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Forest change detection in incomplete satellite images with deep neural networks / Salman H. Khan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
[article]
Titre : Forest change detection in incomplete satellite images with deep neural networks Type de document : Article/Communication Auteurs : Salman H. Khan, Auteur ; Xuming He, Auteur ; Fatih Porikli, Auteur ; Mohammed Bennamoun, Auteur Année de publication : 2017 Article en page(s) : pp 5407 - 5423 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] analyse multirésolution
[Termes IGN] apprentissage profond
[Termes IGN] détection de changement
[Termes IGN] forêt
[Termes IGN] réflectance de surface
[Termes IGN] réseau neuronal artificiel
[Termes IGN] retouche
[Termes IGN] surveillance de la végétationRésumé : (Auteur) Land cover change monitoring is an important task from the perspective of regional resource monitoring, disaster management, land development, and environmental planning. In this paper, we analyze imagery data from remote sensing satellites to detect forest cover changes over a period of 29 years (1987-2015). Since the original data are severely incomplete and contaminated with artifacts, we first devise a spatiotemporal inpainting mechanism to recover the missing surface reflectance information. The spatial filling process makes use of the available data of the nearby temporal instances followed by a sparse encoding-based reconstruction. We formulate the change detection task as a region classification problem. We build a multiresolution profile (MRP) of the target area and generate a candidate set of bounding-box proposals that enclose potential change regions. In contrast to existing methods that use handcrafted features, we automatically learn region representations using a deep neural network in a data-driven fashion. Based on these highly discriminative representations, we determine forest changes and predict their onset and offset timings by labeling the candidate set of proposals. Our approach achieves the state-of-the-art average patch classification rate of 91.6% (an improvement of ~16%) and the mean onset/offset prediction error of 4.9 months (an error reduction of five months) compared with a strong baseline. We also qualitatively analyze the detected changes in the unlabeled image regions, which demonstrate that the proposed forest change detection approach is scalable to new regions. Numéro de notice : A2017-663 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2707528 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2707528 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87105
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5407 - 5423[article]Insight provenance for spatiotemporal visual analytics : Theory, review, and guidelines / Andreas Hall in Journal of Spatial Information Science, JoSIS, n° 15 (September 2017)
[article]
Titre : Insight provenance for spatiotemporal visual analytics : Theory, review, and guidelines Type de document : Article/Communication Auteurs : Andreas Hall, Auteur ; Paula Ahonen-Rainio, Auteur ; Kirsi Virrantaus, Auteur Année de publication : 2017 Article en page(s) : pp 65 - 88 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] cadre conceptuel
[Termes IGN] données spatiotemporelles
[Termes IGN] orientations
[Termes IGN] raisonnement spatial
[Termes IGN] représentation mentale spatiale
[Vedettes matières IGN] GéovisualisationRésumé : (Auteur) Research on provenance, which focuses on different ways to describe and record the history of changes and advances made throughout an analysis process, is an integral part of visual analytics. This paper focuses on providing the provenance of insight and rationale through visualizations while emphasizing, first, that this entails a profound understanding of human cognition and reasoning and that, second, the special nature of spatiotemporal data needs to be acknowledged in this process. A recently proposed human reasoning framework for spatiotemporal analysis, and four guidelines for the creation of visualizations that provide the provenance of insight and rationale published in relation to that framework, work as a starting point for this paper. While these guidelines are quite abstract, this paper set out to create a set of more concrete guidelines. On the basis of a review of available provenance solutions, this paper identifies a set of key features that are of relevance when providing the provenance of insight and rationale and, on the basis of these features, produces a new set of complementary guidelines that are more practically oriented than the original ones. Together, these two sets of guidelines provide both a theoretical and practical approach to the problem of providing the provenance of insight and rationale. Providing these kinds of guidelines represents a new approach in provenance research. Numéro de notice : A2017-822 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5311/JOSIS.2017.15.337 En ligne : https://doi.org/10.5311/JOSIS.2017.15.337 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89307
in Journal of Spatial Information Science, JoSIS > n° 15 (September 2017) . - pp 65 - 88[article]Mapping theories of transformative learning / Daniel Casebeer in Cartographica, vol 52 n° 3 (Fall 2017)
[article]
Titre : Mapping theories of transformative learning Type de document : Article/Communication Auteurs : Daniel Casebeer, Auteur ; Jessica Mann, Auteur Année de publication : 2017 Article en page(s) : pp 233 – 237 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage par transformation
[Termes IGN] géographie sociale
[Termes IGN] représentation cartographique
[Termes IGN] segmentation sémantiqueRésumé : (auteur) The purpose of this study is to demonstrate the utility of social cartography for mapping theories of transformative learning. Since the 1980s, several alternative conceptions of transformative learning have emerged to challenge the dominance of Jack Mezirow's psychocritical perspective. Rather than positioning these theories in opposition to one another, this study uses textual analysis and a phenomenographic method to situate them in a heterotopic space where researchers can orient themselves as they encounter new intellectual and representational tasks brought on by the diversification of the field. Whether the map is accepted as a metaphorical curiosity or more as a literal representation, it can reveal perceived or acknowledged theoretical relationships while identifying issues in transformative education that still need to be addressed. Numéro de notice : A2017-734 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3138/cart.52.3.3956 En ligne : https://doi.org/10.3138/cart.52.3.3956 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88373
in Cartographica > vol 52 n° 3 (Fall 2017) . - pp 233 – 237[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 031-2017031 SL Revue Centre de documentation Revues en salle Disponible Recurrent neural networks to correct satellite image classification maps / Emmanuel Maggiori in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
[article]
Titre : Recurrent neural networks to correct satellite image classification maps Type de document : Article/Communication Auteurs : Emmanuel Maggiori, Auteur ; Guillaume Charpiat, Auteur ; Yuliya Tarabalka, Auteur ; Pierre Alliez, Auteur Année de publication : 2017 Article en page(s) : pp 4962 - 4971 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par réseau neuronal
