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HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年

作者： Wang, Di Hu, Meiqi Jin, Yao Miao, Yuchun Yang, Jiaqi Xu, Yichu Qin, Xiaolei Ma, Jiaqi Sun, Lingyu Li, Chenxing Fu, Chuan Chen, Hongruixuan Han, Chengxi Yokoya, Naoto Zhang, Jing Xu, Minqiang Liu, Lin Zhang, Lefei Wu, Chen Du, Bo Tao, Dacheng Zhang, Liangpei Wuhan University School of Computer Science China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering China Zhongguancun Academy China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing China Sun Yat-sen University School of Geography and Planning China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Chongqing University College of Computer Science China The University of Tokyo Japan RIKEN Center for Advanced Intelligence Project Japan Intelligent Science & Technology Academy Limited CASIC China iFlytek Company Ltd. National Engineering Research Center of Speech and Language Information Processing China Nanyang Technological University College of Computing & Data Science Singapore Henan Academy of Sciences Aerospace Information Research Institute China

Accurate hyperspectral image (HSI) interpretation is critical for providing valuable insights into various earth observation-related applications such as urban planning, precision agriculture, and environmental monitoring. However, existing HSI processing methods are predominantly task-specific and scene-dependent, which severely limits their ability to transfer knowledge across tasks and scenes, thereby reducing the practicality in real-world applications. To address these challenges, we present HyperSIGMA, a vision transformer-based foundation model that unifies HSI interpretation across tasks and scenes, scalable to over one billion parameters. To overcome the spectral and spatial redundancy inherent in HSIs, we introduce a novel sparse sampling attention (SSA) mechanism, which effectively promotes the learning of diverse contextual features and serves as the basic block of HyperSIGMA. HyperSIGMA integrates spatial and spectral features using a specially designed spectral enhancement module. In addition, we construct a large-scale hyperspectral dataset, HyperGlobal-450 K, for pre-training, which contains about 450 K hyperspectral images, significantly surpassing existing datasets in scale. Extensive experiments on various high-level and low-level HSI tasks demonstrate HyperSIGMA's versatility and superior representational capability compared to current state-of-the-art methods. Moreover, HyperSIGMA shows significant advantages in scalability, robustness, cross-modal transferring capability, real-world applicability, and computational efficiency. The code and models will be released at HyperSIGMA. © 1979-2012 IEEE.

关键词： Hyperspectral imaging

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Multi-layer network embedding on scc-based network with motif

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Digital communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic communication and computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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Clustering time-evolving data using an efficient differential evolution 6th

Clustering time-evolving data using an efficient differentia...

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6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015

作者： Chen, Gang Luo, Wenjian Anhui Province key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China HefeiAnhui China

ISBN: (纸本)9783319204659

The previous evolutionary clustering methods for time-evolving data usually adopt the temporal smoothness framework, which controls the balance between temporal noise and true concept drift of clusters. They, however, have two major drawbacks: (1) assuming a fixed number of clusters over time;(2) the penalty term may reduce the accuracy of the clustering. In this paper, a Multimodal Evolutionary Clustering (MEC) based on Differential Evolution (DE) is presented to cope with these problems. With an existing chromosome representation of the ACDE, the MEC automatically determines the cluster number at each time step. Moreover, instead of adopting the temporal smoothness framework, we try to deal with the problem from view of the multimodal optimization. That is, the species-based DE (SDE) for multimodal optimization is adopted in the MEC. Thus the MEC is a hybrid of the ACDE and the SDE, and designed for time-evolving data clustering. Experimental evaluation demonstrates the MEC achieves good results. © Springer International Publishing Switzerland 2015.

关键词： Cluster analysis

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A clustering system for gene expression data based upon genetic programming and the HS-model

A clustering system for gene expression data based upon gene...

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3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice

作者： Liu, Guiquan Jiang, Xiufang Wen, Lingyun Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9780769540306

Cluster analysis is a major method to study gene function and gene regulation information for there is a lack of prior knowledge for gene data. Many clustering methods existed at present usually need manual operations or predetermined parameters, which are difficult for gene data. Besides, gene data possess their own characteristics, such as large scale, high-dimension, and noise. Therefore, a systematic clustering algorithm should be proposed to effectively deal with gene data. In this paper, a novel genetic programming (GP) clustering system for gene data based on hierarchical statistical model (HS-model) is proposed. And an appropriate fitness function is also proposed in this system. This clustering system can largely eliminate the infection of data scale and dimension. The proposed GP clustering system is applied to cluster the whole intact yeast gene data without dimensionality reduction. The experimental results indicate that the algorithm is highly efficient and can effectively deal with missing values in gene dataset. © 2010 IEEE.

关键词： Cluster analysis

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A fast algorithm for finding community structure based on community closeness

A fast algorithm for finding community structure based on co...

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3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice

作者： Jiang, Xiufang Liu, Guiquan Lin, Zhiting Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9780769540306

Recently, the characterization of community structures in complex networks has received a considerable amount of attentions. Effective identification of these communities or clusters is a general problem in the field of data mining. In this paper we present a fast hierarchical agglomerative algorithm based on community closeness (FHACC) algorithm, for detecting community structure which is very efficient and faster than many other competing algorithms. FHACC tends to agglomerate such communities that share the most common vertices into larger ones. Its running time on a sparse network with n vertices and m edges is O(mk¯+mt), where k¯ denotes the mean vertex degree, and t is the iteration times of community agglomeration in FHACC algorithm. The algorithm was tested on several real-world networks and proved to be high efficient and effective in community finding. © 2010 IEEE.

关键词： Complex networks

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Stochastic prefix-based fault tolerant source routing algorithm for network on chip

Stochastic prefix-based fault tolerant source routing algori...

