Remote sensing image building change detection aims to identify building changes that occur in remote sensing images of the same areas acquired at different times. In recent years, the development of deep learning has led to significant advancements in building change detection methods. However, these fully supervised methods require a large number of bi-temporal remote sensing images with pixel-wise change detection labels to train the model, which incurs substantial time and manpower for annotation. To address this issue, this study proposes a single-temporal semi-supervised joint learning framework for building change detection, called SemiJointNet. Firstly, to reduce annotation costs, SemiJointNet uses building extraction labels instead of change detection labels to train the change detection model. Furthermore, to improve the semantic understanding capability of the model, SemiJointNet employs a joint learning approach for building extraction and change detection tasks. Lastly, SemiJointNet introduces semi-supervised learning, reducing the need for labels from thousands to dozens. Experimental results on the WHU dataset demonstrate that the proposed SemiJointNet achieves excellent building change detection performance with only a few dozen labels.

The Graph Convolutional Network (GCN) has recently achieved promising performance in human motion prediction by modeling the nodes and edges of the human skeleton. However, most previous methods still suffer from two unaddressed drawbacks. First, in the inference stage, their graph topologies are static and fixed, resulting in dependencies between nodes that cannot be dynamically adjusted for different actions. Second, the implicit relationships between pose sequences are ignored, which makes the prior advantages of the graph structure invalid in temporal feature fusion. To address these limitations, we propose an adaptive spatial-temporal graph-mixer (GraphMixer) for human motion prediction, which consists of a series of fully separated spatial-temporal graph convolution structures. In spatial GCN, we construct an additional adaptive skeleton graph to capture the node features of action-specific poses. In temporal GCN, we introduce a variety of graph topologies to enhance feature fusion between pose sequences. Comparing state-of-the-art algorithms on the Human3.6 M and the 3DPW datasets and ablation studies shows that our GraphMixer and the proposed multiple graph topologies are effective and critical. The code is publicly available athttps://github.com/young0304/Adaptive-Spatial-Temporal-Graph-Mixer.

Integrated visible light positioning and communication (VLPC), capable of combining advantages of visible light communications (VLC) and visible light positioning (VLP), is a promising key technology for the future Internet of Things. In VLPC networks, positioning and communications are inherently coupled, which has not been sufficiently explored in the literature. We propose a robust power allocation scheme for integrated VLPC Networks by exploiting the intrinsic relationship between positioning and communications. Specifically, we derive explicit relationships between random positioning errors, following both a Gaussian distribution and an arbitrary distribution, and channel state information errors. Then, we minimize the Cramer-Rao lower bound (CRLB) of positioning errors, subject to the rate outage constraint and the power constraints, which is a chance-constrained optimization problem and generally computationally intractable. To circumvent the nonconvex challenge, we conservatively transform the chance constraints to deterministic forms by using the Bernstein-type inequality and the conditional value-at-risk for the Gaussian and arbitrary distributed positioning errors, respectively, and then approximate them as convex semidefinite programs. Finally, simulation results verify the robustness and effectiveness of our proposed integrated VLPC design schemes.

Online maps play an essential role in modern life. The convenience of acquiring remote sensing images provides reliable geographic information sources for the compilation of online maps. Some existing works have used the idea of domain mapping to translate remote sensing images into maps directly, which is of great prospect for application. However, many of the current remote sensing image-to-map translation works are performed in an unsupervised manner that would lead to problems such as distortion and local detail inaccuracy. Although the fully-supervised method is effective, it requires plenty of paired as well as matched data for training. Paired remote sensing images and maps with consistent spatial locations can be easily accessed through online map services, whereas many pairs of samples in which some geographic element information is not accurately and completely matched. Supervised learning-based translation models are often confused by these unmatched data. Accurate and complete matched data has to be selected deliberately by humans, and the manual selection process is time-consuming and laborious, which brings new challenges. Therefore, we propose a novel remote sensing image-to-map translation model named Semi-MapGen based on semi-supervised generative adversarial networks (GAN), which requires only a small set of accurate and complete matched data and plenty of unpaired data. In this model, we apply a knowledge extension-based learning strategy that can improve the accuracy of translated maps. In addition, we design Expansion loss and Channel-wise loss to learn the information from massive unpaired data in an unsupervised manner. Qualitative and quantitative experiment results on three datasets demonstrate that the proposed model outperforms state-of-the-art semi-supervised and supervised methods.

