411 results about "Change detection" patented technology

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.

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 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.

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 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 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.

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 embodiment of the invention provides a three-dimensional space change detection method and device, equipment and a storage medium, and relates to the field of digital image processing. The method comprises the following steps: acquiring image sequences of the same area shot by the unmanned aerial vehicle in two periods; for each image sequence, performing point cloud reconstruction on the region based on the image sequence to obtain the group of point cloud data; point cloud registration is carried out according to the similarity between the feature descriptors of all the points in the two sets of point cloud data, a matching point pair set comprising a plurality of matching point pairs is obtained, and two points in each matching point pair belong to two pieces of point cloud data and are closest to each other; and for each matching point pair, geometric information of two points in the matching point pair in respective point cloud data is acquired, and if it is determined that the difference between the two pieces of geometric information meets a preset difference condition, it is determined that the corresponding position of the matching point pair in the region is changed. According to the embodiment of the invention, the problem of insufficient spatial change detection precision in a complex scene is solved.

The invention relates to a bridge structure linear change detection method based on a three-dimensional laser point cloud. The method comprises the following steps: 1) cutting a component section point cloud; 2) generating a section template library; 3) matching a section template; 4) fitting accurate key points; and 5) detecting the variable quantity of the characteristic line. According to the parameterized section template library generation method based on the large language model, dependence of design prior information is reduced, the posture size of a component is assisted in positioning in advance, meanwhile, template geometric information is considered, accurate feature points are positioned and screened out, point cloud quality is dynamically adapted, and the requirement that in the non-ideal environment of point cloud scanning in a project, the precision of the component is greatly improved can be met. The method solves the problem of local position missing of the point cloud, provides a general solution thought for automatic detection of the bridge line shape, and can be applied to bridge line shape detection in other bridge scenes.

The invention discloses an image change detection method based on parallel processing of detection and description. The method comprises the following steps: step 1, acquiring a data set; 2, an image change detection model comprises a feature extraction module, a time sequence encoder, and parallel change detection branches and change description branches; 3, training an image change detection model based on the data set; 4, obtaining a to-be-detected dual-temporal remote sensing image; and 5, inputting a to-be-detected dual-temporal remote sensing image into the trained image change detection model to obtain a change detection result and a change description result. According to the method, the parallel change detection branch and the change description branch are designed, the pixel position and the language description of the change area are output at the same time, and the hard association is established between the change area and the language description in the spatial dimension, so that the feature richness required for analyzing the complex scene is reserved, and the balance between the detection precision and the description quality is ensured through labor division; the method is suitable for complex ground feature change scenes.

The invention relates to the technical field of remote sensing intelligent interpretation, in particular to a multi-modal remote sensing change detection method and system based on multi-granularity alignment and frequency adaptive fusion, and the method comprises the steps: obtaining optical remote sensing images and SAR remote sensing images of the same geographic region and different time phases, and constructing a multi-modal image data pair; respectively extracting multi-scale features of the optical remote sensing image and the SAR remote sensing image by adopting non-weight sharing double encoders; a multi-modal feature alignment module is used for carrying out feature distribution alignment on multi-scale optical features and SAR features on different spatial scales through KL divergence constraint, in the alignment process, a change area is eliminated through a binary change mask, and alignment constraint is only carried out on an unchanged area; constructing a unified cross-modal embedding space by comparing and learning related semantic alignment modules; and designing a modal coupling attention mechanism, and performing frequency adaptive fusion on the multi-modal features in a frequency domain. According to the method, accurate cross-modal alignment and complementary feature fusion are realized, and the accuracy and reliability of change detection are remarkably improved.

