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| 1 | 遥感图像处理中的深度学习专题简介显示文摘深度学习是一种非常适用于大数据应用的新兴技术.在对地观测领域,由大量在轨卫星获取的海量遥感数据,使其成为数据驱动应用的典范.过去几年来,遥感图像处理相关的深度学习研究快速增长,包括高光谱遥感图像、合成孔径雷达(SAR)图像等处理、分类、参数反演及目标检测识别.除了遥感数据的高分辨率、高维度和大尺寸之外,该领域还存在一些特殊的挑战,如不同传感器及其不同工作模式的复杂性和特殊性,隐含在遥感数据中的独特物理属性,信息反演的物理原理等. | 徐丰 胡程 李军 Antonio PLAZA Mihai DATCU | 2020 | 中国科学:信息科学2020，50，4： | 4 |
| 2 | Super-resolution of geosynchronous synthetic aperture radar images using dialectical GANs显示文摘Dear editor,The concept of geosynchronous synthetic aperture radar (GEO SAR) system was conceived in an effort to realize a quick observation of emergency disasters (e.g., landslides and earthquakes)[1].The novel GEO SAR system has the significant advantages of short-revisit time and large coverage and facilitates the nearly continuous observation of target regions, unlike the currently operatinglow earth orbit (LEO) SARs [2]. | Yuanhao LI Dongyang AO Corneliu Octavian DUMITRU Cheng HU Mihai DATCU | 2019 | Science China(Information Sciences)2019，62，10： | 3 |
| 3 | Special focus on deep learning in remote sensing image processing显示文摘As a newly emerging technology,deep learning is a very promising field in big data applications.Remote sensing applications often involve huge volume data obtained daily by numerous in-orbit satellites.This makes it a perfect area for data-driven applications.Over the past years,there has been an exponentially increasing interest in deep learning for remote sensing image processing,including not only optical imagery but also synthetic aperture radar(SAR)imagery.In addition to the rapidly growing size and spectral,spatial and temporal resolution of remote sensing data,there are other challenges that are unique in this area,e.g.the intrinsic complexity and particularity of each specific sensor and their multi-modality,the fundamental physical properties embedded in the data,the underlying principles for information retrieval,etc.To promote the research in this area,we have organized a special focus feature on deep learning in remote sensing image processing in the SCIENCE CHINA Information Sciences. | Feng XU Cheng HU Jun LI Antonio PLAZA Mihai DATCU | 2020 | Science China(Information Sciences)2020，63，4： | 2 |
| 4 | Information Mining in Remote Sensing Image Archives: System Concepts 显示文摘 | Mihai Datcu Herbert Daschiel Perlizzari etc | 2003 | IEEE Transactions on Image Processing2003，41，： | 1 |
| 5 | Huber–Markov Model for Complex SAR Image Restoration显示文摘 | Matteo Soccorsi Du an Gleich Mihai Datcu | 2010 | IEEE Geoscience and RemoteSensing Letters2010，7，1： | 1 |
| 6 | Information mining in remote sensing image archives:system concepts显示文摘 | Mihai Datcu Herbert Daschiel Andrea Pelizzari | 2003 | IEEE Transactions on Geoscience and Remote Sensing2003，41，： | 1 |
| 7 | Human-centered concepts for exploration and understanding of earth observation images显示文摘 | Mihai Datcu Klaus Seidel | 2005 | IEEE Transactions on Geoscience and Remote Sensing2005，43，： | 1 |
| 8 | Model Fitting and Model Evidence for Multiscale Image Texture Analysis显示文摘 | Datcu Mihai Stoichescu Dan Alexandru Seidel Klaus Iorga Cristian | 2004 | AIP Conference Proceedings2004，735，1： | 1 |
| 9 | Gauss - Markov modelfor wavelet-based SAR image despeckling 显示文摘 | DUSAN Gleich MIHAI Datcu | 2006 | IEEESignal Processing Letters2006，，6： | 1 |
| 10 | Image time series data mining based on the information bottleneck principle显示文摘 | GUEGUEN Lionel DATCU Mihai | 2007 | IEEE Transactions on Geoscience and Remote Sensin2007，45，4： | 1 |
