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Large-scale individual building extraction from open-source satellite imagery via super-resolution-based instance segmentation approach / Shenglong Chen in ISPRS Journal of photogrammetry and remote sensing, vol 195 (January 2023)
Titre : Large-scale individual building extraction from open-source satellite imagery via super-resolution-based instance segmentation approach Type de document : Article/Communication Auteurs : Shenglong Chen, Auteur ; Yoshiki Ogawa, Auteur ; Chenbo Zhao, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 129 - 152 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] couleur (variable spectrale)
[Termes IGN] détection du bâti
[Termes IGN] distribution de Gauss
[Termes IGN] image à haute résolution
[Termes IGN] mosaïquage d'images
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Building footprint is a primary dataset of an urban geographic information system (GIS) database. Therefore, it is essential to establish a robust and automated framework for large-scale building extraction. However, the characteristic of remote sensing images complicates the application of the instance segmentation method based on the Mask R-CNN model, which ought to be improved toward extracting and fusing multi-scale features. Moreover, open-source satellite image datasets with wider spatial coverage and temporal resolution than high-resolution images may exhibit different coloration and resolution. This study proposes a large-scale building extraction framework based on super-resolution (SR) and instance segmentation using a relatively lower-resolution (>0.6 m) open-sourced dataset. The framework comprises four steps: color normalization and image super-resolution, scene classification, building extraction, and scene mosaicking. We took Hyogo Prefecture, Japan (19,187 km2) as a test area and extracted 1,726,006 (29.12 km2) of the 3,301,488 buildings (32.46 km2), where the number of buildings and footprint area increased by 3.0 % and 5.0 % respectively. The result indicated that the color normalization and image super-resolution could improve the visual quality of open-source satellite images and contribute to building extraction accuracy. Moreover, the improved Mask R-CNN based on Multi-Path Vision Transformer (MPViT) backbone achieved F1 scores of 0.71, 0.70, 0.81, and 0.67 for non-built-up, rural, suburban, and urban areas, respectively, which is better than those of the baseline model and other mainstream instance segmentation approaches. This study demonstrates the potential of acquiring acceptable building footprint maps from open-source satellite images, which has significant practical implications. Numéro de notice : A2023-019 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.11.006 Date de publication en ligne : 30/11/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.11.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102214
in ISPRS Journal of photogrammetry and remote sensing > vol 195 (January 2023) . - pp 129 - 152[article]
Titre : Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images Type de document : Article/Communication Auteurs : Ekrem Saralioglu, Auteur ; Oguz Gungor, Auteur Année de publication : 2022 Article en page(s) : pp 5433 - 5452 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] acquisition d'images
[Termes IGN] apprentissage profond
[Termes IGN] approche participative
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] couleur (variable spectrale)
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] étiquette
[Termes IGN] image multibande
[Termes IGN] OpenStreetMap
[Termes IGN] pixel
[Termes IGN] plateforme collaborative
[Termes IGN] texture d'image
[Termes IGN] WorldviewRésumé : (auteur) In order to solve insufficient training data problem in remote sensing, a web platform was created so that registered users can generate labeled data for various classes in a dynamic structure. Users were asked to select representative pixel groups for the forest, hazelnut, shadow, soil, tea, and building classes with the polygon tool, and then assign a class label corresponding to each created polygon thanks to the help document displaying descriptive information regarding the locations, colors, textures and distributions of the classes in the image. Crowdsourcing was again used to test the accuracy of the tagged data produced by crowdsourcing. The created data set was overlaid with the original WV-2 image, and the correctness of the labels of the polygons was once visually verified. Finally, the WV-2 image, consisting of 40 patches, was classified with CNN and an average of over 95% accuracy was achieved. Numéro de notice : A2022-702 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1917006 Date de publication en ligne : 26/05/2021 En ligne : https://doi.org/10.1080/10106049.2021.1917006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101561
in Geocarto international > vol 37 n° 18 [01/09/2022] . - pp 5433 - 5452[article]
Titre : A GAN-based approach toward architectural line drawing colorization prototyping Type de document : Article/Communication Auteurs : Qian (Chayn) Sun, Auteur ; Yan Chen, Auteur ; Wenyuan Tao, Auteur ; Han Jiang, Auteur ; Mu Zhang, Auteur ; Kan Chen, Auteur ; Marius Erdt, Auteur Année de publication : 2022 Article en page(s) : pp 1283 - 1300 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] architecture
[Termes IGN] bâtiment
[Termes IGN] couleur (variable spectrale)
[Termes IGN] prototype
[Termes IGN] réseau antagoniste génératif