The invention relates to the technical field of crossing of remote sensing image processing and computer vision, and discloses a remote sensing image change detection method which comprises the following steps: obtaining a first time phase bottom layer pixel feature and a second time phase bottom layer pixel feature according to a first time phase remote sensing image and a second time phase remote sensing image; according to the first time phase bottom layer pixel features and the second time phase bottom layer pixel features, enhanced visual features are obtained; according to the first pixel-text similarity score plot and the second pixel-text similarity score plot, semantic guidance features are obtained; according to the enhanced visual features and semantic guidance features, obtaining fusion features of multiple levels; pixel-level binary classification is carried out on the fusion features of the multiple levels, and a change detection binary image is output. According to the invention, the precision and accuracy of remote sensing image change detection are improved.

The invention discloses a self-supervised learning-driven efficient three-dimensional model change detection method and system, relates to the technical field of self-supervised learning and three-dimensional modeling crossing, and aims to regress three-dimensional scene coordinates from a two-dimensional image in a self-supervised manner and realize high-precision camera pose estimation. And constructing a 3D Gaussian model of the scene according to the obtained three-dimensional point cloud and the camera attitude, and rapidly realizing three-dimensional model change detection by comparing a newly collected picture with a 3D Gaussian model rendering result under the same camera attitude. According to the self-supervised learning-driven efficient three-dimensional model change detection method and system, the training efficiency and stability are improved, the three-dimensional structure understanding ability of the model is enhanced, the training cost is reduced, the deployment flexibility is improved, and high-precision three-dimensional scene change detection is realized.

Aspects of the present disclosure relate generally to systems and methods for detecting change in images using a quantum information processing (QIP) system. The method includes implementing a quantum circuit in the QIP system, the quantum circuit comprising at least an ancilla qubit denoted as |a> and qubits denoted as image qubit conditions on a |0> state and a |1> state of the ancilla qubit |a>. The method also includes loading a reference image and a test image onto image qubits controlled on the |0> state and the |1> state of the ancilla qubit |a> in the quantum circuit. The method further includes determining a state of the image qubits after measuring |1> on the ancilla qubit for a predetermined number of times, wherein the reference image is detected to be different from the test image when the state of the ancilla qubit measures |1>.

The invention discloses a hyperspectral remote sensing image change detection method of a multi-branch coder-decoder. The method comprises the following steps: step 1, constructing a multi-branch feature extraction module; 2, constructing a cross-branch attention fusion module, wherein the cross-branch attention fusion module is connected with the output of the multi-branch feature extraction module; 3, inputting the image into a twin branch, a differential branch and a splicing branch; step 4, inputting the feature maps extracted by the three branches into a cross-branch attention fusion module; 5, sending the fused feature map and feature maps output by the differential branches and the splicing branches into a decoder module to obtain a change prediction map; step 6, obtaining a trained model; and 7, inputting a test image into the trained model to obtain a change detection image. According to the method, from three angles of common feature extraction, spectral difference enhancement and global information integration, multi-dimensional modeling of the dual-time-phase hyperspectral remote sensing image is realized, and the change detection precision is improved.

The invention discloses an object level change detection method based on semantic flow feature alignment and global attention fusion, and belongs to the field of change detection. The method comprises the following steps: inputting a double-time-phase image and carrying out preprocessing; constructing a twin feature encoder to extract multi-scale features; a self-global cross attention module is introduced in a coding stage to realize global semantic fusion and coarse registration of double-temporal image features; a semantic flow alignment module is further designed, and pixel-level alignment of a feature level is realized by generating a semantic flow field, so that view angle difference and structure offset are compensated; in the decoding stage, a feature optimization and jump connection fusion mechanism is adopted, shallow spatial information and deep semantic features are integrated, and boundary expression of a change region is enhanced; and finally, outputting an object level change detection result through a detection head. Experiments show that the method has object-level change detection capability in scenes with visual angle differences, weak textures and the like.

