Change detection (CD) in hyperspectral images (HSIs) has become an increasingly vital research field in remote sensing. Over the past few years, the adoption of deep learning approaches, particularly convolutional neural network (CNN) and transformer-based architectures have significantly advanced performance in this field. While these models effectively capture spectral-spatial features, they may also introduce redundant or irrelevant spatial information, potentially degrading the accuracy of HSI CD. To address this challenge, a center-pixel and gated mechanism-based attention network (CGMNet) is proposed for HSI CD, leveraging the central pixel's significance to enhance accuracy and robustness. First, a gated-based center spatial attention (GCSA) module is designed to emphasize spatial relationships surrounding the central pixel. By incorporating gating mechanisms, GCSA selectively enhances relevant spatial features while suppressing irrelevant information. Second, a gated-based spectral attention (GSA) module is proposed to dynamically highlight the most significant spectral features, ensuring an effective spectral representation. Finally, a global transform fusion (GTF) module is proposed to capture global contextual information and to fuse it with the extracted spatial and spectral features. Moreover, we introduce a novel benchmark dataset, named the Hangzhou Bay (HZB), specifically designed to advance coastal remote sensing research. Experimental evaluations conducted on three publicly available datasets, as well as the HZB dataset, show that our CGMNet consistently outperforms some state-of-the-art methods in the HSI CD task. The source code of the proposed CGMNet, along with the HZB dataset, will be made publicly available at https://github.com/creativeXin/CGMNet.

Recently, deep neural networks have demonstrated outstanding performance in hyperspectral image (HSI) change detection (CD), especially when there is sufficient labeled samples. However, the labels of HSI are difficult to obtain, and acquiring enough labels to train deep network is a great challenge in practice. Therefore, to mitigate the effect of insufficient labels in detection results, this paper proposes a cross-scene few-shot learning (FSL) network based on intra-inter domain contrastive alignment (CAFSL) for HSI-CD, which combines contrastive learning (CL) and FSL into a unified framework, aiming to achieve better detection results using only a few labeled samples. Specifically, we perform cross-scene FSL using a pair of dual-phase images from a very high-resolution image (VHRI) as the source domain and a pair of HSI as the target domain. Then, an intra-domain supervised contrastive learning (INSCL) module is designed to enhance the compactness within classes and widen the discrimination between classes by maximizing the feature similarity of intra-class and minimizing the feature similarity of inter-class. Finally, a cross-domain contrastive alignment (CRCA) module is proposed to align the features of source and target domains, which mitigates the effect of domain migration problems caused by different data types. Experiments on three HSI benchmark datasets reveal that the CAFSL algorithm outperforms current advanced algorithms based on deep learning and FSL while with limited labeled samples.

Hyperspectral image (HSI) change detection (CD) plays a crucial role in remote sensing observation. It leverages the abundant spectral and spatial information in bi-temporal HSIs to identify subtle Earth surface changes. Most current deep-learning-based HSI CD methods primarily utilize convolutional neural networks or transformers to extract features from bi-temporal images. However, these methods lack an effective attention mechanism to enhance differential features. In addition, they do not fully leverage the aggregation relationship between the features of bi-temporal images to extract interaction features. To address these challenges, we propose a novel adaptive information weighting and synchronized enhancement network (AIWSEN) for HSI CD. This network employs the information entropy to capture change features specific to the CD task and enhances bi-temporal interaction features. Specifically, an adaptive information weighting attention module (AIWAM) leverages the maximum discrete entropy theorem to capture the difference information. A dual-time synchronic change enhancing module (DSCEM) is designed to extract features by interactively aggregating features from bi-temporal HSIs to enhance difference features. A bi-temporal image feature selection and fusion module (BFSFM) is constructed to filter out important features using forget and update gates. Experimental results on three HSI CD datasets demonstrate that the proposed AIWSEN method outperforms several state-of-the-art methods. The source code of the proposed AIWSEN will be released athttps://github.com/creativeXin/AIWSEN.

Hyperspectral image (HSI) change detection (CD) has gained significant attention in the field of remote sensing. Current CD methods typically extract features based on spatial or spectral correlations between bitemporal HSIs, which often overlook the difference information, leading to a decrease in CD accuracy. Furthermore, these algorithms do not fully consider the alignment of features between images across different channel and spatial dimensions. To tackle these issues, we propose a novel approach called the differential information-enhanced feature exchange network (DIEFEN) for HSI CD, which leverages the difference information between images and enhances the alignment of bitemporal image features to improve CD accuracy. Specifically, an enhanced differential multihead attention (EDMA) module is proposed to utilize difference information to guide the feature aggregation of bitemporal images, effectively highlighting changing pixels and suppressing unchanging pixels. A feature focus and long-range attention (FFLA) module is designed to extract local and global features, and a channel-spatial interaction (CSI) module is constructed to align features and mitigate the impact of noise. Experimental results on three HSI CD datasets demonstrate that the proposed DIEFEN method outperforms several state-of-the-art methods. The source code of the proposed DIEFEN is released athttps://github.com/creativeXin/DIEFEN.

In this paper, we study the performance of rate-splitting multiple access (RSMA)-aided unmanned aerial vehicle (UAV) systems. Specifically, the downlink transmission is considered and multiple UAVs are employed to serve the ground users. In order to evaluate the performance of the system, we derive the expressions of the coverage probability and the area spectral efficiency (ASE) by utilizing stochastic geometry tools, where the distribution of the clustered users is modeled as a Poisson cluster process (PCP). Besides, the influence of the height and the density of UAVs on the coverage probability is investigated. We also highlight the significance of the tradeoff between the power allocations of the common and private messages so as to maximize the system ASE. Furthermore, the superiority of RSMA over the non-orthogonal multiple access (NOMA) scheme is validated in terms of spectral efficiency. Finally, simulation results show the effectiveness of our derived expressions and verify the insights.