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Line drawing with colorization is a popular art format and tool for architectural illustration. The goal of this research is toward generating a high-quality and natural-looking colorization based on an architectural line drawing. This paper presents a new Generative Adversarial Network (GAN)-based method, named ArchGANs, including ArchColGAN and ArchShdGAN. ArchColGAN is a GAN-based line-feature-aware network for stylized colorization generation. ArchShdGAN is a lighting effects generation network, from which the building depiction in 3D can benefit. In particular, ArchColGAN is able to maintain the important line features and the correlation property of building parts as well as reduce the uneven colorization caused by sparse lines. Moreover, we proposed a color enhancement method to further improve ArchColGAN. Besides the single line drawing images, we also extend our method to handle line drawing image sequences and achieve rotation animation. Experiments and studies demonstrate the effectiveness and usefulness of our proposed method for colorization prototyping. Numéro de notice : A2022-154 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s00371-021-02219-x Date de publication en ligne : 23/07/2021 En ligne : https://doi.org/10.1007/s00371-021-02219-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100292
in The Visual Computer > vol 38 n° 4 (April 2022) . - pp 1283 - 1300[article]
Titre : 3D point cloud compression Type de document : Thèse/HDR Auteurs : Chao Cao, Auteur ; Titus Zaharia, Directeur de thèse ; Marius Preda, Directeur de thèse Editeur : Paris : Institut Polytechnique de Paris Année de publication : 2021 Importance : 165 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de doctorat de l’Institut polytechnique de Paris, Spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] compression d'image
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] couleur (variable spectrale)
[Termes IGN] état de l'art
[Termes IGN] objet 3D
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] scène 3D
[Termes IGN] segmentation d'image
[Termes IGN] semis de points
[Termes IGN] structure-from-motionIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) With the rapid growth of multimedia content, 3D objects are becoming more and more popular. Most of the time, they are modeled as complex polygonal meshes or dense point clouds, providing immersive experiences in different industrial and consumer multimedia applications. The point cloud, which is easier to acquire than mesh and is widely applicable, has raised many interests in both the academic and commercial worlds.A point cloud is a set of points with different properties such as their geometrical locations and the associated attributes (e.g., color, material properties, etc.). The number of the points within a point cloud can range from a thousand, to constitute simple 3D objects, up to billions, to realistically represent complex 3D scenes. Such huge amounts of data bring great technological challenges in terms of transmission, processing, and storage of point clouds.In recent years, numerous research works focused their efforts on the compression of meshes, while less was addressed for point clouds. We have identified two main approaches in the literature: a purely geometric one based on octree decomposition, and a hybrid one based on both geometry and video coding. The first approach can provide accurate 3D geometry information but contains weak temporal consistency. The second one can efficiently remove the temporal redundancy yet a decrease of geometrical precision can be observed after the projection. Thus, the tradeoff between compression efficiency and accurate prediction needs to be optimized.We focused on exploring the temporal correlations between dynamic dense point clouds. We proposed different approaches to improve the compression performance of the MPEG (Moving Picture Experts Group) V-PCC (Video-based Point Cloud Compression) test model, which provides state-of-the-art compression on dynamic dense point clouds.First, an octree-based adaptive segmentation is proposed to cluster the points with different motion amplitudes into 3D cubes. Then, motion estimation is applied to these cubes using affine transformation. Gains in terms of rate-distortion (RD) performance have been observed in sequences with relatively low motion amplitudes. However, the cost of building an octree for the dense point cloud remains expensive while the resulting octree structures contain poor temporal consistency for the sequences with higher motion amplitudes.An anatomical structure is then proposed to model the motion of the point clouds representing humanoids more inherently. With the help of 2D pose estimation tools, the motion is estimated from 14 anatomical segments using affine transformation.Moreover, we propose a novel solution for color prediction and discuss the residual coding from prediction. It is shown that instead of encoding redundant texture information, it is more valuable to code the residuals, which leads to a better RD performance.Although our contributions have improved the performances of the V-PCC test models, the temporal compression of dynamic point clouds remains a highly challenging task. Due to the limitations of the current acquisition technology, the acquired point clouds can be noisy in both geometry and attribute domains, which makes it challenging to achieve accurate motion estimation. In future studies, the technologies used for 3D meshes may be exploited and adapted to provide temporal-consistent connectivity information between dynamic 3D point clouds. Note de contenu : Chapter 1 - Introduction
1.1. Background and motivation
1.2. Outline of the thesis and contributions
Chapter 2 - 3D Point Cloud Compression: State of the art
2.1. The 3D PCC “Universe Map” for methods
2.2. 1D methods: geometry traversal
2.3. 2D methods: Projection and mapping onto 2D planar domains
2.4. 3D methods: Direct exploitation of 3D correlations
2.5. DL-based methods
2.6. 3D PCC: What is missing?