The invention discloses a high-resolution remote sensing image change detection method, and belongs to the field of remote sensing image change detection. In order to alleviate the problem of false change caused by style difference and shadow interference in change detection of a high-resolution remote sensing image, for a dual-temporal remote sensing image, an encoder sharing weight is adopted to process the dual-temporal remote sensing image to obtain a multi-scale dual-temporal feature map; the efficient state space model feature interaction unit performs internal and cross-temporal interaction on the multi-scale dual-temporal feature map, and captures temporal difference features on a plurality of feature levels; the multi-scale feature refinement unit aggregates the multi-level difference features through up-sampling and fusion, obtains enhanced aggregation features through a VSSBlock, redistributes the enhanced aggregation features, and fuses the enhanced aggregation features with the multi-scale time-phase difference features to obtain refined time-phase difference features. And the image is sent to a decoder based on an enhanced visual state space module to carry out high-resolution reconstruction step by step so as to obtain fusion features, and a final prediction image, namely a detection result, is obtained.

The invention relates to the technical field of remote sensing change detection, and discloses a remote sensing change detection method and system based on boundary perception semantic context. The method comprises the following steps: extracting global spatio-temporal features of a double-temporal image pair under different scales through a boundary perception semantic context network model to obtain time-phase differential features of each scale, extracting texture and contour information from the time-phase differential features of each scale to obtain detail features, and integrating the time-phase differential features of each scale to obtain semantic features; and introducing the boundary information into a learning process, fusing the detail features and the semantic features to obtain enhanced features, and performing prediction according to the boundary information and the enhanced features to obtain a remote sensing change detection result. According to the invention, information of different scales and boundary information in the remote sensing image can be effectively utilized, and the accuracy of remote sensing change detection is improved.

The invention relates to a cross-domain change detection method and device for a hyperspectral image, electronic equipment and a medium, and the method comprises the steps: aligning the spectral features of a source domain and a target domain in a sample hyperspectral data set, obtaining the features of the source domain and the target domain, extracting visual features and pseudo tags from the features of the source domain and the target domain, and obtaining the cross-domain change of the hyperspectral image; constructing a global-local loss function of the visual features by using a contrast loss function, obtaining corresponding text features by using a pseudo tag, fusing the visual features and the text features to obtain general change detection features, and constructing a plurality of loss functions of the visual features and the text to train a deep learning network to obtain a change detection model; and cross-domain change detection of the hyperspectral image is realized. Therefore, the technical problems that in the related technology, an image algebraic method is prone to being influenced by radiation differences, a transformation method is insensitive to nonlinear changes, a classification method depends on label accuracy, deep semantic information of remote sensing images is difficult to fully mine, and therefore the accuracy and robustness of change detection are limited are solved.

The invention discloses an unmanned aerial vehicle remote sensing image change detection method and system, and relates to the technical field of remote sensing image processing. The method comprises the following steps: acquiring dual-time-phase remote sensing images collected by an unmanned aerial vehicle in the same designated area at different time periods; inputting the remote sensing image into a pre-trained remote sensing image change detection model, and outputting a change detection result graph; wherein the remote sensing image change detection model is configured to extract RGB features and depth features of two time-phase remote sensing images respectively; performing cross-modal fusion on the RGB features and the depth features to obtain enhanced single-temporal features corresponding to each temporal phase; and performing cross-time interaction on the enhanced single-time-phase features of the two time phases to identify and output a change region. According to the method, the utilization of depth information in an unmanned aerial vehicle remote sensing image feature extraction process and depth interaction and recognition between dual-tense features can be enhanced, the influence of false change noise is reduced, and the prediction performance of the model is improved.

The invention belongs to the technical field of cross-seasonal remote sensing image change detection, and particularly relates to a cross-seasonal remote sensing image domain adaptive change detection method based on an improved CycleGAN. According to the method, through segmentation of all models and boundary constraint multi-scale super-pixel segmentation, the source domain image and the target domain image are kept consistent in object-level structure, the problems of ground feature breakage, texture dislocation and sample alignment irregularity caused by seasonal differences are effectively reduced, pre-training ViT-B high-dimensional semantic features and a density clustering algorithm are introduced, and the accuracy and the robustness of the method are improved. Noise samples such as mixed ground features, shadows and illumination anomalies in a complex remote sensing scene can be automatically recognized, a source domain training set is made to be purer, the stability of CycleGAN style migration training is improved, a generator fuses a multi-scale residual block and a self-attention module, a migrated image is made to be close to a target domain in the aspects of color, texture and seasonal features, and the image migration efficiency is improved. And meanwhile, the boundary and the structure of the ground object are kept not to be damaged through semantic consistency constraint, so that the problem of false change in cross-seasonal change detection is fundamentally solved.

