9results about How to "Improve Change Detection Accuracy" patented technology

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.

The invention provides a hyperspectral spatial-spectral fusion classification method, a hyperspectral fast detection device and a storage medium, and the method comprises the steps: employing a twin network layer, and enabling two convolutional neural network branches of the twin network layer to extract the feature value of the spatial dimension and the feature value of the spectral dimension of hyperspectral data respectively, the two convolutional neural network branches share a weight; inputting the feature value of the spatial dimension and the feature value of the spectral dimension into a feature fusion layer for fusion to form a fusion feature vector; inputting the fusion feature vector into a shared network layer, and carrying out global modeling by using a Transform model; and inputting the output features of the Transform model into a classifier formed by a full connection layer, and carrying out final classification prediction. According to the method, the spatial-spectral characteristics of the hyperspectral data are fully utilized, and the classification precision and robustness are improved.

The application provides a three-dimensional point cloud change detection method based on Siamese AdaptConv, and belongs to the field of intelligent scene reconstruction; firstly, three-dimensional point cloud data of a to-be-detected scene at two different time points is acquired, and an adaptive neighborhood is constructed for each point; then, the pretreated three-dimensional point cloud data is input into a Siamese AdaptConv double-branch network, multi-scale features of each layer are fused respectively, corresponding fusion features are obtained, the change probability of the same position point is further calculated, and a candidate change point set is mapped; DBSCAN clustering is performed, and a candidate region is output; finally, semantic verification is performed on the candidate region, a geographical knowledge graph is pre-constructed, and semantic correlation is calculated; when the semantic correlation exceeds a set threshold value, it is considered that the three-dimensional point cloud change of the candidate region is consistent with the existing entity semantics in the knowledge graph, and the reasonable change is determined; and the application realizes end-to-end multi-sequential feature alignment and change identification.

The present invention proposes a remote sensing image change detection method based on neural network structure search, and the implementation steps are: obtaining training, verification, and test sample sets; constructing a super neural network model; iteratively training the super neural network model; using a genetic algorithm to search the trained super neural network model to obtain structure search parameters; constructing a remote sensing image change detection model based on the structure search parameters; iteratively training the remote sensing image change detection model; and obtaining remote sensing image change detection results. The present invention uses a genetic algorithm to search the trained super network neural model, and determines the structure of the remote sensing image change detection model through the structure search parameters obtained by the search. The structure of the remote sensing image change detection model can be highly matched with the characteristics of the remote sensing image, and the remote sensing image features can be more fully extracted, effectively improving the change detection accuracy of the remote sensing image.

The present application relates to the technical field of image detection, and particularly relates to a scene change detection and damage assessment method based on infrared polarization imaging, wherein the scene change detection method comprises the following steps: acquiring infrared intensity, infrared polarization degree and polarization angle images of a target scene before and after a change; performing feature registration on the infrared intensity, infrared polarization degree and polarization angle images before and after the change respectively; performing difference processing to obtain three kinds of two-phase difference images corresponding to the infrared intensity, infrared polarization degree and polarization angle; based on the two-phase difference images, candidate change point image blocks are obtained through image block extraction and screening processing; the candidate change point image blocks are input into a trained detection model to obtain a change detection result; and based on the change detection result, a final change detection image result of the target scene is obtained through region filling and noise filtering. The present application can make full use of scene infrared polarization features, improve scene change detection accuracy, and provide a new technical approach for damage effectiveness assessment.

The application provides a remote sensing image building change detection method based on matching optimization, comprising the following steps: step 1: high-precision building recognition is performed on two images before and after, a graph model is constructed according to image change intensity information and edge intensity characteristics, and a building change candidate area of two time phases before and after is obtained; step 2: a method based on motion statistical feature matching is used to estimate the roof relationship, and a fast robust filtering strategy is used for judgment and correction, so that the stability of the matching process is improved; step 3: a fully connected conditional random field model is established, building change information of the candidate area of two time phases before and after and roof matching results are integrated together for cooperative optimization, and the influence of position difference on building change detection is eliminated. The application can accurately capture potential building change areas, and overcomes the problem that building change detection of high-resolution remote sensing images is difficult to find and complete under complex and diverse scenes.

This scheme discloses a method for constructing a change detection model for remote sensing image change detection. The model includes two twin networks, each of which includes an input layer, a network layer 1, a network layer 2, and a classification layer in sequence. The input of the input layer is image block 1 and image block 2. The detection model obtains a difference image of the two image blocks according to the output of the two classification layers. The input layer includes a convolution module, an FMBConv module, and an ASPP module. The network layer 1 includes a first dual-branch module and a second dual-branch module, one of which includes a deformable convolution. The network layer 2 includes a multi-head self-attention bottleneck module. This scheme uses a dual-branch structure including deformable convolution and a multi-head self-attention mechanism, so that the model increases its receptive field while ensuring the spatial resolution of the feature map, thereby improving the detection accuracy of the model.

A method for extracting building change areas in dual-phase remote sensing images based on a twin hybrid attention mechanism and multi-scale feature fusion, preprocessing the acquired remote sensing image building change detection data set and dividing it into a training set, a validation set and a test set; constructing a remote sensing image building change detection network; inputting the training set and the validation set into the remote sensing image building change detection network in turn for training and verification, and saving the weight parameter file; inputting the test set into the trained remote sensing image building change detection network for testing, loading the weight parameter file, and outputting the remote sensing image building change detection result; the present invention can effectively perform feature fusion and jointly extract multi-scale features by introducing a hybrid attention feature extraction module, a multi-scale feature fusion module and a hole coordinate attention upsampling module, and has the advantages of fully extracting feature information, avoiding information redundancy and noise interference, and enhancing the model's ability to capture global image information.

The invention provides a flood damaged farmland detection method and device, equipment and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: obtaining a first remote sensing image and a second remote sensing image of a to-be-detected farmland area; based on the first remote sensing image and the second remote sensing image, applying to a flood damage change detection model to obtain a flood damage map, and constructing a twin deep convolutional neural network; performing model training on the twin deep convolutional neural network based on the marked sample image data to obtain a pre-trained flood damage change detection model; predicting the unmarked sample image data based on a pre-trained flood damage change detection model to obtain a flood damage false label probability graph; and performing semi-supervised training on a pre-trained flood damage change detection model based on the marked sample image data, the unmarked sample image data and the flood damage false label probability graph to obtain a trained flood damage change detection model. Therefore, the flood damaged farmland change detection precision is improved.