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1. Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest

Author

Manuel Campos-Taberner, Adriana Romero-Soriano, Carlo Gatta, Gustau Camps-Valls, Adrien Lagrange, Bertrand Le Saux, Anne Beaupere, Alexandre Boulch, Adrien Chan-Hon-Tong, Stephane Herbin, Hicham Randrianarivo, Marin Ferecatu, Michal Shimoni, Gabriele Moser, and Devis Tuia

Subjects
Deep neural networks, extremely high spatial resolution, image analysis and data fusion (IADF), landcover classification, LiDAR, multiresolution-, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

In this paper, we discuss the scientific outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high-resolution RGB images and a three-dimensional (3-D) LiDAR point cloud. The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this paper, we discuss the scientific results obtained by the winners of the 2-D contest, which studied either the complementarity of RGB and LiDAR with deep neural networks (winning team) or provided a comprehensive benchmarking evaluation of new classification strategies for extremely high-resolution multimodal data (runner-up team). The data and the previously undisclosed ground truth will remain available for the community and can be obtained at http://www.grss-ieee.org/community/technical-committees/data-fusion/2015-ieee-grss-data-fusion-contest/. The 3-D part of the contest is discussed in the Part-B paper [1].

Published
2016
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2. Deep learning for urban remote sensing

Author

Hicham Randrianarivo, Renaud Marlet, Nicolas Audebert, Alexandre Boulch, Marin Ferecatu, Sébastien Lefèvre, Bertrand Le Saux, Environment observation with complex imagery (OBELIX), Université de Bretagne Sud (UBS)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), ONERA - The French Aerospace Lab [Palaiseau], ONERA-Université Paris Saclay (COmUE), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Laboratoire d'Informatique Gaspard-Monge (LIGM), Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS), imagine [Marne-la-Vallée], Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM)-Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-CentraleSupélec, ONERA, Laboratoire d'Informatique Gaspard-Monge (ligm), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
Earth observation, Computer science, Feature extraction, 0211 other engineering and technologies, Context (language use), 02 engineering and technology, Hough transform, law.invention, Labeling, law, 0202 electrical engineering, electronic engineering, information engineering, Training, Computer vision, 021101 geological & geomatics engineering, Remote sensing, Support vector machines, Contextual image classification, business.industry, Hough transforms, Deep learning, 020207 software engineering, Semantics, Support vector machine, Semantic mapping, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Three-dimensional displays, learning (artificial intelligence), Artificial intelligence, business, image classification
Abstract

International audience; This work shows how deep learning techniques can benefit to remote sensing. We focus on tasks which are recurrent in Earth Observation data analysis. For classification and semantic mapping of aerial images, we present various deep network architectures and show that context information and dense labeling allow to reach better performances. For estimation of normals in point clouds, combining Hough transform with convolutional networks also improves the accuracy of previous frameworks by detecting hard configurations like corners. It shows that deep learning allows to revisit remote sensing and offers promising paths for urban modeling and monitoring.

Published
2017
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3. Scalable action localization with kernel-space hashing

Author

Jenny Benois-Pineau, Marin Ferecatu, Michel Crucianu, Andrei Stoian, CEDRIC. Données complexes, apprentissage et représentations (CEDRIC - VERTIGO), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Laboratoire Bordelais de Recherche en Informatique (LaBRI), and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)

Subjects
Sublinear function, business.industry, hyperplane queries, Hash function, Detector, kernel indexing, [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], Pattern recognition, 0102 computer and information sciences, 02 engineering and technology, 01 natural sciences, [SPI]Engineering Sciences [physics], Kernel (image processing), 010201 computation theory & mathematics, 020204 information systems, Scalability, 0202 electrical engineering, electronic engineering, information engineering, query-by-detector, [INFO]Computer Science [cs], Artificial intelligence, LSH, business, Action localization, Mathematics, Linear search
Abstract

International audience; To detect and locate complex human actions in video, one trains a detector for each target class and applies it to the video content. This approach can scale to large video databases if the application of the detector can be made sublinear in the size of the database. Sublinear retrieval methods have been successfully explored for query-by-example but few were devised for these more challenging queries by detector. We put forward here a novel approximate search method that relies on LSH to support query-by-detector. We evaluate our method on a recent large action localization dataset and show it has significantly better efficiency than linear search.