The invention relates to an unmanned aerial vehicle image target level change detection method based on multi-level fusion in the technical field of image processing. Registering two unmanned aerial vehicle images of the same scene and different time phases, and performing pixel alignment on an overlapping region of the two images according to registration points; processing the two images of which the pixels are aligned, and outputting a pixel-level change detection result; training a universal target detection model, and performing target detection on the two images with the pixels aligned by using the model; and fusing the target detection result with the pixel-level change detection result to obtain a target-level change detection result. According to the invention, an unmanned aerial vehicle image target level change detection task is decomposed into an unmanned aerial vehicle image pixel level change detection sub-task, a target detection sub-task and a multi-level fusion sub-task, so that the task difficulty is reduced, and the problems of insufficient training samples and poor generalization performance in the prior art are fundamentally solved; and the target level change detection speed is high, the accuracy is high, the application range is wide, and the robustness is high.

A system for managing data availability for database transactions is disclosed. The system is programmed to receive source data and extract a plurality of events from the source data. The system is programmed to create a halt record corresponding to each event of the plurality of events. The system is programmed to resolve conflicts among one or more halt records. The system is programmed to receive a request for a specific database transaction related to a specific database item from a client device. The system is programmed to determine whether any halt record is in effect for the specific database item. The system is programmed to perform the specific database transaction in response to determining no halt record is in effect. The system is programmed to transmit a result of the specific database transaction in response to the request to the client device.

The invention discloses a cross-sensor remote sensing image unsupervised change detection method based on a structured graph auto-encoder, belongs to the field of computer vision and remote sensing image processing, and particularly relates to a cross-sensor remote sensing image unsupervised change detection method. The objective of the invention is to solve the problem of low cross-sensor remote sensing image change detection accuracy caused by difficulty in explicit modeling of semantic change and insufficient robustness to modal difference in an existing model. The method comprises the steps of 1, obtaining a source domain and a target domain; 2, obtaining a source domain graph structure and a target domain graph structure; 3, constructing a learnable structural difference tensor; 4, constructing a structured graph auto-encoder 1; 5, constructing a structured graph auto-encoder 2; 6, obtaining a trained structured graph auto-encoder 1 and a corresponding weight, offset and structural difference tensor; 7, obtaining a trained structured graph auto-encoder 2 and a corresponding weight, offset and structural difference tensor; and 8, obtaining a final difference chart.

An apparatus includes a memory for storing image data; and processing circuitry in communication with the memory. The processing circuitry is configured to obtain a current set of one or more camera images from a current time, calculate lane marking confidence values for two or more lane marking types at various positions in a scene captured by the images, and determine the lane marking type for each position by comparing these confidence values with previously stored confidence values associated with the same positions. The apparatus then outputs the lane marking type for each position in the scene.

The invention discloses an MBSE model version pedigree iteration method and system based on a graph theory and a knowledge graph. The method comprises the following steps: constructing an MBSE model of an aerospace craft and mapping the MBSE model into an attribute graph; a directed acyclic graph is adopted to record a version iteration process, management is carried out through a version number system comprising a main version number, a combined version number and a revised version number, and a differential storage strategy is adopted; and performing change detection and merging based on a baseline, accurately identifying changes through a model comparison algorithm including internal attributes, internal relationships, overall relationships and difference analysis, and processing conflicts in collaborative design. According to the method, atomic-scale version control is realized, the problems of missing change traceability, low verification efficiency and multi-baseline conflict are solved, and the efficiency and quality of aerospace craft full-life-cycle model management are improved.