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Building detection with convolutional networks trained with transfer learning / Simon Šanca in Geodetski vestnik, vol 65 n° 4 (December 2021 - February 2022)
[article]
Titre : Building detection with convolutional networks trained with transfer learning Type de document : Article/Communication Auteurs : Simon Šanca, Auteur ; Krištof Oštir, Auteur ; Alen Mangafić, Auteur Année de publication : 2021 Article en page(s) : pp 559 - 576 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification automatique d'objets
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection du bâti
[Termes IGN] données cadastrales
[Termes IGN] image aérienne
[Termes IGN] image infrarouge couleur
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] orthoimage couleur
[Termes IGN] segmentation d'image
[Termes IGN] SlovénieRésumé : (Auteur) Building footprint detection based on orthophotos can be used to update the building cadastre. In recent years deep learning methods using convolutional neural networks have been increasingly used around the world. We present an example of automatic building classification using our datasets made of colour near-infrared orthophotos (NIR-R-G) and colour orthophotos (R-G-B). Building detection using pretrained weights from two large scale datasets Microsoft Common Objects in Context (MS COCO) and ImageNet was performed and tested. We applied the Mask Region Convolutional Neural Network (Mask R-CNN) to detect the building footprints. The purpose of our research is to identify the applicability of pre-trained neural networks on the data of another colour space to build a classification model without re-learning. Numéro de notice : A2021-930 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.15292/geodetski-vestnik.2021.04.559-593 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.15292/geodetski-vestnik.2021.04.559-593 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99409
in Geodetski vestnik > vol 65 n° 4 (December 2021 - February 2022) . - pp 559 - 576[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 139-2021041 RAB Revue Centre de documentation En réserve L003 Disponible DiResNet: Direction-aware residual network for road extraction in VHR remote sensing images / Lei Ding in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)
[article]
Titre : DiResNet: Direction-aware residual network for road extraction in VHR remote sensing images Type de document : Article/Communication Auteurs : Lei Ding, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2021 Article en page(s) : pp 10243 - 10254 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] image à très haute résolution
[Termes IGN] segmentation d'imageRésumé : (auteur) The binary segmentation of roads in very high resolution (VHR) remote sensing images (RSIs) has always been a challenging task due to factors such as occlusions (caused by shadows, trees, buildings, etc.) and the intraclass variances of road surfaces. The wide use of convolutional neural networks (CNNs) has greatly improved the segmentation accuracy and made the task end-to-end trainable. However, there are still margins to improve in terms of the completeness and connectivity of the results. In this article, we consider the specific context of road extraction and present a direction-aware residual network (DiResNet) that includes three main contributions: 1) an asymmetric residual segmentation network with deconvolutional layers and a structural supervision to enhance the learning of road topology (DiResSeg); 2) a pixel-level supervision of local directions to enhance the embedding of linear features; and 3) a refinement network to optimize the segmentation results (DiResRef). Ablation studies on two benchmark data sets (the Massachusetts data set and the DeepGlobe data set) have confirmed the effectiveness of the presented designs. Comparative experiments with other approaches show that the proposed method has advantages in both overall accuracy and F1-score. The code is available at: https://github.com/ggsDing/DiResNet . Numéro de notice : A2021-870 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3034011 Date de publication en ligne : 16/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3034011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99128
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 12 (December 2021) . - pp 10243 - 10254[article]Flexible Gabor-based superpixel-level unsupervised LDA for hyperspectral image classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)
[article]
Titre : Flexible Gabor-based superpixel-level unsupervised LDA for hyperspectral image classification Type de document : Article/Communication Auteurs : Sen Jia, Auteur ; Qingqing Zhao, Auteur ; Jiayue Zhuang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 10394 - 10409 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] classification non dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de Gabor
[Termes IGN] image hyperspectrale
[Termes IGN] ondelette de Gabor
[Termes IGN] segmentation d'image
[Termes IGN] superpixelRésumé : (auteur) Hyperspectral images encompass abundant information and provide unique characteristics for material classification. However, the labeling of training samples can be challenging in hyperspectral image classification. To address this problem, this study proposes a framework named flexible Gabor-based superpixel-level unsupervised linear discriminant analysis (FG- Su ULDA) to extract the most informative and discriminating features for classification. First, a number of 3-D flexible Gabor filters are rigorously designed using an asymmetric sinusoidal wave to sufficiently characterize the spatial–spectral structure in hyperspectral images. Then, an unsupervised linear discriminant analysis strategy guided by the entropy rate superpixel (ERS) segmentation algorithm, called Su ULDA, is skillfully introduced to reduce the extracted large amount of FG features. The Su ULDA method not only boosts the classification capability but also increases the peculiarity of features, with the aid of superpixel information. Finally, the achieved features are imported to the popular support vector machine classifier. The proposed FG- Su ULDA framework is applied to four real hyperspectral image data sets, and the experiments constantly prove that our FG- Su ULDA is superior to several state-of-the-art methods in both classification performance and computational efficiency, especially with scarce training samples. The codes of this work are available at http://jiasen.tech/papers/ for the sake of reproducibility. Numéro de notice : A2021-872 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3048994 Date de publication en ligne : 20/01/2021 En ligne : https://doi.org/10.1109/TGRS.2020.3048994 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99131
