scene adaptability is high, the detection precision and efficiency are improved, and the application range is wider.

The invention relates to the technical field of computer vision and artificial intelligence, in particular to a multi-mode double-branch twin network remote sensing change detection method and system for image and text fusion, and the method comprises the steps: carrying out the interactive fusion of text features and a high-level semantic feature map, enabling the text features to find the most relevant visual clues, and enabling the visual clues to be more accurate; performing residual connection on the refined text features, and updating the text features; the updated text features are combined with the high-level semantic feature map to generate a high-level semantic feature map with higher discrimination; the method comprises the following steps of: extracting difference information among multi-level features according to multi-level feature maps of front and back time phases and a high-level semantic feature map with higher distinction, and then acquiring a changed image segmentation mask based on a cross-shaped Transform multi-mode decoder guided by a U-shaped visual language. By fusing the remote sensing image and the associated text, the problems of false detection and missing detection of single-mode data are effectively reduced, and the recognition accuracy of the change area is improved.

The present invention belongs to the technical field of remote sensing image processing and change detection, and particularly relates to a method for detecting surface terrain changes in planetary remote sensing images based on deep learning. The method includes: inputting the dual-temporal planetary remote sensing image data of the area to be detected into a pre-established and trained planetary remote sensing image change model to obtain the detection result of surface terrain changes; the planetary remote sensing image change model extracts multi-layer features of dual-temporal images based on the Siamese network framework, and through a multi-level differential feature fusion structure, comprehensively measures the differential features of different levels to achieve the detection of terrain changes. The planetary remote sensing image change model designed by the present invention not only has high accuracy, but also has the advantages of few parameters and low computational complexity, and has high practical application value.

The present invention discloses a polarimetric SAR change detection method, device, equipment and medium, which relate to the field of radar image processing technology. The method acquires a time-series PolSAR image, uses JBLD divergence to calculate the similarity measure of the multi-phase covariance matrix; calculates a time-series edge intensity map based on the similarity measure; uses the time-series edge intensity map to initialize the cluster center and introduces a dynamic edge constraint mechanism to suppress superpixels from crossing the image edge during the iteration process, and outputs the superpixel segmentation result; constructs an image topology representation that integrates time-series feature similarity, spatial adjacency and cross-phase cross-feature similarity; constructs an energy function containing node cost and edge cost, solves the energy minimization problem through quadratic pseudo-Boolean optimization, and obtains a change detection map. The present invention can avoid errors caused by regional discontinuity and boundary fuzziness in superpixel segmentation, and exhibits strong robustness in both natural objects and complex urban building scenes.

A system, method, and computer program product for real-time change detection are described. The system includes at least one processor configured to receive transaction data associated with a transaction associated with an entity, and determine an aggregate value based on a portion of the transaction data for the transaction. The at least one processor is further configured to generate a predicted aggregate value for the entity by inputting historical transaction data associated with a plurality of historical transactions associated with the entity into the machine learning model. The at least one processor is further configured to determine a deviation of the aggregate value from the predicted aggregate value, and compare the deviation to a dynamic threshold associated with the entity. The at least one processor is further configured to, in response to determining that the deviation satisfies the dynamic threshold, trigger a risk mitigation process associated with the entity.

The invention discloses a polarimetric SAR change detection method, device and equipment and a medium, and relates to the technical field of radar image processing. The method comprises the following steps: acquiring a time sequence PolSAR image, and calculating similarity measurement of a multi-temporal covariance matrix by adopting JBLD divergence; calculating a time sequence edge intensity graph based on similarity measurement; initializing a clustering center by using a time sequence edge intensity graph, introducing a dynamic edge constraint mechanism in an iteration process to inhibit super-pixels from crossing image edges, and outputting a super-pixel segmentation result; constructing image topological representation fusing time sequence feature similarity, spatial adjacency and cross-time phase cross feature similarity; and constructing an energy function containing node cost and edge cost, and solving an energy minimization problem through quadratic pseudo Boolean optimization to obtain a change detection graph. According to the method, errors caused by discontinuous regions and fuzzy boundaries in super-pixel segmentation can be avoided, and high robustness is shown in natural ground features and complex urban building scenes.

The invention discloses a flood storage and detention area change pattern spot identification label generation technology based on style decoupling. The technology comprises the following steps: S1, constructing and preprocessing a change detection data set; s2, constructing a conditional diffusion generation network based on a decoupling encoder; s3, decoupling extraction and orthogonalization representation of content-style features are carried out; s4, constructing a multi-target training strategy and two-stage model training; s5, injecting and fusing style features based on cross attention; s6, cross-domain style migration and diversified label generation; and S7, based on label quality screening of physical and semantic double constraints, outputting a high-quality change detection expansion data set. Compared with the prior art, the method has the advantages that by introducing a content-style decoupling mechanism, the style and the content of the generated sample are independently and accurately controlled, and the change detection label which is consistent in ground feature layout, diversified in imaging style and accurately labeled at a pixel level is generated.

