18 results about "Change detection algorithms" patented technology

The invention discloses a remote sensing image change detection algorithm based on multi-dimensional feature collaborative modeling. The method comprises the following steps: preprocessing and dividing image blocks of a remote sensing image change detection common reference data set; constructing a remote sensing image change detection model based on multi-dimensional feature collaborative modeling, inputting the output from a feature extraction stage into a double-branch feature difference modeling module by a trunk branch, and performing difference modeling on double-time-phase features of each layer to form multi-scale difference features; then inputting into a boundary protection feature calibration module to strengthen the edge, and inputting the strengthened features into a decoder to generate a detection result; the auxiliary branch inputs the data into a style difference feature alignment module; and training the model and then testing the test set. Compared with an existing change detection method FC-Siam-Conv, the four evaluation indexes including the accuracy rate, the recall rate, the F1 score and the intersection-union ratio are improved by 3.72%, 6.93%, 5.18% and 8.38% respectively; compared with the existing change detection method, the BIT is respectively improved by 1.63%, 1.11%, 1.38% and 2.32%.

A river and lake shoreline monthly monitoring remote sensing image automatic interpretation and result management system comprises a data resource management and access module, a remote sensing image intelligent preprocessing module, an intelligent interpretation and change identification module, a man-machine collaborative interaction and correction module and a database and report generation module. The data resource management and access module is used for accessing image data, the remote sensing image intelligent preprocessing module is used for data preprocessing, the intelligent interpretation and change recognition module is used for element automatic extraction and change discovery, and the man-machine collaborative interaction and correction module is used for man-machine interaction and result correction. And the database and report generation module is used for data storage and monitoring result generation. The invention provides an improved self-adaptive gating network sub-pixel shoreline interpretation algorithm for automatic interpretation of remote sensing images, and provides an improved cross-temporal attention and uncertainty collaborative change detection algorithm for monthly dynamic analysis of river and lake shorelines. A better scheme is provided for a river and lake shoreline monthly monitoring remote sensing image automatic interpretation and result management system.

This invention discloses a low-power data transmission method and system for IoT terminals based on edge collaboration, relating to the field of IoT wireless communication technology. The method includes: an event-driven acquisition step that uses a change detection algorithm to determine data changes, triggering transmission only when the change exceeds an adaptive threshold; otherwise, entering a deep sleep mode; a transmission scheduling step that uses deep reinforcement learning to optimize transmit power, modulation and coding, and transmission time slots; an edge collaborative processing step that performs semantic analysis and redundancy fusion on multi-terminal data before batch forwarding; a group collaborative transmission step that achieves channel state sharing and energy-balanced proxy forwarding within a group; and a feedback optimization step that feeds back the transmission effect to the terminal to update parameters. This invention, through an end-edge-cloud collaborative architecture and a closed-loop optimization mechanism, reduces terminal standby power consumption to the microampere level, improves energy efficiency, and extends network lifetime.

The invention discloses a tunnel blasting lumpiness in-situ real-time detection method based on video measurement. The method is realized through the following steps: installing cameras and high-intensity light sources on a cab, a movable arm and a bucket rod of a deslagging excavator, and matching the cameras and the high-intensity light sources; system calibration is completed by using an orientation sensor, a calibration plate and tunnel contour information; a muck pile change video is automatically collected in the deslagging process; screening typical frame segments through a change detection algorithm, and marking change types and spatio-temporal information; the disturbed rocks are restored based on the mark information, and a muck pile multi-layer three-dimensional morphological model is constructed from the outside to the inside in combination with the excavation exposure sequence; and finally extracting a lumpiness grading curve, rock spatial distribution and muck pile shape information. The system correspondingly comprises a camera, a light source, an analysis processing component, a calibration component and the like. The method solves the problems that in the prior art, the internal lumpiness of the muck pile is difficult to obtain, the detection aging is poor, and the environmental adaptability is insufficient, and automatic, instant and comprehensive detection of the tunnel blasting in-situ lumpiness is achieved.

This invention discloses a method and system for optimizing drought emergency response plans based on multi-source data fusion. The method includes collecting multi-source data and historical emergency data from a preset area, and preprocessing the multi-source data and historical emergency data. The multi-source data includes meteorological data, soil data, crop data, and water conservancy data. The multi-source data is input into a lightweight multimodal fusion network to obtain fused data. A dual-index temporal abrupt change detection algorithm and a weakly supervised convolutional autoencoder are used to capture sudden drought features in the fused data to obtain sudden drought features. A machine learning-based drought emergency response plan model is constructed based on the sudden drought features. Predicted emergency data is obtained based on the drought emergency response plan model. Decision bias fit is obtained based on the historical emergency data and the predicted emergency data. Multi-objective reinforcement learning is used to dynamically optimize decisions based on the decision bias fit to obtain the optimized result.

