With the continuous development of artificial intelligence, swarm control and other technologies, the application of Unmanned Aerial Vehicles (UAVs) in the battlefield is more and more extensive, and the UAV swarm is increasingly playing a prominent role in the future of warfare. How tasks are assigned in the dynamic and complex battlefield environment is very important. This paper proposes a task assignment model and its objective function based on dynamic information convergence. In order to resolve this multidimensional function, the Wolf Pack Algorithm (WPA) is selected as the alternative optimization algorithm. This is because its functional optimization of high-dimensional complex problems is better than other intelligent algorithms, and the fact that it is more suitable for UAV swarm task allocation scenarios. Based on the traditional WPA algorithm, this paper proposes a Multi-discrete Wolf Pack Algorithm (MDWPA) to solve the UAV task assignment problem in a complex environment through the discretization of wandering, calling, sieging behavior, and new individual supplement. Through Orthogonal Experiment Design (OED) and analysis of variance, the results show that MDWPA performs with better accuracy, robustness, and convergence rate and can effectively solve the task assignment problem of UAVs in a complex dynamic environment.

The fusion of multimodal data plays a crucial role in classification tasks. However, existing research typically mines and analyzes the individual features of each data source separately before considering how to fuse them. In contrast, our approach first constructs interactive enhanced fusion features for initial fusion while considering the extraction of individual features, and finally integrates them effectively to utilize the information from each data source more comprehensively. To this end, we propose a novel interactive enhanced network based on multihead self-attention (MSA) and graph convolution. Specifically, we extract individual features from hyperspectral image (HSI) and light detection and ranging (LiDAR) data, and then construct interactive enhanced fusion features based on the row and column features of the central pixel. Individual features focus on the local characteristics of a single data source, while interactive enhanced fusion features strengthen the feature expression of the central pixel through matrix operations, integrating the complementary information of multimodal data. Subsequently, we use graph convolutional networks to construct graph structures for four types of features (interactive enhanced HSI features, interactive enhanced LiDAR features, HSI individual features, and LiDAR individual features), modeling the pixels as nodes and capturing spatial relationships. On this basis, we apply an MSA mechanism to mine spectral dependencies, further extracting global spectral features. Finally, we design a multimodal gated fusion module that effectively integrates these features through its weighting mechanism. The weight allocation is adjusted dynamically according to the characteristics of the feature, achieving optimal fusion of multimodal data. Extensive experiments on three popular HSI and LiDAR datasets verify the superior performance of our method. Our code will be available at https://github.com/haofeng0003/MSA-GCN.

Convolution Neural Networks (CNNs) have demonstrated strong feature extraction capabilities in Euclidean spaces, achieving remarkable success in hyperspectral image (HSI) classification tasks. Meanwhile, Graph convolution networks (GCNs) effectively capture spatial-contextual characteristics by leveraging correlations in non-Euclidean spaces, uncovering hidden relationships to enhance the performance of HSI classification (HSIC). Methods combining GCNs with CNNs have achieved excellent results. However, existing GCN methods primarily rely on single-scale graph structures, limiting their ability to extract features across different spatial ranges. To address this issue, this paper proposes a multiscale segmentation-guided fusion network (MS2FN) for HSIC. This method constructs pixel-level graph structures based on multiscale segmentation data, enabling the GCN to extract features across various spatial ranges. Moreover, effectively utilizing features extracted from different spatial scales is crucial for improving classification performance. This paper adopts distinct processing strategies for different feature types to enhance feature representation. Comparative experiments demonstrate that the proposed method outperforms several state-of-the-art (SOTA) approaches in accuracy. The source code will be released at https://github.com/shengrunhua/MS2FN

Accurately identifying and locating water-crossing objects is of utmost importance for environmental protection. However, traditional detection algorithms often exhibit poor anti-interference performance and low detection accuracy in complex environments. To address these issues, this paper proposes a YOLOW algorithm based on a one-stage object detection algorithm for the automatic identification of water objects. The proposed algorithm incorporates two new modules, namely the SPDCS module and the SPPAUG module, to improve the model’s performance. Specifically, the SPDCS module retains all information in the channel dimension, thereby enhancing the model’s detection accuracy and recognition ability for water-crossing objects. The SPPAUG module performs multiscale feature fusion, which further improves the model’s detection accuracy and recognition ability. Moreover, the C2f module is introduced from YOLOv8 to increase the detection speed. Experimental results on a water-floating object dataset demonstrate that the improved YOLOW model outperforms the standard YOLOv5s algorithm, especially in water-crossing object detection. This research has significant implications for environmental monitoring and protection.