726 results about "Change detection" patented technology

The invention discloses a terrain-level real-scene three-dimensional entity and element dynamic updating method, which comprises the following steps: S1, data acquisition and preprocessing: acquiring data of a target area by an acquisition mechanism based on entity and element type priorities, and preprocessing the acquired data to obtain model data of the target area; s2, change detection: comparing newly collected model data with historical model data, and identifying a change region and type; s3, entities and elements are extracted and updated, wherein the entities and the elements of the change area are processed to obtain updated models; s4, model fusion and optimization: fusing the updated entity and element model with the original model, and realizing efficient acquisition and preprocessing of target area data by using the multi-source surveying and mapping equipment and an acquisition mechanism based on entity and element type priorities; by dividing the small regions according to the region characteristics and correcting the acquisition frequency, the resource allocation is optimized, and the pertinence and efficiency of data acquisition are improved.

In some embodiments, a method and system for detecting surface changes in a geographical area over time is disclosed. The method includes encoding pre- and post-event images using frozen features of Segment Anything Model (SAM), processing encoded embeddings by a change modeler, processing change embeddings by a change prompter, decoding change embeddings and prompt embeddings using SAM to obtain change masks which include the detected changes.

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 an ultra-high resolution remote sensing image semi-supervised change detection method based on pseudo label boundary quality improvement. The change detection aims to explore a change region of an image at a later moment compared with an image at a previous moment in a dual-time image; according to the method, semi-supervised learning guidance is enhanced through a high-quality pseudo tag, and the method comprises the following steps: 1, preparing a semi-supervised learning training data set; 2, building a teacher model and a student model; 3, starting training, reasoning data of the training data set by the student model, and generating corresponding rough and false labels by utilizing the teacher model; 4, refining the rough and false labels by using a boundary optimization method, and calculating the consistency loss of the labels and the reasoning result loss of the student model; 5, calculating the total loss, and updating the parameters of the student model and the teacher model; 6, finishing training, and performing change detection on the remote sensing double-time image by the student model; according to the method, the semi-supervised change detection performance which is limited by rough false label boundaries and cannot be further improved is improved.

The invention discloses a remote sensing change detection method based on edge enhanced cross attention and multi-dimensional loss. The method comprises the following steps: inputting a dual-time-phase image into a trained twin backbone network, and extracting multi-scale backbone features of the dual-time-phase image; inputting the multi-scale backbone features into a trained non-sharpening mask cross attention fusion model to extract spatial information and semantic information of the dual-temporal image through cross attention operation, and extracting edge information of the dual-temporal image through a sharpening convolution kernel to obtain multi-level differential features with different scales; inputting the differential features into a trained multi-scale hierarchical connection decoder to obtain a multi-scale preliminary prediction result of the change area; and carrying out weighted fusion on the multi-scale preliminary prediction result to obtain a change prediction result. According to the invention, the multi-scale information of the twin backbone network can be fully utilized, and the capability of extracting the edge information of the dual-time-phase image can be improved, so that the change detection accuracy can be improved.

Synthesizing training data for training a change detection model includes receiving a patched image comprising a background image with a patch pasted into the background image. It further includes synthesizing a harmonized patched image at least in part by harmonizing the patched image using a machine learning model trained on the background image. It further includes providing as output a synthetic training sample usable to train a change detection model. The synthetic training sample includes a reference image, at least a portion of the harmonized patched image, and a corresponding mask.

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 remote sensing image change detection method of a difference enhancement network based on edge guidance, and the constructed difference enhancement change detection network mainly comprises a backbone network, an edge enhancement module, a double-branch difference enhancement module, a multi-scale difference feature fusion module and a decoder. Edge information of the changed object is refined through an edge enhancement module, so that feature information with clear boundaries can be extracted for the changed object; a dual-branch difference enhancement module effectively learns the difference between the foreground and the background. The module captures global and local information of a dual-time image through space and channel branches, emphasizes a change area, inhibits noise interference, and enhances long-range dependence of difference. According to the method, the detection performance of the small object under the high-resolution remote sensing image is improved by using a multi-scale difference feature fusion method. Experimental results show that the detection effects of the proposed network on a plurality of public data sets are superior to those of several common remote sensing image change detection methods at present.

The invention discloses a time-space pixel feature progressive fusion remote sensing change detection method based on ViT, and the method comprises the steps: employing a pre-trained Vision Transform (ViT) as a backbone network, carrying out the fine adjustment of a model parameter through introducing a low-rank matrix, and extracting the features of a dual-time-phase remote sensing image through the fine-adjusted model; global-local feature interaction of the dual-temporal remote sensing image is enhanced through a global context branch and a time sequence dependent branch, and three-stage fusion (shallow layer, middle layer and high layer) is performed on features of the front and rear temporal remote sensing images by combining a multi-level progressive fusion mechanism to obtain enhanced features of the dual-temporal remote sensing image; after the features of the dual-time-phase remote sensing image are connected according to channels, convolution fusion is carried out, and a dichotomy prediction change image is generated through up-sampling, nonlinear feature transformation and convolution. According to the method, the target boundary definition and detection precision in a complex scene can be remarkably improved, the model generalization ability is effectively enhanced, and information loss is reduced.