[Termes IGN] convolution (signal)
[Termes IGN] itération
[Termes IGN] réseau neuronal convolutifRésumé : (Auteur) While initially devised for image categorization, convolutional neural networks (CNNs) are being increasingly used for the pixelwise semantic labeling of images. However, the proper nature of the most common CNN architectures makes them good at recognizing but poor at localizing objects precisely. This problem is magnified in the context of aerial and satellite image labeling, where a spatially fine object outlining is of paramount importance. Different iterative enhancement algorithms have been presented in the literature to progressively improve the coarse CNN outputs, seeking to sharpen object boundaries around real image edges. However, one must carefully design, choose, and tune such algorithms. Instead, our goal is to directly learn the iterative process itself. For this, we formulate a generic iterative enhancement process inspired from partial differential equations, and observe that it can be expressed as a recurrent neural network (RNN). Consequently, we train such a network from manually labeled data for our enhancement task. In a series of experiments, we show that our RNN effectively learns an iterative process that significantly improves the quality of satellite image classification maps. Numéro de notice : A2017-659 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2697453 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2697453 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87070
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 4962 - 4971[article]Remote sensing scene classification by unsupervised representation learning / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
[article]
Titre : Remote sensing scene classification by unsupervised representation learning Type de document : Article/Communication Auteurs : Xiaoqiang Lu, Auteur ; Xiangtao Zheng, Auteur ; Yuan Yuan, Auteur Année de publication : 2017 Article en page(s) : pp 5148 - 5157 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage non-dirigé
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] déconvolution
[Termes IGN] image à haute résolution
[Termes IGN] réseau neuronal artificiel
[Termes IGN] scène
[Termes IGN] Sydney (Nouvelle-Galles du Sud)Résumé : (Auteur) With the rapid development of the satellite sensor technology, high spatial resolution remote sensing (HSR) data have attracted extensive attention in military and civilian applications. In order to make full use of these data, remote sensing scene classification becomes an important and necessary precedent task. In this paper, an unsupervised representation learning method is proposed to investigate deconvolution networks for remote sensing scene classification. First, a shallow weighted deconvolution network is utilized to learn a set of feature maps and filters for each image by minimizing the reconstruction error between the input image and the convolution result. The learned feature maps can capture the abundant edge and texture information of high spatial resolution images, which is definitely important for remote sensing images. After that, the spatial pyramid model (SPM) is used to aggregate features at different scales to maintain the spatial layout of HSR image scene. A discriminative representation for HSR image is obtained by combining the proposed weighted deconvolution model and SPM. Finally, the representation vector is input into a support vector machine to finish classification. We apply our method on two challenging HSR image data sets: the UCMerced data set with 21 scene categories and the Sydney data set with seven land-use categories. All the experimental results achieved by the proposed method outperform most state of the arts, which demonstrates the effectiveness of the proposed method. Numéro de notice : A2017-664 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2702596 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2702596 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87103
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5148 - 5157[article]SDE: A novel selective, discriminative and equalizing feature representation for visual recognition / Guo-Sen Xie in International journal of computer vision, vol 124 n° 2 (1 September 2017)PermalinkSIG et intelligence artificielle : quels développements et quel futur ? / Christian Carolin in Géomatique expert, n° 118 (septembre - octobre 2017)PermalinkUnsupervised domain adaptation for early detection of drought stress in hyperspectral images / P. Schmitter in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)PermalinkFrom subpixel to superpixel : a novel fusion framework for hyperspectral image classification / Ting Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkLearning and transferring deep joint spectral–spatial features for hyperspectral classification / Jingxiang Yang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkLearning a discriminative distance metric with label consistency for scene classification / Yuebin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkLearning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks / Shaohui Mei in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkReference data enhancement for geographic information retrieval using linked data / Tiago H. V. M. Moura in Transactions in GIS, vol 21 n° 4 (August 2017)PermalinkA relative evaluation of random forests for land cover mapping in an urban area / Di Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 8 (August 2017)PermalinkSimultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks / Rasha Alshehhi in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkAggregation-based information retrieval system for geospatial data catalogs / Javier Lacasta in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)PermalinkMap the gap: alternative visualisations of geographic knowledge production / Margath Walker in Geo: Geography and Environment, vol 4 n°2 (July 2017)PermalinkA morphologically preserved multi-resolution TIN surface modeling and visualization method for virtual globes / Xianwei Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)PermalinkLes observatoires territoriaux : Des outils de la société de la connaissance ? / Jean Philippe Tonneau in Revue internationale de géomatique, vol 27 n° 3 (juillet-septembre 2017)PermalinkPerSE : visual analytics for calendar related spatiotemporal periodicity detection and analysis / Brian Swedberg in Geoinformatica, vol 21 n° 3 (July - September 2017)PermalinkRobust point cloud classification based on multi-level semantic relationships for urban scenes / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)PermalinkVers un observatoire agro-environnemental des territoires : Un système décisionnel multi-échelle pour le bassin de la Charente / Françoise Vernier in Revue internationale de géomatique, vol 27 n° 3 (juillet-septembre 2017)PermalinkSpatial thinking in archaeology: Is GIS the answer? / Gary Lock in Journal of archaeological science, vol 84 (August 2017)PermalinkAn investigation into challenges experienced when route planning, navigating and wayfinding / Erin Koletsis in International journal of cartography, vol 3 n° 1 (June 2017)PermalinkAutomatic GPS ionospheric amplitude and phase scintillation detectors using a machine learning algorithm / Yu Jiao in Inside GNSS, vol 12 n° 3 (May - June 2017)Permalink