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3D iterative spatiotemporal filtering for classification of multitemporal satellite data sets / Hessah Albanwan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 1 (January 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 1 (January 2020) . - pp 23 - 31 Titre : 3D iterative spatiotemporal filtering for classification of multitemporal satellite data sets Type de document : Article/Communication Auteurs : Hessah Albanwan, Auteur ; Rongjun Qin, Auteur ; Xiaohu Lu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 23 - 31 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse de données [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] changement d'utilisation du sol [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] données multitemporelles [Termes descripteurs IGN] filtrage spatiotemporel [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] itération [Termes descripteurs IGN] orthoimage Résumé : (Auteur) The current practice in land cover/land use change analysis relies heavily on the individually classified maps of the multi-temporal data set. Due to varying acquisition conditions (e.g., illumination, sensors, seasonal differences), the classification maps yielded are often inconsistent through time for robust statistical analysis. 3D geometric features have been shown to be stable for assessing differences across the temporal data set. Therefore, in this article we investigate the use of a multi-temporal orthophoto and digital surface model derived from satellite data for spatiotemporal classification. Our approach consists of two major steps: generating per-class probability distribution maps using the random-forest classifier with limited training samples, and making spatiotemporal inferences using an iterative 3D spatiotemporal filter operating on per-class probability maps. Our experimental results demonstrate that the proposed methods can consistently improve the individual classification results by 2%–6% and thus can be an important postclassification refinement approach. Numéro de notice : A2020-049 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.1.23 date de publication en ligne : 01/01/2020 En ligne : https://doi.org/10.14358/PERS.86.1.23 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94534 [article]

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Identification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data / Guo-Hui Yao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 1 (January 2020) . - pp 691 - 703 Titre : Identification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data Type de document : Article/Communication Auteurs : Guo-Hui Yao, Auteur ; Chang-qing Ke, Auteur ; Xiaobing Zhou, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 691 - 703 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse multiéchelle [Termes descripteurs IGN] bande L [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] glacier [Termes descripteurs IGN] Himalaya [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] interferométrie différentielle [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] segmentation Résumé : (auteur) To study the applicability of full polarimetric synthetic aperture radar (SAR) data to identify alpine glaciers in the central Himalayas, six polarimetric decomposition methods were used to obtain 20 polarimetric characteristic parameters based on the Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band SAR (PALSAR) data. Object-oriented multiscale segmentation was performed on a Landsat 8 Operational Land Imager (OLI) image prior to classification, and the vector boundaries of different types of training samples were selected from the segmented results. We performed a support vector machine (SVM)-based classification on the characteristic parameters from each polarimetric decomposition. All 20 parameters were then screened and combined according to different requirements: the degree of separability of different types of training samples and the type of scattering mechanisms. The results show that the classification accuracy of the incoherent decomposition characteristics based on the covariance matrix is the best, reaching 87%, and it can exceed 91% after adding the local incidence angle to the suite of classifiers. Eventually, more than 93% accuracy was achieved using a combination of multiple polarimetric parameters, which reduced the misclassification between bare ice and rock. We also analyzed the use of controlling factors on the accuracy of alpine glacier identification and found that the polarimetric information and aspect of the glacier surface are the most important factors. The former is the main basis for identification but the latter will confuse the feature distributions of different categories and cause misclassification. Numéro de notice : A2020-077 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2939430 date de publication en ligne : 25/09/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2939430 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94613 [article]

Half a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning / Benjamin Kellenberger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 12 (December 2019) . - pp 9524 - 9533 Titre : Half a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning Type de document : Article/Communication Auteurs : Benjamin Kellenberger, Auteur ; Diego Marcos, Auteur ; Sylvain Lobry, Auteur ; Devis Tuia, Auteur Année de publication : 2019 Article en page(s) : pp 9524 - 9533 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] cible mobile [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] faune locale [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] Namibie [Termes descripteurs IGN] réalité de terrain [Termes descripteurs IGN] recensement Résumé : (auteur) We present an Active Learning (AL) strategy for reusing a deep Convolutional Neural Network (CNN)-based object detector on a new data set. This is of particular interest for wildlife conservation: given a set of images acquired with an Unmanned Aerial Vehicle (UAV) and manually labeled ground truth, our goal is to train an animal detector that can be reused for repeated acquisitions, e.g., in follow-up years. Domain shifts between data sets typically prevent such a direct model application. We thus propose to bridge this gap using AL and introduce a new criterion called Transfer Sampling (TS). TS uses Optimal Transport (OT) to find corresponding regions between the source and the target data sets in the space of CNN activations. The CNN scores in the source data set are used to rank the samples according to their likelihood of being animals, and this ranking is transferred to the target data set. Unlike conventional AL criteria that exploit model uncertainty, TS focuses on very confident samples, thus allowing quick retrieval of true positives in the target data set, where positives are typically extremely rare and difficult to find by visual inspection. We extend TS with a new window cropping strategy that further accelerates sample retrieval. Our experiments show that with both strategies combined, less than half a percent of oracle-provided labels are enough to find almost 80% of the animals in challenging sets of UAV images, beating all baselines by a margin. Numéro de notice : A2019-598 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2927393 date de publication en ligne : 20/08/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2927393 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94592 [article]