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 12 (December 2021) . - pp 10394 - 10409[article]MSegnet, a practical network for building detection from high spatial resolution images / Bo Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 12 (December 2021)
[article]
Titre : MSegnet, a practical network for building detection from high spatial resolution images Type de document : Article/Communication Auteurs : Bo Yu, Auteur ; Fang Chen, Auteur ; Ying Dong, Auteur Année de publication : 2021 Article en page(s) : pp 901 - 906 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection du bâti
[Termes IGN] image à haute résolution
[Termes IGN] matrice
[Termes IGN] segmentation multi-échelle
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) Building detection in big earth data by remote sensing is crucial for urban development. However, improving its accuracy remains challenging due to complicated background objects and different viewing angles from various remotely sensed images. The hereto proposed methods predominantly focus on multi-scale feature learning, which omits features in multiple aspect ratios. Moreover, postprocessing is required to refine the segmentation performance. We propose modified semantic segmentation (MSegnet), a single-shot semantic segmentation model based on a matrix of convolution layers to extract features in multiple scales and aspect ratios. MSegnet consists of two modules: backbone feature learning and matrix convolution to conduct vertical and horizontal learning. The matrix convolution comprises a set of convolution operations with different aspect ratios. MSegnet is applied to a public building data set that is widely used for evaluation and shown to achieve satisfactory accuracy, compared with the published single-shot methods. Numéro de notice : A2021-898 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00016R2 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.14358/PERS.21-00016R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99296
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 12 (December 2021) . - pp 901 - 906[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021121 SL Revue Centre de documentation Revues en salle Disponible Multi-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images / Yiying Hua in Forests, vol 12 n° 12 (December 2021)
[article]
Titre : Multi-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images Type de document : Article/Communication Auteurs : Yiying Hua, Auteur ; Xuesheng Zhao, Auteur Année de publication : 2021 Article en page(s) : n° 1768 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] bande infrarouge
[Termes IGN] coefficient de corrélation
[Termes IGN] couvert forestier
[Termes IGN] détection de contours
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] modèle statistique
[Termes IGN] Mongolie intérieure (Chine)
[Termes IGN] régression
[Termes IGN] surveillance de la végétationRésumé : (auteur) In remote sensing, red edge bands are important indicators for monitoring vegetation growth. To examine the application potential of red edge bands in forest canopy closure estimation, three types of commonly used models—empirical statistical models (multiple stepwise regression (MSR)), machine learning models (back propagation neural network (BPNN)) and physical models (Li–Strahler geometric-optical (Li–Strahler GO) models)—were constructed and verified based on Sentinel-2 data, DEM data and measured data. In addition, we set up a comparative experiment without red edge bands. The relative error (ER) values of the BPNN model, MSR model, and Li–Strahler GO model with red edge bands were 16.97%, 20.76% and 24.83%, respectively. The validation accuracy measures of these models were higher than those of comparison models. For comparative experiments, the ER values of the MSR, Li–Strahler GO and BPNN models were increased by 13.07%, 4% and 1.22%, respectively. The experimental results demonstrate that red edge bands can effectively improve the accuracy of forest canopy closure estimation models to varying degrees. These findings provide a reference for modeling and estimating forest canopy closure using red edge bands based on Sentinel-2 images. Numéro de notice : A2021-125 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f12121768 Date de publication en ligne : 14/12/2021 En ligne : https://doi.org/10.3390/f12121768 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99318
in Forests > vol 12 n° 12 (December 2021) . - n° 1768[article]OBIA-based extraction of artificial terrace damages in the Loess plateau of China from UAV photogrammetry / Xuan Fang in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)PermalinkParticle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images / Mohammad Hossein Gamshadzaei in Geocarto international, vol 36 n° 20 ([01/12/2021])PermalinkSemi-automatic reconstruction of object lines using a smartphone’s dual camera / Mohammed Aldelgawy in Photogrammetric record, Vol 36 n° 176 (December 2021)PermalinkThe use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space / Renato César Dos santos in Applied geomatics, vol 13 n° 4 (December 2021)PermalinkFootprint size design of large-footprint full-waveform LiDAR for forest and topography applications: A theoretical study / Xuebo Yang in IEEE Transactions on geoscience and remote sensing, vol 59 n° 11 (November 2021)PermalinkSemi-automatic extraction of rural roads under the constraint of combined geometric and texture features / Hai Tan in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)PermalinkAdaptive edge preserving maps in Markov random fields for hyperspectral image classification / Chao Pan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkA deep multi-modal learning method and a new RGB-depth data set for building roof extraction / Mehdi Khoshboresh Masouleh in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)PermalinkDisaster Image Classification by Fusing Multimodal Social Media Data / Zhiqiang Zou in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)PermalinkDisaster intensity-based selection of training samples for remote sensing building damage classification / Luis Moya in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)Permalink