In recent years, transformer-based methods have achieved remarkable progress in hyperspectral image classification (HSIC). However, they often rely heavily on extensive training samples to achieve optimal performance. Moreover, these methods frequently fail to adequately capture diverse local spectral–spatial correlations and multi-granular features inherent in hyperspectral images (HSIs). Crucially, existing approaches often overlook the pivotal role of the target center pixel. Their attention mechanisms tend to focus on irrelevant background regions, thereby reducing feature discriminability and degrading classification accuracy. To address these challenges, we propose a novel Center-Focused Transformer (CFT) framework that seamlessly integrates multi-scale spectral–spatial fusion for HSIC. Our framework comprises three key components. First, the Spectral–Spatial Fusion (SSF) mechanism integrates local and global dependencies by employing PCA alongside a Superpixel Graph Feature Extraction (SGFE) block. Second, the Multi-Granular Feature Enhancement (MGFE) approach strengthens spectral–spatial interactions through patch augmentation, a HybridConv block, and a Multi-Scale CBAM (MS-CBAM) block. Finally, the Focus Center Transformer (FCT) strategy explicitly emphasizes the importance of the central pixel for precise classification by incorporating Gaussian Positional Embedding (GPE) and cross-layer aggregation. Extensive experiments on four public datasets demonstrate that the proposed CFT consistently outperforms state-of-the-art methods, highlighting its potential for practical engineering applications.

The increasing demand for autonomous and open peer-to-peer (P2P) data sharing has driven the widespread adoption of decentralized file systems, such as the InterPlanetary File System (IPFS). However, decentralized data sharing inherently requires distributed access control mechanisms due to the absence of centralized authorities. Although blockchain-based access control has become a primary solution, the public nature of blockchain can unintentionally reveal user attributes, posing significant privacy risks. To address the leakage of attribute sets in blockchain, we propose zk-Guard, a decentralized access control framework integrating blockchain and zero-knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs) tailored for IPFS. To further improve the efficiency of zero-knowledge policy checking and reduce the delay of policy updating, we employ a universal constraint circuit and encode policies into sparse configuration matrices, achieving fine-grained, rapid policy updates without regenerating proving keys while guaranteeing constant-time verification regardless of policy complexity. Additionally, to prevent repeated permission checks for large f iles and improve system responsiveness, zk-Guard integrates Merkle Tree Proof (MTP) mechanisms to securely link sub-data blocks to their root block. Comprehensive theoretical complexity analysis and extensive experiments demonstrate that zk-Guard achieves substantial performance improvements over existing schemes, with constant-time proof verification under 2.5 ms enabling efficient data retrieval, and policy deployment and updates completed within 0.2 seconds even for 1,000 attributes. The source code is available at https://github.com/ningboliucug/zk-Guard.

Federated learning (FL) has emerged as a pivotal collaborative machine learning framework, enabling privacy-preserving analytics for smart city applications using distributed data from Internet of Things (IoT) devices. However, the inherent data heterogeneity that arises from diverse geographical and environmental factors poses significant challenges to the effectiveness of FL-based models. To address these challenges, this paper introduces a novel Prototype-Based FL framework for cross-domain object extraction in heterogeneous remote sensing images. The proposed framework employs multiple vectors to represent class prototypes for capturing the intricate intra-class variations and mitigating the adverse effects of non-identically distributed (non-IID) data across clients. Furthermore, we adopt a distance-based classification method to reduce classification errors. Additionally, we propose a Prototype-Anchored Metric Learning approach to minimize intra-class variance and enhance inter-class separability, which can facilitate the alignment of feature representations across heterogeneous datasets. The proposed method improves the coherence and stability of feature spaces in federated settings and enhances the global model’s generalization capabilities for complex urban monitoring tasks. Extensive experiments on three distinct remote sensing datasets(including infrastructure and disaster) demonstrate that the proposed method significantly outperforms state-of-the-art FL-based approaches in urban monitoring accuracy and robustness. The code is available at

Bibliometric analysis based on the Science Citation Index Expanded published by the Thomson Scientific was carried out to identify the research trend of remote sensing between 2010 and 2014. Our analysis reveals the institutional, national, spatiotemporal, and categorical patterns in remote sensing research both from WP (whole publications) view and HCP (high-cited publications) view. Remote sensing research almost doubled during 2010-2014. Environmental Sciences was the most attractive subject category among remote sensing research. International Journal of Remote Sensing was the most productive journal, but IEEE Transactions on geoscience and Remote Sensing published the most HCP among the 31 distributed journals. The productive ranking of countries was headed by US both in WP view and HCP view, CAS was the most productive institutes both in WP view and HCP view with lower CPP. “Classification”, “Change detection” and “hyperspectral” were emerging as the most prevalent research topic during 2010-2014. The statistics illustrated that remote sensing researchers had turned study interests from “microwave remote sensing” to “hyperspectral remote sensing” during 2010-2014.

Flood information extraction, includes flooded area and inundated duration, using remote sensing data plays a fundamental role on evaluating loss and conducting disaster rescue and relief. However, the clear-sky image is restricted by different weather conditions such as cloud, rain, fog and so on. Consequently, lack of the critical period remote sensing data of flood routing cannot produce inundated duration information timely and rapidly once the hazards happen. In order to overcome the above limitation, a new gapless spatio-temporal shape evolving method for extracting flooded inundated duration is presented in this paper. The emphasis of this study lies in applying Level Set Method (LSM) to insert a series of intermediate surface between bi-temporal images without predefined information. Level Set Method is a better choice because it can handle topology changes to extract object with variable shapes from image. Besides, the proposed method modifies the finite difference model both in space and time to speed up the iteration and improve extraction precision. This paper selects GF-1 images to validate the methodology. The result indicated that the proposed method provides more accurate and less error information when compared with official statistic.