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Combining deep learning and mathematical morphology for historical map segmentation / Yizi Chen (2021)  

Public contenu dans ArXiv / Université Cornell (Etats-Unis) (1991)   Titre : Combining deep learning and mathematical morphology for historical map segmentation Type de document : Article/Communication Auteurs : Yizi Chen, Auteur ; Edwin Carlinet, Auteur ; Joseph Chazalon, Auteur ; Clément Mallet , Auteur ; Bertrand Duménieu , Auteur ; Julien Perret , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2021 Projets : SODUCO / Perret, Julien Note générale : bibliographie soumis à DGMM 2021 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] carte ancienne [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] données maillées [Termes descripteurs IGN] morphologie mathématique [Termes descripteurs IGN] vectorisation Résumé : (auteur) The digitization of historical maps enables the study of ancient, fragile, unique, and hardly accessible information sources. Main map features can be retrieved and tracked through the time for subsequent thematic analysis. The goal of this work is the vectorization step, i.e., the extraction of vector shapes of the objects of interest from raster images of maps. We are particularly interested in closed shape detection such as buildings, building blocks, gardens, rivers, etc. in order to monitor their temporal evolution. Historical map images present significant pattern recognition challenges. The extraction of closed shapes by using traditional Mathematical Morphology (MM) is highly challenging due to the overlapping of multiple map features and texts. Moreover, state-of-the-art Convolutional Neural Networks (CNN) are perfectly designed for content image filtering but provide no guarantee about closed shape detection. Also, the lack of textural and color information of historical maps makes it hard for CNN to detect shapes that are represented by only their boundaries. Our contribution is a pipeline that combines the strengths of CNN (efficient edge detection and filtering) and MM (guaranteed extraction of closed shapes) in order to achieve such a task. The evaluation of our approach on a public dataset shows its effectiveness for extracting the closed boundaries of objects in historical maps. Numéro de notice : P2021-001 Affiliation des auteurs : LaSTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Preprint nature-HAL : Préprint En ligne : https://arxiv.org/abs/2101.02144 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96739

Crater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis / David Solarna in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6039 - 6058 Titre : Crater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis Type de document : Article/Communication Auteurs : David Solarna, Auteur ; Alberto Gotelli, Auteur ; Jacqueline Le Moigne, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6039 - 6058 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] cratère [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] distance de Hausdorff [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] Mars (planète) [Termes descripteurs IGN] ondelette [Termes descripteurs IGN] processus ponctuel marqué [Termes descripteurs IGN] séparateur à vaste marge [Termes descripteurs IGN] transformation de Hough [Termes descripteurs IGN] zone d'intérêt Résumé : (auteur) Because of the large variety of planetary sensors and spacecraft already collecting data and with many new and improved sensors being planned for future missions, planetary science needs to integrate numerous multimodal image sources, and, as a consequence, accurate and robust registration algorithms are required. In this article, we develop a new framework for crater detection based on marked point processes (MPPs) that can be used for planetary image registration. MPPs were found to be effective for various object detection tasks in Earth observation, and a new MPP model is proposed here for detecting craters in planetary data. The resulting spatial features are exploited for registration, together with fitness functions based on the MPP energy, on the mean directed Hausdorff distance, and on the mutual information. Two different methods—one based on birth–death processes and region-of-interest analysis and the other based on graph cuts and decimated wavelets—are developed within the proposed framework. Experiments with a large set of images, including 13 thermal infrared and visible images of the Mars surface, 20 semisimulated multitemporal pairs of images of the Mars surface, and a real multitemporal image pair of the Lunar surface, demonstrate the effectiveness of the proposed framework in terms of crater detection performance as well as for subpixel registration accuracy. Numéro de notice : A2020-526 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2970908 date de publication en ligne : 18/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2970908 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95704 [article]

Generating urban morphologies at large scales / Juste Raimbault (2019)    

Public Titre : Generating urban morphologies at large scales Type de document : Article/Communication Auteurs : Juste Raimbault, Auteur ; Julien Perret , Auteur Editeur : Cambridge [Massachusetts - Etats-Unis] : MIT Press Année de publication : 2019 Projets : DynamiCity / Conférence : Alife 2019, Conference on Artificial life 29/07/2019 02/08/2019 Newcastle Royaume-Uni Open Access Proceedings Importance : 8 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] Europe (géographie politique) [Termes descripteurs IGN] grande échelle [Termes descripteurs IGN] indicateur [Termes descripteurs IGN] modèle numérique [Termes descripteurs IGN] modélisation de processus [Termes descripteurs IGN] morphologie mathématique [Termes descripteurs IGN] morphologie urbaine [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] reconnaissance de formes [Termes descripteurs IGN] zone urbaine Résumé : (Auteur) At large scales, typologies of urban form and corresponding generating processes remain an open question with important implications regarding urban planning policies and sustainability. We propose in this paper to generate urban configurations at large scales, typically of districts, with morphogene-sis models, and compare these to real configurations according to morphological indicators. Real values are computed on a large sample of districts taken in European urban areas. We calibrate each model and show their complementarity to approach the variety of real urban configurations, paving the way to multi-model approaches of urban morphogenesis. Numéro de notice : C2019-013 Affiliation des auteurs : LaSTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans En ligne : https://halshs.archives-ouvertes.fr/halshs-02265415 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93262