Published
2015
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4. Local integrity constraints for structure detection and segmentation in high-resolution earth observation images

Author

Marin Ferecatu and Pierre Blanchart

Subjects
Earth observation, Computer science, Segmentation-based object categorization, business.industry, Scale-space segmentation, Pattern recognition, Image segmentation, Image texture, Minimum spanning tree-based segmentation, Region growing, Data integrity, Segmentation, Computer vision, Artificial intelligence, business
Abstract

Considering the idea that objects in images have a higher local structural integrity than the background they lie into, we propose a method that learns a supervised distance characterizing the membership of a pair of elements to the target structure. We test our ideas by applying them to the task of extracting semantic structures in high resolution Earth Observation images. The results show that the method works well in many situations when there is no training dataset. The limits of the method are also discussed.1

Published
2015
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5. Benchmarking classification of earth-observation data: From learning explicit features to convolutional networks

Author

Anne Beaupere, Adrien Lagrange, Alexandre Boulch, Adrien Chan-Hon-Tong, Marin Ferecatu, Hicham Randrianarivo, Bertrand Le Saux, and Stéphane Herbin

Subjects
Earth observation, Artificial neural network, Computer science, business.industry, Deep learning, Feature extraction, Pattern recognition, Machine learning, computer.software_genre, Support vector machine, Benchmark (computing), Artificial intelligence, business, Transfer of learning, computer
Abstract

In this paper, we address the task of semantic labeling of multisource earth-observation (EO) data. Precisely, we benchmark several concurrent methods of the last 15 years, from expert classifiers, spectral support-vector classification and high-level features to deep neural networks. We establish that (1) combining multisensor features is essential for retrieving some specific classes, (2) in the image domain, deep convolutional networks obtain significantly better overall performances and (3) transfer of learning from large generic-purpose image sets is highly effective to build EO data classifiers.

Published
2015
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6. Urban structure detection with deformable part-based models

Author

Hicham Randrianarivo, Marin Ferecatu, Bertrand Le Saux, Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), ONERA - The French Aerospace Lab [Palaiseau], ONERA-Université Paris Saclay (COmUE), and CEDRIC, Laboratoire

Subjects
analyse d'images, Computer science, reconnaissance d'objets, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, Context (language use), 02 engineering and technology, [INFO] Computer Science [cs], la télédétection, object recognition, Field (computer science), remote sensing, image analysis, very high resolution, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], [INFO]Computer Science [cs], Computer vision, très haute résolution, Image resolution, 021101 geological & geomatics engineering, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Pixel, business.industry, modèles déformables axée sur la partie, détection d'objet, Cognitive neuroscience of visual object recognition, object detection, Object (computer science), Object detection, Moment (mathematics), deformable part-based models, 020201 artificial intelligence & image processing, Artificial intelligence, business
Abstract

In this paper we apply the deformable part model by Felzenszwalb et al., which is at this moment the state of the art in many computer vision related tasks, to detect different types of man made structures in very high resolution aerial images - a reputedly difficult problem in our field. We test the framework on a database of crops of aerial images at a definition of 10 cm/pixel and investigate how the model performs on several classes of objects. The results show that the model can achieve reasonable performance in this context. However, depending on the type of object, there are specific issues which will have to be taken into account to build an effective semi-supervised annotation tool based on this model.

Published
2013
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7. Mining large satellite image repositories using semi-supervised methods

Author

Mihai Datcu, Marin Ferecatu, Pierre Blanchart, and CEDRIC, Laboratoire

Subjects
Earth observation, Active learning, Apprentissage statistique, Cascade of classifiers, Object detection, Computer science, Relevance Feedback, L'intérêt pour vos commentaires, Relevance feedback, Image Databases, [INFO] Computer Science [cs], computer.software_genre, Data modeling, Earth Observation Mining, Statistical Learning, Search engine, Component (UML), [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], Image retrieval, Information retrieval, Semi-supervised methods, Auto-annotation, Satellite imagery, Bases de données image, Support vector machine, Feature (computer vision), Content-based image retrieval (CBIR) systems, Earth Observation minier, Data mining, computer
Abstract

The increasing number and resolution of earth observation (EO) imaging sensors has had a significant impact on both the acquired image data volume and the information content in images. There is consequently a strong need for highly efficient search tools for EO image databases and for search methods to automatically identify and recognize structures within EO images. In this paper, we present a concept for an earth observation image data mining system mixing an auto-annotation component with a category search engine which combines a generic image class search and an object detection feature. The proposed concept relies thus on three distinct components which are detailed successively: in the first part, we describe the auto-annotation component, in the second part, the generic category search engine and in the third part, the object detection tool. In the concluding part of the paper, we provide an insight into how these three components can be related to each other and used in a complementary way to arrive at a system which combines the advantages of both the auto-annotation systems and the category search engines.

