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vol 145 - part A - November 2018 - Deep Learning RS Data (Bulletin de ISPRS Journal of photogrammetry and remote sensing) / Giorgos Mountrakis  

Public [n° ou bulletin]  est un bulletin de ISPRS Journal of photogrammetry and remote sensing / International society for photogrammetry and remote sensing (1990 - )   Titre : vol 145 - part A - November 2018 - Deep Learning RS Data Type de document : Périodique Auteurs : Giorgos Mountrakis, Editeur scientifique Année de publication : 2018 Langues : Anglais (eng) Numéro de notice : 081-201811 Affiliation des auteurs : non IGN Nature : Numéro de périodique En ligne : https://www.sciencedirect.com/journal/isprs-journal-of-photogrammetry-and-remote [...] Format de la ressource électronique : URL Bulletin Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=31347 [n° ou bulletin]

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• Multi-scale object detection in remote sensing imagery with convolutional neural networks / Zhipeng Deng in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)  
• A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification / Wei Han in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)  
• Semantic labeling in very high resolution images via a self-cascaded convolutional neural network / Yoncheng Liu in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)  
• Land cover mapping at very high resolution with rotation equivariant CNNs : Towards small yet accurate models / Diego Marcos in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)  
• A new deep convolutional neural network for fast hyperspectral image classification / Mercedes Eugenia Paoletti in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)  
• Pan-sharpening via deep metric learning / Yinghui Xing in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)  

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Relative importance analysis of Landsat, waveform LIDAR and PALSAR inputs for deciduous biomass estimation / Alyssa Endres in European journal of remote sensing, vol 49 (2016)  

Public [article]  inEuropean journal of remote sensing > vol 49 (2016) . - pp 795 - 807 Titre : Relative importance analysis of Landsat, waveform LIDAR and PALSAR inputs for deciduous biomass estimation Type de document : Article/Communication Auteurs : Alyssa Endres, Auteur ; Giorgos Mountrakis, Auteur ; Huiran Jin, Auteur ; Wei Zhuang, Auteur ; Ioannis Manakos, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 795 - 807 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] biomasse aérienne [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation statistique [Termes IGN] feuillu [Termes IGN] fusion de données [Termes IGN] image ALOS-PALSAR [Termes IGN] image Landsat Résumé : (auteur) Aboveground forest biomass estimation is an integral component for climate change, carbon stocks assessment, biodiversity and forest health. LiDAR (Light Detection And Ranging), specifically NASA’s Laser Vegetation Imaging Sensor (LVIS), PALSAR (Phased Array type L-band Synthetic Aperture Radar), and Landsat data have been previously used in biomass estimation with promising results when used individually. In this manuscript, all three products are jointly utilized for the first time to assess their importance for deciduous biomass estimation. Results indicate that LVIS inputs are ranked as most important followed by PALSAR inputs. Particularly for PALSAR, scenes acquired in May and August were ranked higher compared to other months. Numéro de notice : A2016-827 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article En ligne : http://dx.doi.org/10.5721/EuJRS20164942 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82707 [article]

An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data / Wei Zhuang in ISPRS Journal of photogrammetry and remote sensing, vol 95 (September 2014)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 95 (September 2014) . - pp 81 – 92 Titre : An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data Type de document : Article/Communication Auteurs : Wei Zhuang, Auteur ; Giorgos Mountrakis, Auteur Année de publication : 2014 Article en page(s) : pp 81 – 92 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] empreinte [Termes IGN] filtrage numérique d'image [Termes IGN] forêt [Termes IGN] forme d'onde [Termes IGN] groupe [Termes IGN] identification automatique [Termes IGN] onde lidar [Termes IGN] surface du sol [Termes IGN] télémétrie laser aéroporté [Termes IGN] traitement de données Résumé : (Auteur) Large footprint waveform LiDAR sensors have been widely used for numerous airborne studies. Ground peak identification in a large footprint waveform is a significant bottleneck in exploring full usage of the waveform datasets. In the current study, an accurate and computationally efficient algorithm was developed for ground peak identification, called Filtering and Clustering Algorithm (FICA). The method was evaluated on Land, Vegetation, and Ice Sensor (LVIS) waveform datasets acquired over Central NY. FICA incorporates a set of multi-scale second derivative filters and a k-means clustering algorithm in order to avoid detecting false ground peaks. FICA was tested in five different land cover types (deciduous trees, coniferous trees, shrub, grass and developed area) and showed more accurate results when compared to existing algorithms. More specifically, compared with Gaussian decomposition, the RMSE ground peak identification by FICA was 2.82 m (5.29 m for GD) in deciduous plots, 3.25 m (4.57 m for GD) in coniferous plots, 2.63 m (2.83 m for GD) in shrub plots, 0.82 m (0.93 m for GD) in grass plots, and 0.70 m (0.51 m for GD) in plots of developed areas. FICA performance was also relatively consistent under various slope and canopy coverage (CC) conditions. In addition, FICA showed better computational efficiency compared to existing methods. FICA’s major computational and accuracy advantage is a result of the adopted multi-scale signal processing procedures that concentrate on local portions of the signal as opposed to the Gaussian decomposition that uses a curve-fitting strategy applied in the entire signal. The FICA algorithm is a good candidate for large-scale implementation on future space-borne waveform LiDAR sensors. Numéro de notice : A2014-474 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74051 [article]

