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A novel sharpening approach for superresolving multiresolution optical images / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1545 - 1560 Titre : A novel sharpening approach for superresolving multiresolution optical images Type de document : Article/Communication Auteurs : Claudia Paris, Auteur ; José Bioucas-Dias, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2019 Article en page(s) : pp 1545 - 1560 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] filtrage du bruit [Termes IGN] image multibande [Termes IGN] image Sentinel-MSI [Termes IGN] problème inverse Résumé : (Auteur) This paper aims to provide a compact superresolution formulation specific for multispectral (MS) multiresolution optical data, i.e., images characterized by different scales across different spectral bands. The proposed method, named multiresolution sharpening approach (MuSA), relies on the solution of an optimization problem tailored to the properties of those images. The superresolution problem is formulated as the minimization of an objective function containing a data-fitting term that models the blurs and downsamplings of the different bands and a patch-based regularizer that promotes image self-similarity guided by the geometric details provided by the high-resolution bands. By exploiting the approximately low-rank property of the MS data, the ill-posedness of the inverse problem in hand is strongly reduced, thus sharply improving its conditioning. The state-of-the-art color block-matching and 3D filtering (C-BM3D) image denoiser is used as a patch-based regularizer by leveraging the “plug-and-play” framework: the denoiser is plugged into the iterations of the alternating direction method of multipliers. The main novelties of the proposed method are: 1) the introduction of an observation model tailored to the specific properties of (MS) multiresolution images and 2) the exploitation of the high-spatial-resolution bands to guide the grouping step in the color block-matching and 3D filtering (C-BM3D) denoiser, which constitutes a form of regularization learned from the high-resolution channels. The results obtained on the real and synthetic Sentinel 2 data sets give an evidence of the effectiveness of the proposed approach. Numéro de notice : A2019-129 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2867284 Date de publication en ligne : 26/09/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2867284 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92458 [article]

A growth-model-driven technique for tree stem diameter estimation by using airborne LiDAR data / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 1 (January 2019) . - pp 76 - 92 Titre : A growth-model-driven technique for tree stem diameter estimation by using airborne LiDAR data Type de document : Article/Communication Auteurs : Claudia Paris, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2019 Article en page(s) : pp 76 - 92 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Alpes [Termes IGN] analyse discriminante [Termes IGN] croissance des arbres [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] modèle de croissance végétale [Termes IGN] Pinophyta [Termes IGN] régression [Termes IGN] structure d'un peuplement forestier Résumé : (Auteur) Diameter at breast height (DBH) is one of the most important tree parameter for forest inventory. In this paper, we present a novel method for the adaptive and the accurate DBH estimation of trees characterized by small and large stems. The method automatically discriminates among different tree growth models by means of a data-driven technique based on a clustering procedure. First, the method detects young trees belonging to the lowest forest layer by simply considering the vertical structure of the forest. Then, different clusters of mature trees that are expected to share the same growth-model are identified by analyzing the environmental factors that can affect the stem expansion (e.g., topography and forest density). For each detected growth-model cluster, a tailored regression analysis is performed to obtain accurate DBH estimation results. Experiments have been carried out in an homogeneous coniferous forest located in the Alpine mountainous scenario characterized by a complex topography and a wide range of soil fertility. The method was tested on two data sets characterized by different light detection and ranging (LiDAR) point densities and different forest properties. The results obtained demonstrate the effectiveness of having multiple regression models adapted to the different growth models. Numéro de notice : A2019-103 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2852364 Date de publication en ligne : 07/08/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2852364 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92409 [article]

A novel automatic method for the fusion of ALS and TLS LiDAR data for robust assessment of tree crown structure / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 7 (July 2017) . - pp 3679 - 3693 Titre : A novel automatic method for the fusion of ALS and TLS LiDAR data for robust assessment of tree crown structure Type de document : Article/Communication Auteurs : Claudia Paris, Auteur ; David Kelbe, Auteur ; Jan Van Aardt, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2017 Article en page(s) : pp 3679 - 3693 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] canopée [Termes IGN] corrélation croisée normalisée [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion d'images [Termes IGN] semis de points [Termes IGN] télémétrie laser aéroporté [Termes IGN] télémétrie laser sur satellite Résumé : (Auteur) Tree crown structural parameters are key inputs to studies spanning forest fire propagation, invasive species dynamics, avian habitat provision, and so on, but these parameters consistently are difficult to measure. While airborne laser scanning (ALS) provides uniform data and a consistent nadir perspective necessary for crown segmentation, the data characteristics of terrestrial laser scanning (TLS) make such crown segmentation efforts much more challenging. We present a data fusion approach to extract crown structure from TLS, by exploiting the complementary perspective of ALS. Multiple TLS point clouds are automatically registered to a single ALS point cloud by maximizing the normalized cross correlation between the global ALS canopy height model (CHM) and each of the local TLS CHMs through parameter optimization of a planar Euclidean transform. Per-tree canopy segmentation boundaries, which are reliably obtained from ALS, can then be adapted onto the more irregular TLS data. This is repeated for each TLS scan; the combined segmentation results from each registered TLS scan and the ALS data are fused into a single per-tree point cloud, from which canopy-level structural parameters readily can be extracted. Numéro de notice : A2017-485 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2675963 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2675963 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86407 [article]

