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A survey on hyperspectral image restoration: from the view of low-rank tensor approximation

Science China(Information Sciences) 2023年第4期66卷 33-63页

作者： Na LIU Wei LI Yinjian WANG Ran TAO Qian DU jocelyn chanussot School of Information and Electronics and Beijing Key Laboratory of Fractional Signals and Systems Beijing Institute of Technology Department of Electrical and Computer Engineering Mississippi State University GIPSA-Lab University of Grenoble Alpes Aerospace Information Research Institute Chinese Academy of Sciences

The ability to capture fine spectral discriminative information enables hyperspectral images(HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the captured HSIs may not represent the true distribution of ground objects and the received reflectance at imaging instruments may be degraded, owing to environmental disturbances, atmospheric effects, and sensors’ hardware limitations. These degradations include but are not limited to complex noise, heavy stripes, deadlines,cloud/shadow occlusion, blurring and spatial-resolution degradation, etc. These degradations dramatically reduce the quality and usefulness of HSIs. Low-rank tensor approximation(LRTA) is such an emerging technique, having gained much attention in the HSI restoration community, with an ever-growing theoretical foundation and pivotal technological innovation. Compared to low-rank matrix approximation(LRMA),LRTA characterizes more complex intrinsic structures of high-order data and owns more efficient learning abilities, being established to address convex and non-convex inverse optimization problems induced by HSI restoration. This survey mainly attempts to present a sophisticated, cutting-edge, and comprehensive technical survey of LRTA toward HSI restoration, specifically focusing on the following six topics: denoising, fusion,destriping, inpainting, deblurring, and super-resolution. For each topic, state-of-the-art restoration methods are introduced, with quantitative and visual performance assessments. Open issues and challenges are also presented, including model formulation, algorithm design, prior exploration, and application concerning the interpretation requirements.

关键词： hyperspectral image image restoration low-rank tensor approximation multisource fusion remote sensing

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Information extraction from remote sensing imagery

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Geo-Spatial Information Science 2017年第4期20卷 297-298页

作者： Xin Huang Jiayi Li Wenzhi Liao jocelyn chanussot School of Remote Sensing and Information Engineering Wuhan University China TELIN-IPI-IMEC Ghent University Belgium Grenoble Institute of Technology France

Various platforms,such as satellite,aircraft,ground-based,some emerging aspects (***) have resulted in a dramatic improvement in the capabilities of earth observations (EO).The numerous remote sensing data promote an enhanced possibility to assess,monitor,and predict the dynamics of land-covers,anthropologic processes,and influence to the ***,the properties of the data acquired by such diverse sources pose challenges to the processing methodologies,and hence,development of a series of new methods for the analysis of remote sensing images is required,The aim of this special issue of Geospatial Information Science is to develop new ideas and technologies to facilitate the utility of remote sensing data and to further explore its potential in various applications.

关键词： Information retrieval

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Multimodal hyperspectral remote sensing: an overview and perspective

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Science China(Information Sciences) 2021年第2期64卷 115-138页

作者： Yanfeng GU Tianzhu LIU Guoming GAO Guangbo REN Yi MA jocelyn chanussot Xiuping JIA School of Electronics and Information Engineering Harbin Institute of Technology Heilongjiang Province Key Laboratory of Space-Air-Ground Integrated Intelligent Remote Sensing First Institute of Oceangraphy Ministry of Natural Resources GIPSA-Lab Grenoble Institute of Technology School of Engineering and Information Technology University of New South Wales