The application discloses a foreground target barrage anti-shielding method based on artificial intelligence, and comprises the following steps: S10, sampling original video at a preset frequency to obtain video stream images; S20, processing the video stream by using a shot change detection algorithm to determine whether the current video frame has shot change; S30, processing the current image by using a face detector to obtain current image target face information; S40, processing the current image by using an instance segmenter to obtain current image target instance segmentation information; S50, processing the current image by using a semantic segmenter to obtain current image target human body semantic segmentation information, wherein the human body semantic segmentation information is a one-dimensional feature map with the same width and height as the original image, and the value on each pixel represents the probability that the pixel is a human body; S60, processing the current image by using a depth estimator to obtain current image depth estimation information; and S70, sending the above information to a post-processing module for processing.

The invention discloses a domain generalization remote sensing image change detection method based on cross-domain consistency learning, and the method comprises the following steps: carrying out the geometric enhancement of obtained synthetic dual-phase remote sensing image data, and dividing an obtained enhanced synthetic data set into a training set and a test set; constructing a domain generalization remote sensing image change detection network based on semantics; selecting a change detection algorithm as a feature extractor to obtain an output feature of an original image pair and an output feature of an enhanced image pair which are consistent with the output in spatial size, and respectively inputting the two groups of features into a prediction head of 3 * 3 convolution sharing weight to obtain two detection result images; constructing a space-time consistency comparative learning loss function and a pseudo change correction comparative learning loss function; and setting an overall loss function, iteratively training and optimizing network parameters, and inputting a detection image into the trained neural network after the loss is stable to obtain a final detection result graph. According to the method, the influence of similar category confusion caused by domain offset can be effectively reduced, so that the generalization ability of an existing change detection neural network model is effectively improved.

This invention addresses the technical problems of existing unsupervised change detection algorithms, which mainly rely on algebraic transformations and spectral unmixing of spectra, resulting in poor performance and limited applicability in hyperspectral data with mixed pixels. It provides a hyperspectral image change detection method based on an unsupervised spectral unmixing neural network. The method includes the following steps: 1. Segmenting and mixing two hyperspectral images from different time phases to obtain hyperspectral image patch data, which is used as the training dataset; 2. Constructing a spectral unmixing network to generate endmember matrices and abundance matrices with image features; 3. Reconstructing the hyperspectral image patch data based on the endmember matrices and abundance matrices, and selecting reconstruction errors to train the spectral unmixing network; 4. Generating a change grayscale image based on the trained spectral unmixing network and the abundance matrix output by the abundance matrix generation module; 5. Binarizing the change grayscale image using a threshold segmentation algorithm to obtain the final hyperspectral image change detection result.

The application discloses a kind of based on deep learning's optical remote sensing image building change detection method, belong to remote sensing image change detection field.Firstly, optical remote sensing image data is obtained from high two and is preprocessed and change information label Label is marked;Then the image is input into the change detection model of the application, and the detection result map is output after calculation.The application effectively optimizes and improves the problems that the existing change detection method has lower detection accuracy, the detection edge is not complete, and the change detection algorithm has poor universality on different data sets.

The invention discloses a passive target positioning method combining Beidou navigation and remote sensing image change detection, which has the advantages that a remote sensing image change detection algorithm based on a global hypergraph is designed, and the algorithm takes an image block as a vertex and takes a global similar image block set as a hyperedge; constructing a global hypergraph model with image detail and global context representation capability; then, global hypergraph models are constructed on the remote sensing images at different moments respectively, measurement of change information is completed by matching high-order structure information and hyperedge affinity between hypergraphs, and then a binary change graph between the remote sensing images is obtained. In addition, the invention further provides a passive target positioning strategy, the center of a change connected domain in a binary change graph is solved, a position change reference point of an active target is generated, and a longitude and latitude grid under an image plane is constructed by means of a reference point geographic coordinate provided by a Beidou system. And the positions of other passive ground object targets in the image plane are accurately estimated.

The invention discloses a subscription type remote sensing change monitoring method based on a large-scale constellation, belongs to the technical field of satellite technologies and remote sensing application services, solves the technical problems of a traditional change monitoring mode, and can meet the customized change monitoring requirement of a user in a large-area and high-frequency requirement scene. Generating a reference period remote sensing image of the subscription area; when the satellite accords with the shooting condition determined by the shooting decision model, scheduling the satellite to shoot the subscription area to obtain effective remote sensing image data; generating a change probability graph based on the reference period remote sensing image of the subscription area, the effective remote sensing image data after quality inspection and a deep learning change detection algorithm, and generating and outputting a to-be-pushed pattern spot based on the change probability graph; and repeating the above steps to produce the multi-expected to-be-pushed pattern spots, pushing the pattern spots meeting the standard based on the multi-expected to-be-pushed pattern spots and the variable pattern spot background database, and synchronously updating the variable pattern spot background database.

Systems and methods include mitigating interference in satellite communications, using predicted in-line event (ILE) detection between satellites of different systems operating on the same frequency, each prediction being generated via change detection algorithms based on machine learning models or ephemeris-based predictions so as to anticipate reductions in signal quality before they occur. Upon detection, proactive adaptive coding and modulation (P-ACM) adjusts signal characteristics in accordance with the predicted ILEs, the adjustments being sufficient to mitigate interference prior to degradation. Additionally, successive interference cancellation (SIC) and cooperative spectrum sharing techniques are employed to further enhance performance, wherein the combination of P-ACM, SIC, and cooperative spectrum sharing is sufficient to improve signal reliability, reduce latency, and optimize spectral efficiency in dense satellite networks, particularly in low Earth orbit (LEO) systems.