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2012 International Conference on Computer and Information Science, ICCIS 2012 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2012

作者： Xu, Gengchun Ren, Kaixin Gu, Naijie Anhui Province Key Laboratory of Computing and Communication Software Hefei China USTC-SICT Joint Laboratory on Network and Communication Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781467319386

In this paper, a novel fault-tolerant source routing algorithm for Network on Chip (NOC) called Stochastic Prefix-based Source Routing (SPSR) is proposed. Compared to non-source routing algorithms, it releases the intermediate node from complicated decision-making or routing table maintenance. Unlike the traditional source routing algorithms which use broadcast packets to record the YD of every intermediate node in Route Discovery, it predetermine prospective routes, which can greatly reduces the packets generated and forwarded in the Route Discovery phase, and therefore reduces the power consumption. The proposed routing algorithm is implemented on the open source simulator Nirgam [14], incorporating Orion [15] to estimate the power consumption. Experiment results show that the proposed algorithm can greatly reduce the power consumption in the route discovery and the latency of SPSR is comparable with that of the broadcasting mechanism. © 2012 IEEE.

关键词： Network-on-chip

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A hybrid particle swarm optimization for high-dimensional dynamic optimization 11th

A hybrid particle swarm optimization for high-dimensional dy...

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11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Luo, Wenjian Yang, Bin Bu, Chenyang Lin, Xin Anhui Province Key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China HefeiAnhui230027 China

ISBN: (纸本)9783319687582

High-Dimensional Dynamic Optimization Problems (HDDOPs) commonly exist in real-world applications. In evolutionary computation field, most of existing benchmark problems, which could simulate HDDOPs, are non-separable. Thus, we give a novel benchmark problem, called high-dimensional moving peaks benchmark to simulate separable, partially separable, and non-separable problems. Moreover, a hybrid Particle Swarm Optimization algorithm based on Grouping, Clustering and Memory strategies, i.e. GCM-PSO, is proposed to solve HDDOPs. In GCM-PSO, a differential grouping method is used to decompose a HDDOP into a number of sub-problems based on variable interactions firstly. Then each sub-problem is solved by a species-based particle swarm optimization, where the nearest better clustering is adopted as the clustering method. In addition, a memory strategy is also adopted in GCM-PSO. Experimental results show that GCM-PSO performs better than the compared algorithms in most cases. © Springer International Publishing AG 2017.

关键词： Particle swarm optimization (PSO)

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Multi-question negative surveys 3rd

Multi-question negative surveys

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3rd International Conference on Data Mining and Big Data, DMBD 2018 held in conjunction with the 9th International Conference on Swarm Intelligence, ICSI 2018

作者： Jiang, Hao Luo, Wenjiang Anhui Province Key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China HefeiAnhui230027 China

ISBN: (纸本)9783319938028

The negative survey is an emerging method of collecting sensitive information. It could obtain the distribution of sensitive information while preserving the personal privacy. When collecting sensitive information, several questions are often provided together to the respondents. However, when reconstructing positive survey results of multiple questions, previous reconstruction methods have some shortcomings. In this paper, we propose a new reconstruction method for multi-question negative surveys. Experimental results show that our method could obtain more reasonable results. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Surveys

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A novel approach for signal transduction networks simulation at a mesoscopic level

A novel approach for signal transduction networks simulation...

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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Shao, Chenxi Deng, Hongli MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei Anhui 230027 China Department of Computer Science and Technology University of Science and Technology of China Hefei Anhui 230027 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei Anhui 230027 China

ISBN: (纸本)1424415098

Signal transduction (ST) networks simulation is important to medical research. However, owing to the complexity of the networks, most methods presented for the simulation are not desirable. Here, based on multi-agent system (MAS), we proposed a novel approach characterized by a multi-level discrete space and mesoscopic molecule-agents for describing ST networks at a mesoscopic level. The space is developed for the representation of the cellular space;it provides a relatively sufficient spatial representation with less computational cost. The mesoscopic molecule-agents model the active components within the networks, which will offer new potentials to overcome the combinatorial complexity as well as make computation feasible. To validate this method, the simulations of a composition/decomposition system and the Mitogen-Activated Protein Kinase (MAPK) signal transduction pathway are implemented. Finally, an in-silico experiment with perturbation in the MAPK pathway is made. The results obtained from the simulations demonstrate the validity of the approach. ©2007 IEEE.

关键词： Molecules

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Cache locking optimization in java virtual machine

Cache locking optimization in java virtual machine

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2013 IEEE Conference Anthology, ANTHOLOGY 2013

作者： Lin, Chuanwen Gu, Naijie Cai, Songsong School of Computer Science and Technology University of Science and Technology of China Anhui Province Key Laboratory of Computing and Communication Software Hefei China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479916603

Cache plays an important role in multilevel storage system. It can greatly reduce the memory access latency. So the cache hit rate has a significant impact on the performance of the application. Many processors provide cache locking mechanism, which can lock the certain lines in cache. It enables an application to affect the cache replacement decisions under software control. This paper presents a novel method, which uses cache locking mechanism to reduce the run-time of java virtual machine (JVM). JVM often uses just-in-time compiler (JIT) to improve the performance. JIT compiles the method that has been invoked certain times, and then JVM executes the compiled method when invoking this method the next time. This paper analyzes the calling situation of the compiled method in JVM, and then proposes a heuristic approach to lock the compiled method in cache for an appropriate period. It can reduce cache miss rate when JVM executes the compiled method. The algorithm has been implemented in HotSpot based on Loongson-3A. Also, it can be implemented in other run-time systems. Experiment results show that the cache locking heuristic algorithm averagely reduces the cache miss rate by 8.5%, and improves the performance by 4% on the benchmark SPECjvm2008. © 2013 IEEE.

关键词： Application programs

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