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Domain adaptive transfer attack-based segmentation networks for building extraction from aerial images / Younghwan Na in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp 5171 - 5182 Titre : Domain adaptive transfer attack-based segmentation networks for building extraction from aerial images Type de document : Article/Communication Auteurs : Younghwan Na, Auteur ; Jun Hee Kim, Auteur ; Kyungsu Lee, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 5171 - 5182 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] entropie [Termes IGN] image aérienne [Termes IGN] segmentation sémantique Résumé : (auteur) Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test data sets, the CNN-based segmentation models trained by a training data set fail to segment buildings for the test data set. In this article, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and the adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-data set experiments and ablation study are conducted for three different data sets: the Inria aerial image labeling data set, the Massachusetts building data set, and the WHU East Asia data set. Compared with the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall intersection over union (IoU). Moreover, it is verified that the proposed method outperforms even when compared with feature adaptation (FA) and output space adaptation (OSA). Numéro de notice : A2021-427 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3010055 date de publication en ligne : 30/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3010055 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97783 [article]

A high-resolution satellite DEM filtering method assisted with building segmentation / Yihui Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 6 (June 2021) . - pp 421 - 430 Titre : A high-resolution satellite DEM filtering method assisted with building segmentation Type de document : Article/Communication Auteurs : Yihui Li, Auteur ; Fang Gao, Auteur ; Wentao Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 421 - 430 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du bâti [Termes IGN] filtrage numérique d'image [Termes IGN] filtre adaptatif [Termes IGN] image à haute résolution [Termes IGN] Kappa de Cohen [Termes IGN] modèle numérique de surface [Termes IGN] orthoimage [Termes IGN] point d'appui [Termes IGN] segmentation d'image Résumé : (Auteur) Digital elevation model (DEM) filtering is critical in DEM production, and large-area meter-level resolution DEM is mainly generated from high-resolution satellite images. However, the current DEM filtering methods are mostly aimed at laser scanning data and tend to excessively remove ground points when processing a satellite digital surface model (DSM). To accurately filter out buildings and preserve terrain, we propose a DEM filtering algorithm using building segmentation results of orthophoto. Based on morphological filtering, our method estimates the probability of being a built-up area or mountains for DSM, and according to this probability the filtering parameters are adaptively adjusted. For robustness, our method performs the above filtering operation on DSM through a sliding-window approach, and finally the nonground points are determined by the votes of multiple filtering. Experiments against six representative data sets have shown that our method achieved superior performance than classical algorithms and commercial software. Numéro de notice : A2021-374 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.6.421 date de publication en ligne : 01/06/2021 En ligne : https://doi.org/10.14358/PERS.87.6.421 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97827 [article]

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Mask R-CNN-based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid-19 response in Khartoum, Sudan / Dirk Tiede in Transactions in GIS, Vol 25 n° 3 (June 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 3 (June 2021) . - pp 1213-1227 Titre : Mask R-CNN-based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid-19 response in Khartoum, Sudan Type de document : Article/Communication Auteurs : Dirk Tiede, Auteur ; Gina Schwendemann, Auteur ; Ahmad Alobaidi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1213-1227 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du bâti [Termes IGN] échantillonnage [Termes IGN] épidémie [Termes IGN] gestion de crise [Termes IGN] HRV (capteur) [Termes IGN] image à très haute résolution [Termes IGN] image Pléiades-HR [Termes IGN] itération [Termes IGN] Soudan Résumé : Auteur) Within the constraints of operational work supporting humanitarian organizations in their response to the Covid-19 pandemic, we conducted building extraction for Khartoum, Sudan. We extracted approximately 1.2 million dwellings and buildings, using a Mask R-CNN deep learning approach from a Pléiades very high-resolution satellite image with 0.5 m pixel resolution. Starting from an untrained network, we digitized a few hundred samples and iteratively increased the number of samples by validating initial classification results and adding them to the sample collection. We were able to strike a balance between the need for timely information and the accuracy of the result by combining the output from three different models, each aiming at distinctive types of buildings, in a post-processing workflow. We obtained a recall of 0.78, precision of 0.77 and F1 score of 0.78, and were able to deliver first results in only 10 days after the initial request. The procedure shows the great potential of convolutional neural network frameworks in combination with GIS routines for dwelling extraction even in an operational setting. Numéro de notice : A2021-464 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12766 date de publication en ligne : 06/05/2021 En ligne : https://doi.org/10.1111/tgis.12766 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98060 [article]