Published
2011
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8. Multi-view object matching and tracking using canonical correlation analysis

Author

Hichem Sahbi, Marin Ferecatu, Centre d'études et de recherche en informatique et communications (CEDRIC), and Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)

Subjects
Object detection, Computer science, Multi-view tracking, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, 03 medical and health sciences, 0302 clinical medicine, Analyse des corrélations canoniques, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Computer vision, Suivi des vue multiples, Image sensor, Invariant (mathematics), [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, Pattern recognition, Kernel (image processing), Canonical Correlation Analysis, Video tracking, 020201 artificial intelligence & image processing, Détection d'objet, Artificial intelligence, business, Canonical correlation, 030217 neurology & neurosurgery
Abstract

Multi-view tracking of objects in video surveillance consists in segmenting and automatically following them through different camera views. This may be achieved using geometric methods, e.g. by calibrating camera sensors and using their transformation matrices. However, in practice the precision of calibration is a major issue when trying to achieve this task robustly. In this paper, we present an alternative framework for multi-view object matching and tracking based on canonical correlation analysis. Our method is purely statistical and encodes intrinsic object appearances while being view-point invariant. We will show that our technique is (i) easy-to-set (ii) theoretically well grounded and (iii) provides robust matching and tracking results for traffic surveillance.

Published
2009
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9. A new semantic text-image search engine for car designers

Author

Fabienne Gandon, Marin Ferecatu, C. Bouchard, G. Logerot, Arantza Bereciartua, H. Houissa, and J.-F. Omhover

Subjects
Multimedia, Product design, business.industry, Computer science, Search engine indexing, Automotive industry, computer.software_genre, Original equipment manufacturer, Consumer-to-business, User interface, business, computer, Image retrieval, Content management
Abstract

The Trends Research ENabler for Design Specifications (TRENDS) system integrates flexible content-based image retrieval facilities with database management and other useful functionalities that aim at improving the inspirational information gathering process in the early design stages. TRENDS is a 6th Framework Programme IST project, funded by the EC that started 01/01/2006 and will end 31/12/2008. It aims at elaborating the design trend boards dedicated to product designers in business to consumer markets such as for the automotive and original equipment manufacturers. The main innovation is related to the content based image and semantic text information search engines and to the integration of the elements under a cutting edge user interface specially designed to fulfill the designerspsila requirements.

Published
2008
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10. Interactive Search for Image Categories by Mental Matching

Author

Donald Geman and Marin Ferecatu

Subjects
Matching (statistics), Information retrieval, Computer science, Similarity (psychology), Process (computing), Key (cryptography), Relevance feedback, Image retrieval, Zero (linguistics), Image (mathematics)
Abstract

Traditional image retrieval methods require a "query image" to initiate a search for members of an image category. However, when the image database is unstructured, and when the category is semantic and resides only in the mind of the user, there is no obvious way to begin (the "page zero " problem). We propose a new mathematical framework for relevance feedback based on mental matching and starting from a random sample of images. At each iteration the user declares which of several displayed images is closest to his category; performance is measured by the number of iterations necessary to display an instance. Our core contribution is a Bayesian formulation which scales to large databases with no semantic annotation. The two key components are a response model which accounts for the user's subjective perception of similarity and a display algorithm which seeks to maximize the flow of information. Experiments with real users and a database with 20,000 images demonstrate the efficiency of the search process.

Published
2007
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11. Improving performance of interactive categorization of images using relevance feedback

Author

Michel Crucianu, Nozha Boujemaa, and Marin Ferecatu

Subjects
business.industry, Computer science, Active learning (machine learning), Relevance feedback, Machine learning, computer.software_genre, Relevance vector machine, Support vector machine, Redundancy (information theory), Categorization, Artificial intelligence, business, Selection criterion, Image retrieval, computer
Abstract

When using relevance feedback for the interactive categorization of images, the strategy employed by the system to select images to be presented to the user is of paramount importance for overall performance. Using SVM-based relevance feedback, we present a new selection criterion, based on the active learning principle, that minimizes redundancy between the candidate images shown to the user at every round. We also emphasize the fact that insensitivity to the scale of the target classes in the description space is an important quality of the learner in the interactive categorization context and we propose specific kernel functions to achieve this. Experimental results on several image databases confirm the attractiveness of our suggestions.

Published
2005
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