Due to the trade-off of temporal resolution and spatial resolution, spatiotemporal image-fusion uses existing high-spatial-low-temporal (HSLT) and high-temporal-low-spatial (HTLS) images as prior knowledge to reconstruct high-temporal-high-spatial (HTHS) images. However, some existing spatiotemporal image-fusion algorithms ignore the issue that the spatial information of HTLS images is insufficient to support the acquisition of spatial information, which leads to the unsatisfactory accuracy of the fusion result. To introduce more spatial information, the algorithm in this article uses Cycle-generative adversarial networks (GANs) to simulate the change process of two HSLT images at k-1 and k+1, and to generate some simulated images between k-1 and k+1. Then, the generated images are selected under the help of HTLS images, and the selected ones are then enhanced with wavelet transform. Finally, the image with spatial information is introduced into the Flexible Spatiotemporal DAta Fusion (FSDAF) framework to improve the performance of spatiotemporal image-fusion. Extensive experiments on two real data sets demonstrate that our proposed method outperforms current state-of-the-art spatiotemporal image-fusion methods.

Widely used in three-dimensional (3D) modeling, reverse engineering and other fields, point cloud registration aims to find the translation and rotation matrix between two point clouds obtained from different perspectives, and thus correctly match the two point clouds. As the most common point cloud registration method, ICP algorithm, however, requires a good initial value, not too large transformation between the two point clouds, and also not too much occlusion; Otherwise, the iteration would fall into a local minimum. To solve this problem, this paper proposes an ICP registration algorithm based on the local features of point clouds. With this algorithm, a robust and efficient 3D local feature descriptor (density, curvature and normal angle, DCA) is firstly designed by combining the density, curvature, and normal information of the point clouds, then based on the feature description, the correspondence between the point clouds and also the initial registration result are found, and finally, the aforementioned result is used as the initial value of ICP to achieve fine tuning of the registration result. The experimental results on public data sets show that the improved ICP algorithm boosts good registration accuracy and robustness, and a fast running speed as well.

Teaching reflection based on videos is the main method in teacher education and professional development. However, it takes a long time to analyse videos, and teachers are easy to fall into the state of information overload. With the development of “AI + education,” automatic recognition of teacher behavior to support teaching reflection has become an important research topic. In this paper, taking online open classroom teaching video as the data source, we collected and constructed a teacher behavior dataset. Using this dataset, we explored the behavior recognition methods based on RGB video and skeleton information, and the information fusion between them is carried out to improve the recognition accuracy. The experimental results show that the fusion of RGB information and skeleton information can improve the recognition accuracy, and the early-fusion effect is better than the late-fusion effect. This study helps to solve the problems of time-consumption and information overload in teaching reflection and then helps teachers to optimize the teaching strategies and improve the teaching efficiency.

High-resolution remote-sensing-image change detection is widely used in urban dynamic monitoring, geographic information updating, natural disaster monitoring, illegal building investigation, and land resource surveys. Common change-detection algorithms are mainly implemented in a fully supervised manner that relies on a large number of high-quality samples. Compared with a building change-detection dataset, a building semantic-segmentation dataset is easier to accumulate and obtain. Making full use of this semantic information in the design of a building change-detection network can effectively reduce the sample size required to train a change-detection model. In view of this, a semantic feature-guided Siamese change-detection framework is devised in this letter. The framework effectively exploits the prior information of building semantic features and uses the popular transformer structure to improve the change analysis module. The results of extensive experiments on two public datasets show that the framework is more accurate than the other state-of-the-art change detection algorithms and can effectively reduce the dependence of data on change detection samples in the model training process.

As a key on-orbit service technology, relative measurement of non-cooperative spatial targets can give accurate relative pose for the unmanned rendezvous and docking of non-cooperative targets. With the rapid development of three-dimensional data acquisition equipment such as flash lidar in the field of non-cooperative targets’ pose measurement in recent years, research on the pose tracking technology based on the three-dimensional (3D) point cloud of non-cooperative targets has become more and more urgent. In this paper, we proposed an approach for autonomous recognition and pose tracking of non-cooperative targets based on point cloud features. For this method, the density, curvature and normal angle of a non-cooperative target 3D point cloud are first calculated as the local feature description (DCA) of the point cloud, and then the feature description is used to match and remove errors through RANSAC. Finally the Iterative Closest Point (ICP) algorithm is adopted to obtain the precise pose of the non-cooperative target. Experimental results have shown that this approach can effectively find the features of non-cooperative tumbling targets, get rid of the dependence on the target model and database, and realize the pose tracking of non-cooperative targets.