FD-6: Image Information Mining - Methods and Applications for Exploration of Earth Observation data

Presented by: Mihai Datcu
German Aerospace Center (DLR), Oberpfaffenhofen, Germany

Recent advances in Earth Observation (EO) data acquisition technology are significantly increasing the volumes of data acquired. During the last decades satellite and airborne sensors are collecting and transmitting to receiving stations several terabytes of data every day. With the increasing spatial resolution not only the data volume is growing, but also the image information content is exploding. This became a challenge for the data exploitation and information dissemination methods: how to enlarge the usability of the millions of EO images acquired and stored in archives to a larger user community. In reality, EO data analysis is still performed in a very laborious way at the end of repeated cycles of trial and error. To overcome this, new methods of Image Information Mining (IIM) are proposed. They are based on human-centered concepts. New features and functions are being implemented allowing to improve content extraction, search on a semantic level, the availability of collected knowledge, interactive knowledge discovery, and new visual user interfaces. IIM is a new frame of methodologies and tools providing solutions for automating exploration and the extraction of information from EO archives that can lead to knowledge discovery and the creation of actionable intelligence (exploiting). IIM supports novel applications involving the analysis of complex contextual spatio-temporal relationships among image structures, also adapting for user needs. IIM is an interdisciplinary approach for automating EO data analyses that draws on signal/image analysis, pattern recognition, artificial intelligence, machine learning, information theory, databases, semantics, ontologies, and knowledge management.

This tutorial addresses EO and Geoinformation engineers and scientists who deal with the acquisition, processing, visualization, and analysis of spatial information. They play a dynamic role in modelling, understanding and forging our living space, at scales ranging from human activities to Earth scale. The goal of this tutorial is to promote a new methodology that did not exist until now and does not grow out of the current competencies. The new methodology is based on a novel class of advanced computer engineering and information technologies, associated with overall man - machine system intelligence.

The tutorial gives the basics of the IIM methods, beginning with a presentation of the EO sensor models and an evaluation of their quantitative and qualitative information content. The most important categories of optical and Synthetic Aperture Radar (SAR) sensors are treated. An overview of the fields of Data Mining, Knowledge and Data Discovery, and image search engines, is intended to position IIM field for EO applications. The basics of EO image content extraction, as specific methods for image cues and features estimation are presented in relation with the specificity of the IIM methodology for geostatistics and spatial statistics, i.e. geometrical and topological information representation. EO image time series and spatio-temporal information descriptors are introduced as a base for long term Earth processes understanding and full exploitation of historical EO data archives. The kernel problematic of IIM is the discovery of information, thus similarity measures and grouping methods are presented and analyzed. A special attention is given to a new way to analyze the data massive based on data compression methods. The field is related to Kolmogorov theory of complexity and emerges in methods for Pattern recognition. The learning and unlearning techniques for the Man-Machine Communication are introduced and evaluated. The over all IIM system architecture including DBMS and HPC issues is presented. The use of IIM for the generation of semantic catalogues for large EO archives, for KDD and detailed and fast analysis of EO images are demonstrated and discussed. The demonstrations are of actual scenarios using TerraSAR-X, TanDEM-X, and optical images, such as Landsat, WorldView, etc.

Mihai Datcu received the M.S. and Ph.D. degrees in Electronics and Telecommunications from the University "Politechnica" of Bucharest UPB, Romania, in 1978 and 1986. In 1999 he received the title "Habilitation à diriger des recherches" from Université Louis Pasteur, Strasbourg, France. He holds a professorship in electronics and telecommunications with UPB since 1981. Since 1993 he is scientist with the German Aerospace Center (DLR), Oberpfaffenhofen. He is developing algorithms for model based information retrieval from high complexity signals and methods for scene understanding from synthetic aperture radar (SAR) and interferometric SAR data. He is engaged in research related to information theoretical aspects and semantic representations in advanced communication systems. Currently he is Senior Scientist and Image Analysis research group leader with the Remote Sensing Technology Institute (IMF) of DLR, Oberpfaffenhofen, coordinator of the CNES-DLR-ENST Competence Centre on Information Extraction and Image Understanding for Earth Observation, and professor at Paris Institute of Technology/GET Telecom Paris. His interests are in Bayesian inference, information and complexity theory, stochastic processes, model-based scene understanding, image information mining, for applications in information retrieval and understanding of high resolution SAR and optical observations. He has held visiting professor appointments from 1991 to 1992 with the Department of Mathematics of the University of Oviedo, Spain, from 2000 to 2002 with the Université Louis Pasteur, and the International Space University, both in Strasbourg, France. In 1994 was guest scientist with the Swiss Center for Scientific Computing (CSCS), Manno, Switzerland and in 2003 he was visiting professor with the University of Siegen, Germany.

From 1992 to 2002 he had a longer invited professor assignment with the Swiss Federal Institute of Technology ETH Zurich. He is involved in advanced research programs for information extraction, data mining and knowledge discovery and data understanding with the European Space Agency (ESA), Centre National d'Etudes Spatiales (CNES), NASA, and in a variety of European projects. He is member of the European Image Information Mining Coordination Group (IIMCG).