The invention discloses an edge feature knowledge distillation-guided change detection method and device, computer equipment and a storage medium. Based on a feature knowledge distillation mode, hierarchical edge knowledge distillation is adopted, and an edge knowledge transmission channel from a teacher network to a student network is constructed. Through the improvement, the detection precision of the student network is obviously improved, meanwhile, compared with a fine and complex teacher network, the parameter quantity, the calculation quantity and the reasoning time of the student network are obviously reduced, and the network successfully achieves effective balance between fine detection performance and a lightweight network structure. The collaboration problem of edge learning and change detection tasks is solved. According to the method, effective balance is achieved among model refined edge perception, change detection performance and computing resource consumption, and reference and guidance are provided for practical application of an edge collaborative change detection network.

A remote sensing image change detection method and device based on feature enhancement and fusion belongs to the field of remote sensing image change detection technology, and particularly relates to real-time processing of remote sensing images on the ground, on satellites, or on drones. It solves the problems of low accuracy, poor feature representation ability, insufficient category balance ability, and inability to have high precision and efficiency while maintaining low computational complexity in existing change detection technologies. The detection method comprises: inputting registered dual-phase remote sensing image data of the same area to be detected, and extracting three levels of features of the data respectively, and then performing global and local feature enhancement to obtain three corresponding difference features; further, performing fusion processing to obtain aggregated features; and finally, obtaining remote sensing image change detection results based on the features. The present invention is suitable for deployment on the ground, on drones, or on satellites for change detection of remote sensing images.

The invention discloses a semantic change detection method, system, equipment and medium for a remote sensing image, and relates to the technical field of semantic change detection, and the method comprises the steps: obtaining a plurality of dual-time remote sensing images; constructing a double-path double-branch network structure, and guiding one branch to selectively learn and fuse the feature information of the other branch through the learning of a bidirectional guiding module; the feature information of the double branches in different time periods is kept consistent in channel number and spatial resolution, an interaction strategy is adopted, double-branch network structures with different scales are used for extracting channel attention weights respectively, dynamic feature fusion is carried out based on weight information, and double-time semantic features are obtained; and performing binary change detection on the dual-time semantic features to obtain a binary change detection graph, and obtaining a final result of semantic change detection. According to the method, a two-way guiding strategy is integrated, the network can be effectively guided to be more focused on key change characteristics in a two-time-phase image, and the method also has strong robustness and adaptability under images with different resolutions.

The invention relates to the technical field of unmanned aerial vehicle remote sensing detection, and discloses a change detection method and system for an unmanned aerial vehicle remote sensing image. The method comprises the following steps: acquiring a multi-temporal unmanned aerial vehicle remote sensing image data set containing high-resolution image data acquired at different times in a target area; carrying out geometric correction and radiation normalization processing on the data set to generate a standardized remote sensing image data set, and eliminating geometric distortion and illumination difference between images; then extracting multi-scale spatial features in the standardized remote sensing image data set, generating a spatial feature matrix containing texture, spectrum and structure information, and comprehensively capturing image details; inputting the spatial feature matrix into a time sequence feature fusion network, calculating feature differences among different time phase images, and generating a change feature vector; and finally, constructing a dynamic change detection model based on the change feature vector, and generating a target area earth surface change detection result.

The invention provides a disaster response-oriented change detection method based on dual-time-phase characteristic interaction, which comprises the following steps of: 1, acquiring building remote sensing image data before and after a disaster, and preprocessing the building remote sensing image data; 2, sending the preprocessed building remote sensing image data into a weight sharing ConvMama encoder to extract multi-scale features of images before and after a disaster; 3, the multi-scale features are sent to a feature enhancement module based on multi-branch expansion convolution, and enhanced features are obtained; step 4, the enhanced features are sent to a double-time-phase feature interaction module based on grouping Mamba, and difference features are obtained; and step 5, sending the difference characteristics into a multi-level decoder for decoding, and outputting a building damage grading graph to complete damage evaluation. According to the method disclosed by the invention, accurate damage evaluation can be well realized on the building under various complex background influences, and the problems of false detection, missing detection and the like are effectively solved.

The invention discloses a building change detection method and system based on a double-branch fusion network, and the method comprises the following steps: S1, obtaining a double-time-phase SAR image of a building in a target region, carrying out the preprocessing of the double-time-phase SAR image, marking a region where the front and rear adjacent time phases of the double-time-phase SAR image change, and obtaining a data set; s2, classifying the data set in the step S1 by combining a difference generation operator and a clustering algorithm to obtain a training sample set and a test sample set; s3, constructing a double-branch fusion network based on a local enhancement module and a self-attention module, and training the double-branch fusion network by using the training sample set in the step S2; and S4, detecting the test sample set in the step S2 by using the trained double-branch fusion network to obtain a building change detection result of the target area. According to the invention, the building edge detection precision can be improved, and the building change condition can be accurately detected.