A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery / Ting Bai in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 4 (April 2021) . - pp 249-262 Titre : A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Ting Bai, Auteur ; Kaimin Sun, Auteur ; Wenzhuo Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 249-262 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement d'occupation du sol [Termes IGN] classe d'objets [Termes IGN] classification par forêts aléatoires [Termes IGN] détection de changement [Termes IGN] détection du bâti [Termes IGN] image à haute résolution [Termes IGN] milieu urbain [Termes IGN] segmentation multi-échelle Résumé : (Auteur) A single-scale object-based change-detection classifier can distinguish only global changes in land cover, not the more granular and local changes in urban areas. To overcome this issue, a novel class-specific object-based change-detection method is proposed. This method includes three steps: class-specific scale selection, class-specific classifier selection, and land cover change detection. The first step combines multi-resolution segmentation and a random forest to select the optimal scale for each change type in land cover. The second step links multi-scale hierarchical sampling with a classifier such as random forest, support vector machine, gradient-boosting decision tree, or Adaboost; the algorithm automatically selects the optimal classifier for each change type in land cover. The final step employs the optimal classifier to detect binary changes and from-to changes for each change type in land cover. To validate the proposed method, we applied it to two high-resolution data sets in urban areas and compared the change-detection results of our proposed method with that of principal component analysis k-means, object-based change vector analysis, and support vector machine. The experimental results show that our proposed method is more accurate than the other methods. The proposed method can address the high levels of complexity found in urban areas, although it requires historical land cover maps as auxiliary data. Numéro de notice : A2021-332 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.4.249 date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.14358/PERS.87.4.249 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97528 [article]

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L'oeil de l'espace / Anonyme in Géomètre, n° 2190 (avril 2021)

Public [article]  inGéomètre > n° 2190 (avril 2021) . - pp 45 - 45 Titre : L'oeil de l'espace Type de document : Article/Communication Auteurs : Anonyme, Auteur Année de publication : 2021 Article en page(s) : pp 45 - 45 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] acquisition d'images [Termes IGN] détection de changement [Termes IGN] détection du bâti [Termes IGN] données localisées [Termes IGN] droit foncier [Termes IGN] image aérienne Résumé : (Auteur) Plus rien n'échappe à la télédétection. S'il est envisageable de se cacher derrière une clôture, ce n'est plus possible depuis le ciel ou l'espace. Numéro de notice : A2021-325 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtSansCL DOI : sans date de publication en ligne : 07/04/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97483 [article]

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Characterizing urban land changes of 30 global megacities using nighttime light time series stacks / Qiming Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)  

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Automatic filtering and 2D modeling of airborne laser scanning building point cloud / Fayez Tarsha-Kurdi in Transactions in GIS, Vol 25 n° 1 (February 2021)  

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Automatic object extraction from airborne laser scanning point clouds for digital base map production / Elyta Widyaningrum (2021)  

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Building extraction from Lidar data using statistical methods / Haval Abdul-Jabbar Sadeq in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)  

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Polarimetric Synthetic Aperture Radar / Irena Hajnsek (2021)  

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Automatic building footprint extraction from UAV images using neural networks / Zoran Kokeza in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)  

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Building change detection using a shape context similarity model for LiDAR data / Xuzhe Lyu in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

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A graph convolutional network model for evaluating potential congestion spots based on local urban built environments / Kun Qin in Transactions in GIS, Vol 24 n° 5 (October 2020)  

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Semi-automatic building extraction from WorldView-2 imagery using taguchi optimization / Hasan Tonbul in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)  

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Object-based automatic multi-index built-up areas extraction method for WorldView-2 satellite imagery / Zhenhui Sun in Geocarto international, Vol 35 n° 8 ([01/06/2020])  

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