Remote sensing (RS) images change detection (CD) aims to obtain change information of the target area between multi-temporal RS images. With the modernization of cities, building change detection (BCD) plays a pivotal role in land resource planning, smart city construction and natural disaster assessment, and it is a typical application field of change detection task. Recently, deep learning based methods have shown their superiority in RS image change detection. However, the performance of the existing supervised change detection methods relies heavily on a large amount of high quality annotated bi-temporal RS image as training data, which is usually hard to obtain in practice. To address this issue, a semi-supervised BCD method using a pseudo bi-temporal data generator with consistency regularization was proposed. This method only needs a very small amount of single-temporal RS images with building extraction labels as labeled data. Firstly, with the help of the pseudo bi-temporal data generator, the model can generate a large number of pseudo bi-temporal images with CD labels from a small number of single-temporal images and corresponding building extraction labels automatically, which greatly augments the labeled data set for CD model training. Then, we proposed an error-prone data enhancement fine-tuning strategy to improve the learning effect of the proposed model to these synthesized training data. Finally, we enhance the robustness of the model by forcing the model to make consistent predictions on the images before and after perturbations. Extensive experimental results demonstrate that our method can effectively improve the BCD performance of the model even if labeled data are scare, and outperforms the state-of-the-art methods.

In this paper, we propose joint beamforming and photo-detector (PD) orientation (BO) optimization schemes for mobile visible light communication (VLC) with the orientation adjustable receiver (OAR). Since VLC is sensitive to line-of-sight propagation, we first establish the OAR model and the human body blockage model for mobile VLC user equipment (UE). To guarantee the quality of service (QoS) of mobile VLC, we jointly optimize BO with minimal UE the power consumption for both fixed and random UE orientation cases. For the fixed UE orientation case, since the transmit beamforming and the PD orientation are mutually coupled, the joint BO optimization problem is nonconvex and intractable. To address this challenge, we propose an alternating optimization algorithm to obtain the transmit beamforming and the PD orientation. For the random UE orientation case, we further propose a robust alternating BO optimization algorithm to ensure the worst-case QoS requirement of the mobile UE. Finally, the performance of joint BO optimization design schemes are evaluated for mobile VLC through numerical experiments.

With the boost of deep learning methods, road extraction has been widely used in city planning and autonomous driving. However, it is very challenging to extract roads around the thorny occlusion areas, even in high-resolution remote sensing images. Existing approaches regard road extraction as an isolated binary segmentation task and ignore the surroundings’ contextual information in the optical image itself, especially the potential dependence implied between roads and buildings. To address the occlusion problem, we proposed a spatial attention-based road extraction neural network using contextual relation between roads and buildings named MSACon to extract the roads more precisely. First, we employed an existing building extraction method to predict buildings in the optical images. Second, we calculated the signed distance map (SDM) based on the building extraction results (which may be inaccurate) as ambiguous auxiliary information to infer the optical images’ potential roads. Due to the color, lines, and texture between the optical images and the SDM are distinct, we then designed the two-branch encoder to extract features and integrated the cross-domain features into the road decoder by a spatial attention-based fusion mechanism. Experiments demonstrate that the proposed method achieves superior performance than other state-of-the-art approaches even with ambiguous auxiliary information. Furthermore, MSACon shows obvious advantages in finding inconspicuous roads in the optical images and eliminating noisy roads, especially when dealing with areas where buildings are located along the roads.