| 11 | Information processing for unmanned aerial vehicles(UAVs)in surveying,mapping,and navigation显示文摘Unmanned Aerial Vehicles(UAVs)have been involved in a wide range of remote sensing applications.In particular,recent developments in robotics,computer vision,and geomatics technologies have made it possible to capture a huge amount of visual data with low-cost UAVs.As a kind of rapid,flexible and low-cost data acquisition system,UAVs have shown great potential to perform numerous surveying,mapping. | Gui-Song Xia Mihai Datcu Wen Yang Xiang Bai | 2018 | Geo-Spatial Information Science2018，21，1： | 0 |
| 12 | 可解释的、物理感知的、可信的人工智能合成孔径雷达的范式转换显示文摘识别或理解合成孔径雷达(SAR)系统观测到的场景需要超出空间背景的更广泛线索。这些包含但不仅限于成像几何、成像模式、图像的傅立叶谱性质或极化特征的行为。在本文中,我们提出以SAR数据为例,将数据科学的可解释性范式转变为SAR的地面可解释人工智能(XAI)。这旨在使用基于完善模型的可解释数据转换,为AI方法生成输入,为训练过程提供知识反馈,并从数据中学习或改进高复杂度的未知或非形式化模型。 | Mihai Datcu Zhongling Huang Andrei Anghel 屈冰洋(译) | 2023 | 电子工程信息2023，，4： | 0 |
| 13 | Understanding satellite images:a data mining module for Sentinel images显示文摘The increased number of free and open Sentinel satellite images has led to new applications of these data.Among them is the systematic classification of land cover/use types based on patterns of settlements or agriculture recorded by these images,in particular,the identification and quantification of their temporal changes.In this paper,we will present guidelines and practical examples of how to obtain rapid and reliable image patch labelling results and their validation based on data mining techniques for detecting these temporal changes,and presenting these as classification maps and/or statistical analytics.This represents a new systematic validation approach for semantic image content verification.We will focus on a number of different scenarios proposed by the user community using Sentinel data.From a large number of potential use cases,we selected three main cases,namely forest monitoring,flood monitoring,and macro-economics/urban monitoring. | Corneliu Octavian Dumitru Gottfried Schwarz Anna Pulak-Siwiec Bartosz Kulawik Mohanad Albughdadi Jose Lorenzo Mihai Datcu | 2020 | Big Earth Data2020，4，4： | 0 |
| 14 | The digital Earth Observation Librarian:a data mining approach for large satellite images archives显示文摘Throughout the years,various Earth Observation(EO)satellites have generated huge amounts of data.The extraction of latent information in the data repositories is not a trivial task.New methodologies and tools,being capable of handling the size,complexity and variety of data,are required.Data scientists require support for the data manipulation,labeling and information extraction processes.This paper presents our Earth Observation Image Librarian(EOLib),a modular software framework which offers innovative image data mining capabilities for TerraSAR-X and EO image data,in general.The main goal of EOLib is to reduce the time needed to bring information to end-users from Payload Ground Segments(PGS).EOLib is composed of several modules which offer functionalities such as data ingestion,feature extraction from SAR(Synthetic Aperture Radar)data,meta-data extraction,semantic definition of the image content through machine learning and data mining methods,advanced querying of the image archives based on content,meta-data and semantic categories,as well as 3-D visualization of the processed images.EOLib is operated by DLR’s(German Aerospace Center’s)Multi-Mission Payload Ground Segment of its Remote Sensing Data Center at Oberpfaffenhofen,Germany. | Mihai Datcu Alexandru-Cosmin Grivei Daniela Espinoza-Molina Corneliu Octavian Dumitru Christoph Reck Vlad Manilici Gottfried Schwarz | 2020 | Big Earth Data2020，4，3： | 0 |
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