The application discloses a remote sensing image change detection algorithm based on multi-dimensional feature collaborative modeling. The steps are as follows: image block preprocessing and division of remote sensing image change detection public reference data set; a remote sensing image change detection model based on multi-dimensional feature collaborative modeling is constructed, for the output from the feature extraction stage, the main branch inputs it into a double-branch feature difference modeling module to model the difference of each layer of double-time feature pairs to form multi-scale difference features, and then inputs it into a boundary protection feature calibration module to strengthen the edge, and the enhanced features are input into a decoder to generate a detection result; the auxiliary branch inputs it into a style difference feature alignment module; after the model is trained, the test set is tested. On the four evaluation indexes of precision, recall, F1 score and intersection over union, compared with the existing change detection method FC-Siam-Conv, it is respectively increased by 3.72%, 6.93%, 5.18% and 8.38%; compared with the existing change detection method BIT, it is respectively increased by 1.63%, 1.11%, 1.38% and 2.32%.

The present application relates to a kind of electronic chart incremental updating file generation method, belong to marine surveying and mapping technical field, by constructing "EN object record table" to store original data, "ER object record table" records change information, in combination with topological influence diffusion analysis, realize the cascade change identification of point, curve, combination geometry and associated elements.Especially, for complex curve editing, a geometric change detection algorithm based on "node sequence double-pointer matching and tolerance threshold judgment" is designed, which can accurately identify the insertion, deletion and coordinate update operation of nodes, and mark the change type in the ER record table.This method effectively improves the automation, accuracy and traceability of electronic chart updating, and ensures the timeliness of navigation data.

The invention discloses a domain generalization remote sensing image change detection method based on domain invariant feature extraction, and the method comprises the following steps: carrying out the geometric enhancement of obtained synthetic dual-phase remote sensing image data, and dividing an obtained enhanced synthetic data set into a training set and a test set; constructing a domain generalization remote sensing image change detection network based on semantics; selecting a change detection algorithm as a feature extractor to obtain an output feature of an original image pair and an output feature of an enhanced image pair which are consistent with the output in spatial size, and respectively inputting the two groups of features into a prediction head of 3 * 3 convolution sharing weight to obtain two detection result images; constructing an auto-covariance matching loss function and a cross-covariance diagonal loss function; and setting an overall loss function, iteratively training and optimizing network parameters, and inputting a detection image into the trained neural network after the loss is stable to obtain a final detection result graph. According to the method, the domain offset between the training domain data set and the unseen domain data set can be effectively reduced, so that the generalization ability of an existing convolutional neural network model is effectively improved.

The invention relates to the technical field of remote sensing image processing and computer vision, in particular to a multi-temporal remote sensing image change detection method based on operator set search, which comprises the following steps of: adding a multi-temporal remote sensing image into a feature map set; collecting operation and function modules commonly used in a multi-temporal remote sensing image change detection algorithm to form an operation subset; adding the feature map set, the operator subset and the combined structure into a micro neural network structure search; searching a proper feature map and operator in the micro neural network structure, and outputting an optimal result map together with the combined structure; the operator set comprises an enhancement operator and a difference operator, and the combined structure performs enhancement and difference operation on the image by using the enhancement operator, and then outputs a difference result after enhancing the difference result. According to the method, accurate extraction of change details is realized, meanwhile, the anti-interference capability of remote sensing data is enhanced, high efficiency and accuracy of results are ensured, and powerful technical support is provided for data analysis and decision making in key fields.

The application provides an SOC abnormality detection method of a battery pack, comprising the following steps: S1: obtaining a plurality of charging and discharging operation data segments based on historical actual operation data of the battery pack; S2: calculating a sequence corresponding to time and an actual available capacity value under each charging and discharging operation data segment; S3: inputting the plurality of sequences corresponding to time and the actual available capacity value into a mutation detection algorithm and a cumulative change detection algorithm in a time sequence and in a sliding window mode, obtaining an SOC abnormality time point through the mutation detection algorithm, and obtaining an SOC abnormal window segment through the cumulative change detection algorithm.

A computer vision system for automatic identification, tracking and management of inventory and / or assets, wherein: the computer vision system is programmed with predetermined and configurable image data conditions that identify and capture certain types of movement; the computer vision system is programmed to constantly capture and process image data from one or more sets of sensors to detect the image data conditions; the computer vision system is programmed to trigger the process of identifying all objects and extracting label information from objects present in a field of view of the system if the image data conditions are fulfilled; the computer vision system processes the image data with one or more object detection algorithms and label detection algorithms and generate correspondences between objects and labels identified; and the computer vision system running change detection algorithms and semantic analysis to detect if any object was moved, removed, occluded and if any new objects were added in to the system.