A hierarchical approach to three-dimensional segmentation of LiDAR data at single-tree level in a multilayered forest / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 7 (July 2016) . - pp 4190 - 4203 Titre : A hierarchical approach to three-dimensional segmentation of LiDAR data at single-tree level in a multilayered forest Type de document : Article/Communication Auteurs : Claudia Paris, Auteur ; Davide Valduga, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2016 Article en page(s) : pp 4190 - 4203 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] arbre remarquable [Termes IGN] canopée [Termes IGN] données lidar [Termes IGN] exploration de données [Termes IGN] forêt [Termes IGN] hauteur de la végétation [Termes IGN] regroupement de données [Termes IGN] semis de points Résumé : (Auteur) Small-footprint high-density LiDAR data provide information on both the dominant and the subdominant layers of the forest. However, tree detection is usually carried out in the Canopy Height Model (CHM) image domain, where not all the dominant trees are distinguishable and the understory vegetation is not visible. To address these issues, we propose a novel method that integrates the analysis of the CHM with that of the point cloud space (PCS) to 1) improve the accuracy in the detection and delineation of the dominant trees and 2) identify and delineate the subdominant trees. By means of a derivative analysis of the horizontal profile of the forest, the method detects the missed crowns and delineates the crown boundaries directly in the PCS. Then, for each segmented crown, the vertical profile is analyzed to identify the presence of subcanopies and extract them. The proposed method does not require any prior knowledge on the stand properties (e.g., crown size and forest density). Experimental results obtained on two LiDAR data sets characterized by different laser point density show that the proposed method always improved the detection rate compared to other state-of-the-art techniques. It correctly detected 97% and 92% of the dominant trees measured in situ in high- and low-density LiDAR data, respectively. Moreover, it automatically identified 77% of the subdominant trees manually extracted by an expert operator in the high-density LiDAR data. Numéro de notice : A2016-881 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2538203 En ligne : https://doi.org/10.1109/TGRS.2016.2538203 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83044 [article]

A three-dimensional model-based approach to the estimation of the tree top height by fusing low-density LiDAR data and very high resolution optical images / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 1 (January 2015) . - pp 467 - 480 Titre : A three-dimensional model-based approach to the estimation of the tree top height by fusing low-density LiDAR data and very high resolution optical images Type de document : Article/Communication Auteurs : Claudia Paris, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2015 Article en page(s) : pp 467 - 480 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] arbre (flore) [Termes IGN] données lidar [Termes IGN] fusion d'images [Termes IGN] hauteur des arbres [Termes IGN] image optique [Termes IGN] modélisation 3D Résumé : (Auteur) Light detection and ranging (LiDAR) technology has been extensively used for estimating forest attributes. Although high-spatial-density LiDAR data can be used to accurately derive attributes at single tree level, low-density LiDAR data are usually acquired for reducing the cost. However, a low density strongly affects the estimation accuracy due to the underestimation of the tree top and the possible loss of crowns that are not hit by any LiDAR point. In this paper, we propose a 3-D model-based approach to the estimation of the tree top height based on the fusion between low-density LiDAR data and high-resolution optical images. In the proposed approach, the integration of the two remotely sensed data sources is first exploited to accurately detect and delineate the single tree crowns. Then, the LiDAR vertical measures are associated to those crowns hit by at least one LiDAR point and used together with the radius of the crown and the tree apex location derived from the optical image for reconstructing the tree top height by a properly defined parametric model. For the remaining crowns detected only in the optical image, we reconstruct the tree top height by proposing a k-nearest neighbor trees technique that estimates the height of the missed trees as the average of the k reconstructed height values of the trees having most similar crown properties. The proposed technique has been tested on a coniferous forest located in the Italian Alps. The experimental results confirmed the effectiveness of the proposed method. Numéro de notice : A2015-035 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2324016 En ligne : https://doi.org/10.1109/TGRS.2014.2324016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75117 [article]

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