Remote sensing image registration is the basis of change detection, environmental monitoring, and image fusion. Under severe appearance differences, feature-based methods have difficulty in finding sufficient feature matches to solve the global transformation and tackling the local deformation caused by height undulations and building shadows. By contrast, nonrigid registration methods are more flexible than feature-based matching methods, while often ignoring the reversibility between images, resulting in misalignment and inconsistency. To this end, this article proposes a nonrigid bidirectional registration network (NBR-Net) to estimate the flow-based dense correspondence for remote sensing images. We first propose an external cyclic registration network to strengthen the registration reversibility and geometric consistency by registering Image A to Image B and then reversely registering back to Image A. Second, we design an internal iterative refinement strategy to optimize the rough predicted flow caused by large distortion and viewpoint difference. Extensive experiments demonstrate that our method shows a performance superior to the state-of-the-art models on the multitemporal satellite image dataset. Furthermore, we attempt to extend our method to heterogeneous remote sensing image registration, which is more common in the real world. Therefore, we test our pretrained model in a satellite and unmanned aerial vehicle (UAV) image registration task. Due to the cyclic registration mechanism and coarse-to-fine refinement strategy, the proposed approach obtains the best performance on two GPS-denied UAV image datasets. Our code will be released athttps://github.com/xuyingxiao/NBR-Net.

In this study, we investigated the nonorthogonal multiple access (NOMA) for visible light communication (VLC) Internet of Things (IoT) networks and provided a promising system design for 5G and beyond 5G applications. Specifically, we studied the capacity region of a practical uplink NOMA for multiple IoT devices with discrete and continuous inputs, respectively. For discrete inputs, we proposed an entropy approximation method to approach the channel capacity and obtain the discrete inner and outer bounds. For the continuous inputs, we derived the inner and outer bounds in closed forms. Based on these results, we further investigated the optimal receiver beamforming design for the multiple access channel (MAC) of VLC IoT networks to maximize the minimum uplink rate under receiver power constraints. By exploiting the structure of the achievable rate expressions, we showed that the optimal beamformers are the generalized eigenvectors corresponding to the largest generalized eigenvalues. Numerical results show the tightness of the proposed capacity regions and the superiority of the proposed beamformers for VLC IoT networks.

With the appearance of satellite cluster nowadays, some new challenges have emerged in satellite data transmission scheduling. Current researches ignored the new features of the satellite cluster data transmission such as the periodicity of data transmission window collisions which could direct the future scheduling. Considering the characteristics of the problem, a data transmission conflict window model for satellite cluster is established and a novel algorithm based on genetic algorithm is proposed. In order to improve the searching efficiency, convergence rate and stability of our algorithm, a rote learning operator is designed, which can generate heuristic from past scheduling results and lead searching direction of our algorithm. Finally, some experiments are conducted to validate the correctness and practicability of our algorithm.

We present a novel fusion algorithm for electronic-reconnaissance (ER) satellite and optical imaging satellite data using coherent point set (CPS) analysis. This work is motivated by a large-scale maritime surveillance problem, where ship groups in the observations are of particular interest for tactical and strategic operations. Fusion of observations from ER satellite and optical imaging satellite is a challenging task. On the one hand, dense and continuous measurement is not available for optical imagery. On the other hand, it is difficult to extract robust features from ER measurements. Considering that the size of a ship is often less than the distance among different ships, we treat each ship as a mass point. The contributions of our work are threefold. First, multisensor data fusion is accomplished by CPS association. To the best of our knowledge, this letter is the first to investigate CPS for multimodal remote sensing data fusion. Second, a novel geometry descriptor, which encodes the topological characteristics of a point set, is presented. Third, we combine both topological features and attributive features within the framework of Dempster–Shafer theory for CPS analysis. The proposed method has been tested using different sets of simulated data and recorded data. Experimental results demonstrate the effectiveness of the proposed method.

No abstract available.