Scene context-driven vehicle detection in high-resolution aerial images / Chao Tao in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 57 n° 10 (October 2019) . - pp 7339 - 7351 Titre : Scene context-driven vehicle detection in high-resolution aerial images Type de document : Article/Communication Auteurs : Chao Tao, Auteur ; Li Mi, Auteur ; Yansheng Li, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 7339 - 7351 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] objet mobile [Termes descripteurs IGN] véhicule automobile Résumé : (auteur) As the spatial resolution of remote sensing images is improving gradually, it is feasible to realize “scene-object” collaborative image interpretation. Unfortunately, this idea is not fully utilized in vehicle detection from high-resolution aerial images, and most of the existing methods may be promoted by considering the variability of vehicle spatial distribution in different image scenes and treating vehicle detection tasks scene-specific. With this motivation, a scene context-driven vehicle detection method is proposed in this paper. At first, we perform scene classification using the deep learning method and, then, detect vehicles in roads and parking lots separately through different vehicle detectors. Afterward, we further optimize the detection results using different postprocessing rules according to different scene types. Experimental results show that the proposed approach outperforms the state-of-the-art algorithms in terms of higher detection accuracy rate and lower false alarm rate. Numéro de notice : A2019-535 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2912985 date de publication en ligne : 03/06/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2912985 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94131 [article]

Delineation of vacant building land using orthophoto and lidar data object classification / Dejan Jenko in Geodetski vestnik, vol 63 n° 3 (September - November 2019)  

Public [article]  inGeodetski vestnik > vol 63 n° 3 (September - November 2019) . - pp 344 - 378 Titre : Delineation of vacant building land using orthophoto and lidar data object classification Type de document : Article/Communication Auteurs : Dejan Jenko, Auteur ; Mojca Foški, Auteur ; Krištof Oštir, Auteur ; Žiga Kokalj, Auteur Année de publication : 2019 Article en page(s) : pp 344 - 378 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] couche thématique [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] logement [Termes descripteurs IGN] orthoimage [Termes descripteurs IGN] Slovénie Résumé : (Auteur) Exact data about the location and area of vacant building land have been a major issue in several Slovene municipalities. This article deals with automatic vacant building land delineation. The presented methodology is based on the object-based classification that derives the land cover layer from orthophoto and laser scanning data. With post-processing and data cleaning in GIS, we create the vacant building land layer. The methodology was tested in study areas in the Municipality of Trebnje. The results were compared to the vacant building land layer generated by visual interpretation (manual vectorisation). We found that the presented methodology of automatic delineation of vacant buildings can speed up the processing and lower the cost of manual vectorisation and, in particular, data updating but we cannot completely replace manual work. Numéro de notice : A2019-500 Thématique : POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2019.03.344-378 En ligne : http://dx.doi.org/10.15292/geodetski-vestnik.2019.03.344-378 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93782 [article]

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Exploring the synergy between Landsat and ASAR towards improving thematic mapping accuracy of optical EO data / Alexander Cass in Applied geomatics, vol 11 n° 3 (September 2019)  

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A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm / Ana Claudia Dos Santos Luciano in International journal of applied Earth observation and geoinformation, vol 80 (August 2019)  

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3D hyperspectral point cloud generation: Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction / Maximilian Brell in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

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A light and faster regional convolutional neural network for object detection in optical remote sensing images / Peng Ding in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)  

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An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery / Luis Angel Ruiz in Geocarto international, vol 33 n° 5 (May 2018)  

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Object-based superresolution land-cover mapping from remotely sensed imagery / Yuehong Chen in IEEE Transactions on geoscience and remote sensing, vol 56 n° 1 (January 2018)  

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Utilisation de QGIS en télédétection, Ch. 2. Apports du MNT topo-bathymétrique pour l'évolution bio-géomorphologique des marais d'Ichkeul (Tunisie) / Zeineb Kassouk (2018)

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Object-based classification of terrestrial laser scanning point clouds for landslide monitoring / Andreas Mayr in Photogrammetric record, vol 32 n° 160 (December 2017)  

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Extraction du bâti sur le territoire de la wilaya de Blida (Algérie) / Siham Bougdour in Géomatique expert, n° 119 (novembre - décembre 2017)

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Robust object-based multipass InSAR deformation reconstruction / Jian Kang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

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