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Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space / Cletah Shoko in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 120 (october 2016) . - pp 13 - 24 Titre : Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space Type de document : Article/Communication Auteurs : Cletah Shoko, Auteur ; Onisimo Mutanga, Auteur ; Timothy Dube, Auteur Année de publication : 2016 Article en page(s) : pp 13 - 24 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] dioxyde de carbone [Termes IGN] herbe [Termes IGN] sursol [Termes IGN] surveillance écologique [Termes IGN] teneur en carbone Résumé : (Auteur) The remote sensing of grass aboveground biomass (AGB) has gained considerable attention, with substantial research being conducted in the past decades. Of significant importance is their photosynthetic pathways (C3 and C4), which epitomizes a fundamental eco-physiological distinction of grasses functional types. With advances in technology and the availability of remotely sensed data at different spatial, spectral, radiometric and temporal resolutions, coupled with the need for detailed information on vegetation condition, the monitoring of C3 and C4 grasses AGB has received renewed attention, especially in the light of global climate change, biodiversity and, most importantly, food security. This paper provides a detailed survey on the progress of remote sensing application in determining C3 and C4 grass species AGB. Importantly, the importance of species functional type is highlighted in conjunction with the availability and applicability of different remote sensing datasets, with refined resolutions, which provide an opportunity to monitor C3 and C4 grasses AGB. While some progress has been made, this review has revealed the need for further remote sensing studies to model the seasonal (cyclical) variability, as well as long-term AGB changes in C3 and C4 grasses, in the face of climate change and food security. Moreover, the findings of this study have shown the significance of shifting towards the application of advanced statistical models, to further improve C3 and C4 grasses AGB estimation accuracy. Numéro de notice : A2016-794 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.08.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.08.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82528 [article]

The D-FCM partitioned D-BSP tree for massive point cloud data access and rendering / Yi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 120 (october 2016) . - pp 25 - 36 Titre : The D-FCM partitioned D-BSP tree for massive point cloud data access and rendering Type de document : Article/Communication Auteurs : Yi Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 25 - 36 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse en composantes principales [Termes IGN] arbre BSP [Termes IGN] classification floue [Termes IGN] densité des points [Termes IGN] semis de points [Termes IGN] traitement de semis de points [Termes IGN] valeur propre Résumé : (Auteur) The spatial partitioning of massive point cloud data involves dividing the space into a multi-tree structure step by step, so as to achieve the purpose of fast access and to render the point cloud. The current methods are based on spatial regularity and equal division, which is not consistent with the irregular and non-uniform distribution of most point clouds. This paper presents a directional fuzzy c-means (D-FCM) method for irregular spatial partitioning. The distance metric is weighted by a direction coefficient, which is determined by the eigenvalue of the point cloud. The orientation of each node is adaptively calculated by principal component analysis of the point cloud, and Karhunen-Loeve (KL) transform is applied to the points coordinates in node. A binary space partitioning (BSP) tree structure is used to partition the point cloud data node by node, and a directional BSP (D-BSP) tree is formed. The D-BSP tree structure was tested with point clouds of 0.1 million to over 2 billion points (up to 60 GB). The experimental results showed that the D-BSP tree can ensure that the bounding boxes are close to the actual spatial distribution of the point cloud, it can completely expand along the spatial configuration of the point cloud without generating unnecessary partitioning, and it can achieve a higher rendering speed with less memory requirement. Numéro de notice : A2016-795 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.08.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.08.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82529 [article]

Disaster debris estimation using high-resolution polarimetric stereo-SAR / Christian N. Koyama in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 120 (october 2016) . - pp 84 - 98 Titre : Disaster debris estimation using high-resolution polarimetric stereo-SAR Type de document : Article/Communication Auteurs : Christian N. Koyama, Auteur ; Hideomi Gokon, Auteur ; Masaru Jimbo, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 84 - 98 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] catastrophe naturelle [Termes IGN] déchet [Termes IGN] estimation statistique [Termes IGN] hauteur (coordonnée) [Termes IGN] image radar moirée [Termes IGN] Japon [Termes IGN] modèle stéréoscopique [Termes IGN] séisme [Termes IGN] volume (grandeur) Résumé : (Auteur) This paper addresses the problem of debris estimation which is one of the most important initial challenges in the wake of a disaster like the Great East Japan Earthquake and Tsunami. Reasonable estimates of the debris have to be made available to decision makers as quickly as possible. Current approaches to obtain this information are far from being optimal as they usually rely on manual interpretation of optical imagery. We have developed a novel approach for the estimation of tsunami debris pile heights and volumes for improved emergency response. The method is based on a stereo-synthetic aperture radar (stereo-SAR) approach for very high-resolution polarimetric SAR. An advanced gradient-based optical-flow estimation technique is applied for optimal image coregistration of the low-coherence non-interferometric data resulting from the illumination from opposite directions and in different polarizations. By applying model based decomposition of the coherency matrix, only the odd bounce scattering contributions are used to optimize echo time computation. The method exclusively considers the relative height differences from the top of the piles to their base to achieve a very fine resolution in height estimation. To define the base, a reference point on non-debris-covered ground surface is located adjacent to the debris pile targets by exploiting the polarimetric scattering information. The proposed technique is validated using in situ data of real tsunami debris taken on a temporary debris management site in the tsunami affected area near Sendai city, Japan. The estimated height error is smaller than 0.6 m RMSE. The good quality of derived pile heights allows for a voxel-based estimation of debris volumes with a RMSE of 1099 m3. Advantages of the proposed method are fast computation time, and robust height and volume estimation of debris piles without the need for pre-event data or auxiliary information like DEM, topographic maps or GCPs. Numéro de notice : A2016-796 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.08.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.08.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82530 [article]

Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning / Xiaorui Ma in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 120 (october 2016) . - pp 99 - 107 Titre : Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning Type de document : Article/Communication Auteurs : Xiaorui Ma, Auteur ; Hongyu Wang, Auteur ; Jie Wang, Auteur Année de publication : 2016 Article en page(s) : pp 99 - 107 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] apprentissage semi-dirigé [Termes IGN] classification par réseau neuronal [Termes IGN] classification semi-dirigée [Termes IGN] image hyperspectrale [Termes IGN] pondération Résumé : (Auteur) Semisupervised learning is widely used in hyperspectral image classification to deal with the limited training samples, however, some more information of hyperspectral image should be further explored. In this paper, a novel semisupervised classification based on multi-decision labeling and deep feature learning is presented to exploit and utilize as much information as possible to realize the classification task. First, the proposed method takes two decisions to pre-label each unlabeled sample: local decision based on weighted neighborhood information is made by the surrounding samples, and global decision based on deep learning is performed by the most similar training samples. Then, some unlabeled ones with high confidence are selected to extent the training set. Finally, self decision, which depends on the self features exploited by deep learning, is employed on the updated training set to extract spectral-spatial features and produce classification map. Experimental results with real data indicate that it is an effective and promising semisupervised classification method for hyperspectral image. Numéro de notice : A2016-797 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.09.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.09.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82532 [article]