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Machine learning for image segmentation / Kaiwen Chang (2019)  

Public contenu dans HAL Hyper articles en ligne / Centre pour la Communication Scientifique Directe CCSD (2000)   Titre : Machine learning for image segmentation Titre original : Apprentissage artificiel pour la segmentation d'image Type de document : Thèse/HDR Auteurs : Kaiwen Chang, Auteur ; Jesus Angulo lopez, Directeur de thèse ; Jesus Angulo lopez, Directeur de thèse ; Bruno Figliuzzi, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2019 Importance : 155 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l'Université PSL, Spécialité : Morphologie Mathématique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme de superpixels [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] base de données d'images [Termes descripteurs IGN] graphe [Termes descripteurs IGN] morphologie mathématique [Termes descripteurs IGN] optique géométrique [Termes descripteurs IGN] rayonnement lumineux [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] segmentation par graphes d'adjacence de régions [Termes descripteurs IGN] superpixel Résumé : (auteur) In this PhD thesis, our aim is to establish a general methodology for performing the segmentation of a dataset constituted of similar images with only a few annotated images as training examples. This methodology is directly intended to be applied to images gathered in Earth observation or materials science applications, for which there is not enough annotated examples to train state-of-the-art deep learning based segmentation algorithms. The proposed methodology starts from a superpixel partition of the image and gradually merges the initial regions until anactual segmentation is obtained. The two main contributions described in this PhD thesis are the development of a new superpixel algorithm which makes use of the Eikonal equation, and the development of a superpixel merging algorithm steaming from the adaption of the Eikonal equation to the setting of graphs. The superpixels merging approach makes use of a region adjacency graph computed from the superpixel partition. The edges are weighted by a dissimilarity measure learned by a machine learning algorithm from low-level cues computed on the superpixels. In terms of application, our approach to image segmentation is finally evaluated on the SWIMSEG dataset, a dataset which contains sky cloud images. On this dataset, using only a limited amount of images for training our algorithm, we were able to obtain segmentation results similar to the ones obtained with state-of-the-art algorithms. Note de contenu : 1- Introduction 2- Fast marching based superpixels 3- Hierarchical segmentation based on wavelet decomposition 4- Learning similarities between regions 5- Region merging 6- Application Conclusion and perspectives Numéro de notice : 25837 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Morphologie Mathématique : Paris sciences et lettres : 2019 Organisme de stage : Centre de Morphologie Mathématique (Mines ParisTech) En ligne : https://hal.archives-ouvertes.fr/tel-02510662 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95191

Individual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images / Fabien Hubert Wagner in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part B (November 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 145 - part B (November 2018) . - pp 362 - 377 Titre : Individual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images Type de document : Article/Communication Auteurs : Fabien Hubert Wagner, Auteur ; Matheus Pinheiro Ferreira, Auteur ; Alber Sanchez, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 362 - 377 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] houppier [Termes descripteurs IGN] identification de plantes [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] morphologie mathématique [Termes descripteurs IGN] segmentation d'image Résumé : (auteur) Mapping tropical tree species at landscape scales to provide information for ecologists and forest managers is a new challenge for the remote sensing community. For this purpose, detection and delineation of individual tree crowns (ITCs) is a prerequisite. Here, we present a new method of automatic tree crown delineation based only on very high resolution images from WorldView-2 satellite and apply it to a region of the Atlantic rain forest with highly heterogeneous tropical canopy cover – the Santa Genebra forest reserve in Brazil. The method works in successive steps that involve pre-processing, selection of forested pixels, enhancement of borders, detection of pixels in the crown borders, correction of shade in large trees and, finally, segmentation of the tree crowns. Principally, the method uses four techniques: rolling ball algorithm and mathematical morphological operations to enhance the crown borders and ease the extraction of tree crowns; bimodal distribution parameters estimations to identify the shaded pixels in the gaps, borders, and crowns; and focal statistics for the analysis of neighbouring pixels. Crown detection is validated by comparing the delineated ITCs with a sample of ITCs delineated manually by visual interpretation. In addition, to test if the spectra of individual species are conserved in the automatic delineated crowns, we compare the accuracy of species prediction with automatic and manual delineated crowns with known species. We find that our method permits detection of up to 80% of ITCs. The seven species with over 10 crowns identified in the field were mapped with reasonable accuracy (30.5–96%) given that only WorldView-2 bands and texture features were used. Similar classification accuracies were obtained using both automatic and manual delineation, thereby confirming that species’ spectral responses are preserved in the automatic method and thus permitting the recognition of species at the landscape scale. Our method might support tropical forest applications, such as mapping species and canopy characteristics at the landscape scale. Numéro de notice : A2018-536 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.09.013 date de publication en ligne : 08/10/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.09.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91541 [article]

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