In the field of remote sensing image applications, RGB and infrared image object detection is an important technology. The object detection performance can be improved and the robustness of the algorithm will be enhanced by making full use of their complementary information. Existing RGB-infrared detection methods do not explicitly encourage RGB and infrared images to achieve effective multimodal learning. We find that when fusing RGB and infrared images, cross-modal redundant information weakens the degree of complementary information fusion. Inspired by this observation, we propose a redundant information suppression network (RISNet) which suppresses cross-modal redundant information and facilitates the fusion of RGB-Infrared complementary information. Specifically, we design a novel mutual information minimization module to reduce the redundancy between RGB appearance features and infrared radiation features, which enables the network to take full advantage of the complementary advantages of multimodality and improve the object detection performance. In addition, in view of the drawbacks of the current artificial classification of lighting conditions, such as the subjectivity of artificial classification and the lack of comprehensiveness (divided into day and night only), we propose a method based on histogram statistics to classify lighting conditions in more detail. Experimental results on two public RGB-infrared object detection datasets demonstrate the superiorities of our proposed method over the state-of-the-art approaches, especially under challenging conditions such as poor illumination, complex background, and low contrast.

Remote sensing object detection based on the combination of infrared and visible images can effectively adapt to the around-the-clock and changeable illumination conditions. However, most of the existing infrared and visible object detection networks need two backbone networks to extract the features of two modalities, respectively. Compared with the single modality detection network, this greatly increases the amount of calculation, which limits its real-time processing on the vehicle and unmanned aerial vehicle (UAV) platforms. Therefore, this paper proposes a local adaptive illumination-driven input-level fusion module (LAIIFusion). The previous methods for illumination perception only focus on the global illumination, ignoring the local differences. In this regard, we design a new illumination perception submodule, and newly define the value of illumination. With more accurate area selection and label design, the module can more effectively perceive the scene illumination condition. In addition, aiming at the problem of incomplete alignment between infrared and visible images, a submodule is designed for the rapid estimation of slight shifts. The experimental results show that the single modality detection algorithm based on LAIIFusion can ensure a large improvement in accuracy with a small loss of speed. On the DroneVehicle dataset, our module combined with YOLOv5L could achieve the best performance.

Knowledge graph construction is aimed at storing and representing the knowledge of the objective world in a structured form. Existing methods for automatic construction of knowledge graphs have problems such as difficulty in understanding potential semantics and low precision. The emergence of Large Language Models (LLMs) provides an effective way for automatic knowledge graph construction. However, using LLMs as automatic knowledge graph construction engines relies on the embedding of schema layers, which brings challenges to the input length of LLMs. In this paper, we present a framework for Adaptive Construction of Knowledge Graph by leveraging the exceptional generation capabilities of LLMs and the latent relational semantic information of triples, named ACKG-LLM. Our proposed framework divides the knowledge graph construction task into three subtasks within a unified pipeline: triple extraction of open information, additional relational semantic information embedding and knowledge graph normalization based on schema-level embedding. The framework can construct knowledge graphs in different domains, making up for the defects of existing frameworks that need to retrain and fine-tune the internal model. Extensive experiments demonstrate that our proposed ACKG-LLM performs favorably against representative methods on the REBEL and WiKi-NRE datasets. The code is available at https://github.com/KustTeamWQW/ACKG-LLM

With the rapid advancement of remote sensing technology, bi-temporal remote sensing change detection (CD) techniques have also seen significant progress. However, existing CD tasks still face two challenges: 1) Variations in lighting and seasonal factors complicate imaging conditions, causing pseudo-variation interference, and 2) The spatial distribution and shapes of building are diverse, leading to difficulties in extracting and utilizing effective change features. In this paper, we propose the spatial-temporal evolution guided change detection network (STEGNet) to capture and fully utilize rich spatial-temporal information. Specifically, we develop the chrono colorizer to mitigate pseudo-variant interference by standardizing color styles and enriching time series information. Additionally, we introduce the temporal-spatial guidance module (T-SGM), which combines spatial-temporal information to guide the decoding operation and mitigate information loss during spatial-temporal fusion, resulting in finer prediction results. Experimental results on three benchmark datasets demonstrate that STEGNet effectively suppresses pseudo-variation interference and significantly improves the integrity of detection boundaries.

A Reinforcement Learning (RL)-based Image Mapping Topology (IMT) optimization approach is proposed. To enable RL to optimize antenna topology and increase the efficiency of the optimization process. Two crucial components, Stratified Sampling Balance (SSB) and IMT are introduced. SSB is utilized to balance different types of data samples. IMT is utilized to facilitate the interaction between RL and simulation software, it also pretrains the actor network within the RL algorithm. This methodology allows the RL agent to acquire knowledge from the interaction of the antenna topology with the environment. Subsequently, the agent outputs antenna topology with the maximum reward signal. Compared with other machine learning-based optimization methods, the proposed method significantly alleviates the issue of learning inefficiency and reduces the exploration time for feasible solutions. To validate the effectiveness of the optimization method, a microstrip antenna is optimized. The measurement results show that the impedance bandwidth is doubled while maintaining the realized gain with fewer simulation samples and less optimization time.

Over an extended period, considerable research has focused on elaborated mapping in navigation systems. Multispectral point clouds containing both spatial and spectral information play a crucial role in remote sensing by enabling more accurate land cover classification and the creation of more accurate maps. However, existing graph-based methods often overlook the individual characteristics and information patterns in these graphs, leading to a convoluted pattern of information aggregation and a failure to fully exploit the spatial–spectral information to classify multispectral point clouds. To address these limitations, this paper proposes a deep spatial graph convolution network with adaptive spectral aggregated residuals (DSGCN-ASR). Specifically, the proposed DSGCN-ASR employs spatial graphs for deep convolution, using spectral graph aggregated information as residuals. This method effectively overcomes the limitations of shallow networks in capturing the nonlinear characteristics of multispectral point clouds. Furthermore, the incorporation of adaptive residual weights enhances the use of spatial–spectral information, resulting in improved overall model performance. Experimental validation was conducted on two datasets containing real scenes, comparing the proposed DSGCN-ASR with several state-of-the-art graph-based methods. The results demonstrate that DSGCN-ASR better uses the spatial–spectral information and produces superior classification results. This study provides new insights and ideas for the joint use of spatial and spectral information in the context of multispectral point clouds.