With the ongoing advancement of the Low-Altitude Economy (LAE) and Low-Altitude Intelligent Networking (LAIN), the demand for multi-UAV cooperative data collection task allocation has become increasingly prominent in scenarios such as environmental monitoring. However, existing methods suffer from issues such as single optimization objectives, poor allocation performance, as well as high computational complexity. To address these issues, this study proposes a dual-objective multi-UAV data collection task allocation model for LAIN scenarios and introduces an improved multi-objective artificial rabbit optimization algorithm based on elite strategy (named ENS-ARMO). Considering the mapping between the task allocation model and the optimization algorithm, a hybrid encoding scheme is proposed. Comparative experiments with MOGA, MOACO, and NSGA-II under two task scales demonstrate that ENS-ARMO not only enhances data collection performance but also significantly reduces total flight distance, highlighting its efficiency and applicability in the context of the low-altitude economy.

Recently, graph neural networks have attracted great attention and achieved outstanding success in hyperspectral image (HSI) classification. However, most existing methods rely on pairwise relationship, neglecting more complex higher-order interactions, which limits the learning of deeply embedded features. Additionally, their dependence on predefined graph structures restricts dynamic node information aggregation. To solve above problems, this paper proposes a CNN enhanced hypergraph attention network (CEHGAT). In order to reveal the high-order interaction in HSI, a hypergraph attention network branch is developed to learn the dynamic connection of hyperedges through the attention mechanism to reveal more representative node embeddings. Then, the CNN enhanced branch uses two multi-scale convolutional blocks to enhance the spatial-spectral features. Finally, the features captured by two branches are fused to realize the complementary advantages of superpixel-level and pixel-level features. Experiments on three benchmark HSI datasets demonstrate that CEHGAT outperforms other state-of-the-art methods with limited labeled samples.

Hyperspectral image (HSI) classification holds great potential for practical applications, but its widespread adoption is limited by the high cost of manual annotation. While unsupervised domain adaptation (UDA) offers a solution by transferring knowledge from labeled source domains (SDs) to unlabeled target domains (TDs), existing methods primarily focus on statistical-level distribution alignment, neglecting instance-level variations in TD data. In addition, for the interfering information such as noise and redundancy that are prevalent in HSI, there are few methods to consider processing the original data at the point level. To overcome these limitations, we propose a mask-guided and confidence-driven UDA (MCUDA) method. It introduces point-level learnable masks to dynamically optimize the input HSI data cube, effectively suppressing interference and enhancing domain-invariant feature extraction. It also proposes a pseudolabel sample set generation strategy based on the idea of confident learning, which takes into account the instance-level differences and domain-related information of TD data. Comprehensive experiments on two cross-scene datasets demonstrate that MCUDA outperforms existing UDA methods, achieving superior classification accuracy.

The classification of land cover material based on hyperspectral image (HSI) has important research significance. Owing to the high cost of obtaining labeled samples and insufficient training samples, the research of cross-scene HSI classification (CS-HSIC) is receiving more and more attention. At present, the performance of the feature extraction module of CS-HSIC is relatively poor, and the number of training parameters is usually large. To fill the shortcomings of domain generalization (DG) methods and reduce the number of parameters, we propose a lightweight DG network with an attention-assisted cascaded bottleneck (ACB), and it adopts a lightweight bottleneck and multiattention design. This model is adept at extracting domain invariant information contained in the source domain (SD), and it may be flexibly embedded into other models. The experimental results show that our network has good classification accuracy and DG ability when the number of training samples is a little small. As a feature extraction subnetwork, it can improve the performance of the original model or reduce the required resources. The code will be available athttps://github.com/zhulongyu1234/ACB.

Inspired by the behavior law of human social groups, a new swarm intelligence algorithm named the dual-population social group optimization (DPSGO) algorithm is proposed in this article. Based on the primitive social group optimization (SGO) algorithm, dual-population grouping technology, reverse learning technology, immigration migration technology, and Gaussian mutation are introduced to further simulate the behavior law of actual human social groups. Experimental results and performance comparison show that the DPSGO algorithm has a better searchability and convergence rate. In addition, aiming at the socially hot issue of aviation safety, the simulation and experimental results show that the temperature measurement error can be reduced to less than 7.5 °C by using the DPSGO algorithm combined with reflected radiation correction to process the aeroengine multispectral radiation temperature measurement data. This article is of great significance to the design and optimization of swarm intelligence algorithms by using the behavior law of human social groups and provides valuable guidance for enhancing the safety monitoring of aeroengines.