Since the advent of hyperspectral remote sensing in the 1980 s, it has made important achievements in aerospace and aviation field and been applied in many fields. Conventional hyperspectral imaging spectrometer extends the number of spectral bands to dozens or hundreds, and provides spatial distribution of the reflected solar radiation from the scene of observation at the same time. Nowadays, with the fast development of new technology in the fields of information and photoelectricity sensing, and the popularity of unmanned aerial vehicle, hyperspectral remote sensing imaging presents the new trends of multimodality and acquires integration information while keeping high or very-high spectral resolution, especially, high temporal even real time sensing and stereo sensing. Therefore, three important modes of hyperspectral imaging come into existence:(1) multitemporal hyperspectral imaging, which refers to the observation of same region at different dates;(2) hyperspectral video imaging, which captures full frame spectral images in real-time;(3) hyperspectral stereo imaging, which obtains the full dimension information(including 2D image, elevation, and spectra) of observed scene. Along this perspective, firstly, the current researches on hyperspectral remote sensing and image processing are briefly reviewed, and then, comprehensive descriptions of the aforementioned three main hyperspectral imaging modes are carried out from the following four aspects: fundamental principle of new mode of hyperspectral imaging, corresponding scientific data acquisition, data processing and application, and potential challenges in data representation, feature learning and interpretation. Through the analysis of development trend of hyperspectral imaging and current research situation, we hope to provide a direction for future research on multimodal hyperspectral remote sensing.

关键词： hyperspectral image processing multitemporal hyperspectral imaging hyperspectral video imaging hyperspectral stereo imaging multimodal hyperspectral remote sensing imaging

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Interpretable foundation models as decryptors peering into the Earth system

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The Innovation 2024年第5期5卷 21-22页

作者： Chenyu Li Danfeng Hong Bing Zhang Tianjun Liao Naoto Yokoya Pedram Ghamisi Min Chen Lizhe Wang Jon Atli Benediktsson jocelyn chanussot Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Electronic Electricaland Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049China Academy of Military Sciences Beijing 100000China Graduate School of Frontier Sciences The University of TokyoChiba 277-8561Japan Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology09599 FreibergGermany Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China School of Computer Science China University of GeosciencesWuhan 430078China Faculty of Electrical and Computer Engineering University of Iceland102 ReykjavikIceland University Grenoble Alpes InriaCNRSGrenoble INPLJK38000 GrenobleFrance

THE EARTH SYSTEM AMID THE BIG DATA *** processes of the Earth system drive interactions between energy,matter,and life,and a comprehensive understanding of their full evolutionary trajectory is critical for sustainable human *** modeling primarily relies on a set of theoretical equations to simulate dynamic process such as the carbon-nitrogen cycle,solar radiation dynamics,and terrestrial ecosystem dynamics.1 Despite the extensive modeling experience of Earth scientists,the rapid advancement of Earth observation techniques has led to a significant increase in the volume of databases,with data accumulating daily or even *** has exacerbated the conflict between the capacity for data collection and utilization for big Earth ***,there is an urgent need to enhance the intelligent processing and analysis of big Earth data.

关键词： dynamics. system foundation

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza jocelyn chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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Study of the behaviour of the dielectric constant in Cu,Fe:BaTiO_3

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2008年第3期9卷 429-434页

作者： Alioune OUEDRAOGO Kalifa PALM Issaka OUEDRAOGO Guy chanussot Laboratory of Applied Organic Chemistry and Physics UFR/SEA University of Ouagadougou BP 7021 Ouagadougou Burkina Faso Laboratory of Dielectrics University of Dijon 21000 Dijon France)

In this work we study the behaviour of the dielectric constant of BaTiO3 single crystals doped with Cu and Fe for different ion percentages, particularly, the influence of these hetemvalent substitutions on the ferroelectric-paraelectric phase transition whose temperature is found at Tc=120℃ for pure samples. The dielectric constant e in terms of temperature shows that the Curie temperature decreases when the quantity of impurities increases and presents a broadening and flattering of the maximum oft（T） within higher values, with the transition becoming more and more diffuse. It is interesting to have a material with very high permittivity （high-k） because of its capacity to store an important quantity of electric charges. The t anisotropy and the Curie-Weiss law are also verified with a good ratio between the slopes ofε^-l（T） from both sides of the transition, leading to a Curie constant： C= 13 × 10^4 K for BaTiO3：1.6%Fe in the polar phase. BaTiO3 is a displacive ferroelectric going through a first-order phase transition. The substitutions have an effect on the dynamics of the perovskite lattice. They induce charges transfer to Ti and a diminution of elastic forces in BaTiO3. Then we discuss the transition but on the nature of the material. fact that the maximum of permittivity does not depend on the phase

关键词： Phase transition Ferroelectric BaTiO3 Remeika single crystals Dielectric constant Curie constant

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