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Assessing reference dataset representativeness through confidence metrics based on information density / Giorgos Mountrakis in ISPRS Journal of photogrammetry and remote sensing, vol 78 (April 2013)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 78 (April 2013) . - pp 129 - 147 Titre : Assessing reference dataset representativeness through confidence metrics based on information density Type de document : Article/Communication Auteurs : Giorgos Mountrakis, Auteur ; Bo Xi, Auteur Année de publication : 2013 Article en page(s) : pp 129 - 147 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse de sensibilité [Termes IGN] carte d'occupation du sol [Termes IGN] carte de confiance [Termes IGN] classification dirigée [Termes IGN] densité d'information [Termes IGN] données localisées de référence [Termes IGN] jeu de données localisées [Termes IGN] representativité Résumé : (Auteur) Land cover maps obtained from classification of remotely sensed imagery provide valuable information in numerous environmental monitoring and modeling tasks. However, many uncertainties and errors can directly or indirectly affect the quality of derived maps. This work focuses on one key aspect of the supervised classification process of remotely sensed imagery: the quality of the reference dataset used to develop a classifier. More specifically, the representative power of the reference dataset is assessed by contrasting it with the full dataset (e.g. entire image) needing classification. Our method is applicable in several ways: training or testing datasets (extracted from the reference dataset) can be compared with the full dataset. The proposed method moves beyond spatial sampling schemes (e.g. grid, cluster) and operates in the multidimensional feature space (e.g. spectral bands) and uses spatial statistics to compare information density of data to be classified with data used in the reference process. The working hypothesis is that higher information density, not in general but with respect to the entire classified image, expresses higher confidence in obtained results. Presented experiments establish a close link between confidence metrics and classification accuracy for a variety of image classifiers namely maximum likelihood, decision tree, Backpropagation Neural Network and Support Vector Machine. A sensitivity analysis demonstrates that spatially-continuous reference datasets (e.g. a square window) have the potential to provide similar classification confidence as typically-used spatially-random datasets. This is an important finding considering the higher acquisition costs for randomly distributed datasets. Furthermore, the method produces confidence maps that allow spatially-explicit comparison of confidence metrics within a given image for identification of over- and under-represented image portions. The current method is presented for individual image classification but, with sufficient evaluation from the remote sensing community it has the potential to become a standard for reference dataset reporting and thus allowing users to assess representativeness of reference datasets in a consistent manner across different classification tasks. Numéro de notice : A2013-183 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32321 [article]

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Multi-scale spatiotemporal analyses of moose-vehicle collisions: a case study in northern Vermont / Giorgos Mountrakis in International journal of geographical information science IJGIS, vol 23 n°11-12 (november 2009)

Public [article]  inInternational journal of geographical information science IJGIS > vol 23 n°11-12 (november 2009) . - pp 1389 - 1412 Titre : Multi-scale spatiotemporal analyses of moose-vehicle collisions: a case study in northern Vermont Type de document : Article/Communication Auteurs : Giorgos Mountrakis, Auteur ; K. Gunson, Auteur Année de publication : 2009 Article en page(s) : pp 1389 - 1412 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] accident de la route [Termes IGN] analyse spatio-temporelle [Termes IGN] Cervidae [Termes IGN] estimation par noyau [Termes IGN] Mammalia [Termes IGN] outil d'aide à la décision [Termes IGN] trafic routier [Termes IGN] véhicule automobile [Termes IGN] Vermont (Etats-Unis) Résumé : (Auteur) Moose-vehicle collisions (MVCs) pose a serious safety and environmental concern in many regions of Europe and North America. For example, in the state of Vermont, one-third of all reported MVCs resulted in motorist injury or fatality while collisions have increased from two in 1982 to 164 in 2002. Our work used a MVC dataset from 1983 to 1999 in the Northeastern Highlands of Vermont (four major roads) to perform space, time and spatiotemporal analyses and guide future mitigation strategies. An adapted kernel density estimator was implemented for exploratory analyses to detect high density collision hotspots on roads. The kernel in space showed seven major density peaks which varied in magnitude and spread between roads. The kernel estimator in time for all roads showed an exponentially increasing trend with annual periodicity and a seasonal cyclic component, where the majority of collisions occurred from May to October. Spatiotemporal kernel estimation exhibited discontinuous density hotspots in time and space suggesting changing animal movement patterns across roads. We used an adapted Ripley's K-function to test the hypothesis that MVCs clustering occurred at multiple scales in space, in time and in space-time combined. Statistically significant spatial clustering was evident on all roads at spatial scales from 2 to 10 km. A more consistent clustering in time occurred on all roads at a scale distance of 5 years. Similar to the kernel estimation, annual periodicity was also evident. Positive space-time clustering was present at small spatial (5 km) and temporal scales (2 years) indicating that where MVCs occur is also influenced by when they occur. In retrospect, using multiple road lengths, and the combined kernel estimation and Ripley's K-function in time and space, provided a powerful methodology to study varying spatiotemporal patterns of wildlife collisions along roads. This can greatly assist transportation planners in identifying optimal mitigation strategies along specific roads, such as deciding on location and spatial length for permanent and expensive measures (e.g. crossing structures and associated fencing) versus less permanent and inexpensive structures (e.g. wildlife signage and reduced speed limits). Copyright Taylor & Francis Numéro de notice : A2009-513 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30142 [article]

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Developing collaborative classifiers using an Expert-based Model / Giorgos Mountrakis in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 7 (July 2009)

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Supporting quality-based image retrieval through user preference learning / Giorgos Mountrakis in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 8 (August 2004)

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