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.

Systems and methods provide for managing changes to federated systems. In accordance with some aspects, a change to a federated system is received. A historical query is accessed from a query store storing information regarding queries received by the federated system. A query plan evaluation is performed for the historical query based on the change to the federated system, and an action is caused to be performed for the change based on the query plan evaluation.

The invention discloses a double-branch change detection method and system based on a visual basic model, and belongs to the technical field of remote sensing image detection. According to the method, a double-branch change detection network model based on a visual basic model is provided, and the frozen visual basic model FastSAM is used as an encoder, so that the feature extraction capability of the network model is greatly improved. In order to realize migration of extracted features of a visual basic model to the field of remote sensing images, a change detection adapter module is designed in the method, and effective conversion from universal visual features to remote sensing image features is realized through fine tuning of a small number of model parameters. In addition, the method also provides a double-branch change detection module, a single-temporal semantic feature decoder and a double-temporal change feature decoder are respectively utilized, and through mining of single-temporal remote sensing image semantic information and fusion of double-temporal multi-scale features, the feature discrimination capability is further improved, so that the detection performance of the model is 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 discloses a topology change detection method and device for a distributed service and electronic equipment, and relates to the field of distributed technologies. The method comprises the steps of obtaining a current topological graph and a historical topological graph of a distributed service based on label information of the distributed service; respectively serializing the current topological graph and the historical topological graph to obtain a first sequence corresponding to the current topological graph and a second sequence corresponding to the historical topological graph; determining an editing distance and an editing operation between the current topological graph and the historical topological graph according to the first sequence and the second sequence; and determining a detection result according to the editing distance and the editing operation. The technical problems that distributed service topology change detection in the prior art mainly depends on log analysis and real-time monitoring data, a large number of computing resources and storage space are needed, and the detection cost is high are solved.

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 relates to the technical field of remote sensing image change detection, in particular to a remote sensing image unsupervised change detection method and system for joint learning of time domain difference and consistency, and the method comprises the steps: constructing a multi-temporal remote sensing image sample through data enhancement transformation; constructing a training loss function based on time domain contrast learning and space contrast learning of consistency constraints, and training the image change detection model based on the training loss function and by using the multi-temporal remote sensing image sample; and inputting the multi-temporal remote sensing image of the target area into the trained image change detection model, and obtaining change information in the multi-temporal remote sensing image of the target area by using the image change detection model. According to the method, difference and consistency among multi-temporal observation are captured in combination with comparative learning, pixel-level semantic representation is obtained in combination with a visual basic model, sparse and compact change mapping learned by a neural network is promoted by utilizing grid sparseness loss, and unsupervised change detection of a high-resolution remote sensing image can be accurately and efficiently realized.

The invention relates to the technical field of radar remote sensing image processing, in particular to a single-polarization synthetic aperture radar change detection method and device.The method comprises the steps that initial single-polarization SAR images of front and back time phases are preprocessed to obtain a final image, a multi-scale feature difference chart is calculated, pseudo tags are generated through fuzzy C-means clustering, and the final image is obtained; a UNet coding and decoding structure is used as a basic framework, a double-branch convolutional neural network structure is designed, an input layer, a convolutional layer, a pooling layer, a full connection layer and an output layer are adopted to form a twin convolutional neural network, residual connection is established between an input tensor and an output tensor after network processing, a change detection model is generated, and a change detection result is obtained. And outputting a detection result of the single-polarization synthetic aperture radar by using the change detection model. Therefore, the problems of low accuracy of a change monitoring result and the like caused by the defects that the processing capacity of speckle noise in an SAR image monitoring method is limited and a static threshold segmentation method is difficult to adapt to the global situation in the prior art are 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 the technical field of computer vision, in particular to a multi-modal large model illusion change resistance detection method, which comprises the following steps: determining remote sensing images corresponding to a plurality of different time points, calling a preset SigLip image encoder, and extracting corresponding time characteristic differences from the plurality of remote sensing images by using the SigLip image encoder; obtaining text data related to the remote sensing image, inputting the text data and the time characteristic difference into a pre-trained multi-modal large language model for identification, generating a preliminary change detection result, correcting the preliminary change detection result based on a CVA-CICL mechanism, and obtaining a corresponding corrected change detection result; and decomposing the corrected change detection result into a plurality of change detection tasks based on a chain thinking reasoning mechanism, generating a corresponding candidate solution for each change detection task, aggregating all the candidate solutions, and outputting a corresponding final change detection result. According to the invention, the adaptability and accuracy of change detection can be improved.