Hyperspectral images have been playing an important role in the field of ground object classification because of their rich spatial and spectral information. Aiming at how to extract complex feature information from hyperspectral images, we propose a new feature fusion network model(DAFFN) with dual attention mechanism, which is mainly used to capture more accurate global-local context attention features. The model extracts global context attention features using self-attention mechanism and local context attention features using cross - attention mechanism. Considering the problem that position information is easily lost during the conversion of attention mechanism, we propose a position self-calibration module that can be flexibly embedded into two attention modules. In addition, in order to better integrate global and local features, we also designed a multi-scale global and local feature fusion module (MSGL), which preserves more representative features with less communication costs by aggregating global and local attention features. We have carried out experiments on three commonly used hyperspectral datasets, and the classification results show that our model can achieve high classification accuracy even in the case of a limited number of samples.

Hyperspectral image (HSI) classification is one of the basic tasks of remote sensing image processing, which is to predict the label of each HSI pixel. Convolution neural network (CNN) and graph convolution neural network (GCN) have become the current research focus due to their outstanding performance in the field of HSI classification in recent years. However, GCN is a transductive learning method, which needs all nodes to participate in the training process to get the node embedding. Graph sample and aggregation (GraphSAGE) is an important branch of graph neural network, which can flexibly aggregate new neighbor nodes in non-Euclidean data of any structure, and capture long-range contextual relationships. Superpixel-based GraphSAGE can not only integrate the global spatial relationship of data, but also further reduce its computing cost. CNN can extract pixel-level features in a small area, and our center attention module (CAM) and center weighted convolution (CW-Conv) can also improve the feature extraction ability of CNN by enhancing the dominant position of target pixels. In order to make full use of the advantages of CNN and GraphSAGE, we propose a center weighted convolution and GraphSAGE (CW-SAGE) cooperative network for HSI classification. Specifically, graph simple and aggregate branch is constructed by superpixel-based encoder and decoder modules, then pixel-level features are extracted by a central attention convolutional neural network. Finally, the features of the two branches are spliced together for feature fusion. We conduct experiments on three hyperspectral datasets and compare the results with other current state-of-the-art methods. A series of experiments demonstrate the advantages of our method.

In recent years, deep learning methods have made great progress in hyperspectral image classification. However, the current models obtain deep-seated global feature by deepening the number of network layers, ignore the local neighborhood environment. To solve this problem, we propose a double-branch network model, which extracts spatial and spectral local context feature respectively, fuses different levels of feature adaptively and has strong ability to aggregate context information. Specifically, this model uses self-calibrated convolution to extract spatial information and multi-scale dense convolution to extract spectral information. We propose that local context feature extraction (LCFM) module makes full use of the background information and mutual information between positions of each pixel to obtain local context feature information. In addition, focal-loss is used to solve the problem of different classification difficulty of each sample. Simulation results showed that the proposed module not only achieves high classification accuracy, but also greatly reduces the amount of calculation and parameters.

Hyperspectral image classification methods based on deep learning and attention mechanism have been extensively studied in recent years because of their superior performance. However, the currently applied spatial attention mechanism and channel attention mechanism are separated from each other. For this reason, we propose a new dual-triple attention network (DTAN), which realizes the high-precision classification of hyperspectral images based on capturing cross-dimensional interactive information. Specifically, DTAN is divided into two branches to extract the spectral information and spatial information of the hyperspectral image, which are called the spectral branch and spatial branch. While applying the channel attention mechanism to the spectral unit, the cross-dimensional interaction between the channel dimension and the spatial dimension is constructed. When the spatial attention mechanism is applied to the spatial branch, the correlation with the channel domain is also considered. Moreover, we introduce an efficient channel attention (ECA) module into the DenseNet, which allows the DenseNet to achieve partial cross-channel interaction. A series of experiments proved that DTAN has a significant advantage compared to other models when the training samples are minimal.

Convolutional neural networks (CNNs) have exhibited extraordinary achievements in hyperspectral image (HSI) classification due to their detailed representation of features. However, the improvement of classification accuracy often leads to an evident increase in the complexity of the model, which makes it challenging for the model with the state-of-the-art performance to be applied in the actual scene. Considering MobileNetV3 as a lightweight feature extractor, this article proposes a model suitable for HSI classification based on MobileNetV3. To decrease the problem of massive redundant calculations in the existing spatial attention module, this article proposes a more concise and efficient spatial attention module based on the visual feature maps experiment. Besides, multiclass focal-loss is applied to solve the problem that the difficulty of classification varies for each sample. The experimental results demonstrate that in the case of using very few training sets, the proposed model can tremendously reduce the number of calculations and parameters while maintaining high accuracy.