2.7. MPEG 3D PCC standards
Chapter 3 - Extended Study of MPEG V-PCC and G-PCC Approaches
3.1. V-PCC methodology
3.2. Experimental evaluation of V-PCC
3.3. G-PCC methodology
3.4. Experimental evaluation of G-PCC
3.5. Experiments on the V-PCC inter-coding mode
3.6. Conclusion
Chapter 4 - Octree-based RDO segmentation
4.1. Pipeline
4.2. RDO-based octree segmentation
4.3. Prediction modeS
4.4. Experimental results
4.5. Conclusion
Chapter 5 - Skeleton-based motion estimation and compensation
5.1. Introduction
5.2. 3D Skeleton Generation
5.3. Motion estimation and compression
5.4. Experimental results
5.5. Conclusion
Chapter 6 - Temporal prediction using anatomical segmentation
6.1. Introduction
6.2. A novel dynamic 3D point cloud dataset
6.3. Prediction structure
6.4. Improved anatomy segmentation
6.5. Experimental results
6.6. Conclusion
Chapter 7 - A novel color compression for point clouds using affine transformation
7.1. Introduction
7.2. The residuals from both geometry and color
7.3. The prediction structure
7.4. Compression of the color residuals
7.5. Experimental results
7.6. Conclusion
Chapter 8 - Conclusion and future work
8.1. Conclusion
8.2. Future workNuméro de notice : 26821 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : informatique : Paris : 2021 Organisme de stage : Telecom SudParis nature-HAL : Thèse DOI : sans Date de publication en ligne : 13/04/2022 En ligne : https://tel.hal.science/tel-03524521 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100476
Titre : Unsupervised vision methods based on image perceptual information Type de document : Thèse/HDR Auteurs : Eric Bazan, Auteur ; Petr Dokladal, Directeur de thèse ; Eva Dokladalova, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2021 Importance : 227 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de doctorat de l'Université Paris Sciences et Lettres, Préparée à MINES ParisTech, spécialité Morphologie MathématiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage non-dirigé
[Termes IGN] compréhension de l'image
[Termes IGN] contour
[Termes IGN] couleur (variable spectrale)
[Termes IGN] décomposition spectrale
[Termes IGN] filtre de Gabor
[Termes IGN] image captée par drone
[Termes IGN] segmentation d'image
[Termes IGN] texture d'image
[Termes IGN] visionIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This thesis work deals with extracting features and low-level primitives from perceptual image information to understand scenes. Motivated by the needs and problems in Unmanned Aerial Vehicles (UAVs) vision based navigation, we propose novel methods focusing on image understanding problems. This work explores three main pieces of information in an image: intensity, color, and texture. In the first chapter of the manuscript, we work with the intensity information through image contours. We combine this information with human perception concepts, such as the Helmholtz principle and the Gestalt laws, to propose an unsupervised framework for object detection and identification. We validate this methodology in the last stage of the drone navigation, just before the landing. In the following chapters of the manuscript, we explore the color and texture information contained in the images. First, we present an analysis of color and texture as global distributions of an image. This approach leads us to study the Optimal Transport theory and its properties as a true metric for color and texture distributions comparison. We review and compare the most popular similarity measures between distributions to show the importance of a metric with the correct properties such as non-negativity and symmetry. We validate such concepts in two image retrieval systems based on the similarity of color distribution and texture energy distribution. Finally, we build an image representation that exploits the relationship between color and texture information. The image representation results from the image’s spectral decomposition, which we obtain by the convolution with a family of Gabor filters. We present in detail the improvements to the Gabor filter and the properties of the complex color spaces. We validate our methodology with a series of segmentation and boundary detection algorithms based on the computed perceptual feature space. Numéro de notice : 15285 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Morphologie Mathématique : Paris Sciences et Lettres : 2021 Organisme de stage : Centre de Morphologie Mathématique DOI : sans En ligne : https://hal.science/tel-03690309 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101418 10th Colour and Visual Computing Symposium 2020 (CVCS 2020), Gjøvik, Norway, and Virtual, September 16-17, 2020 / Jean-Baptiste Thomas (2020)
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PermalinkPermalinkPermalinkPermalinkPermalinkTraitements numériques des images de télédétection, Vol. 1. Notions de base sur les couleurs / Olivier de Joinville (2012)
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