3results about How to "Improve Change Detection Accuracy" patented technology

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 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.