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A local projection-based approach to individual tree detection and 3-D crown delineation in multistoried coniferous forests using high-density airborne LiDAR data / Aravind Harikumar in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 2 (February 2019) . - pp 1168 - 1182 Titre : A local projection-based approach to individual tree detection and 3-D crown delineation in multistoried coniferous forests using high-density airborne LiDAR data Type de document : Article/Communication Auteurs : Aravind Harikumar, Auteur ; Francesca Bovolo, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2019 Article en page(s) : pp 1168 - 1182 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] arbre dominant [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] forêt [Termes descripteurs IGN] houppier [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] modèle numérique de surface de la canopée [Termes descripteurs IGN] pinophyta [Termes descripteurs IGN] projection [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] Trente Résumé : (Auteur) Accurate crown detection and delineation of dominant and subdominant trees are crucial for accurate inventorying of forests at the individual tree level. The state-of-the-art tree detection and crown delineation methods have good performance mostly with dominant trees, whereas exhibits a reduced accuracy when dealing with subdominant trees. In this paper, we propose a novel approach to accurately detect and delineate both the dominant and subdominant tree crowns in conifer-dominated multistoried forests using small footprint high-density airborne Light Detection and Ranging data. Here, 3-D candidate cloud segments delineated using a canopy height model segmentation technique are projected onto a novel 3-D space where both the dominant and subdominant tree crowns can be accurately detected and delineated. Tree crowns are detected using 2-D features derived from the projected data. The delineation of the crown is performed at the voxel level with the help of both the 2-D features and 3-D texture information derived from the cloud segment. The texture information is modeled by using 3-D Gray Level Co-occurrence Matrix. The performance evaluation was done on a set of six circular plots for which reference data are available. The high detection and delineation accuracies obtained over the state of the art prove the performance of the proposed method. Numéro de notice : A2019-112 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2865014 date de publication en ligne : 10/09/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2865014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92452 [article]

An internal crown geometric model for conifer species classification with high-density LiDAR data / Aravind Harikumar in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 5 (May 2017) . - pp 2924 - 2940 Titre : An internal crown geometric model for conifer species classification with high-density LiDAR data Type de document : Article/Communication Auteurs : Aravind Harikumar, Auteur ; Francesca Bovolo, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2017 Article en page(s) : pp 2924 - 2940 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] forêt [Termes descripteurs IGN] houppier [Termes descripteurs IGN] modèle géométrique [Termes descripteurs IGN] pinophyta [Termes descripteurs IGN] structure d'un peuplement forestier [Termes descripteurs IGN] Trente Résumé : (Auteur) The knowledge of the tree species is a crucial information that governs the success of precision forest management practice. High-density small footprint multireturn airborne light detection and ranging (LiDAR) scanning can collect a huge amount of point samples containing structural details of the forest vertical profile, which can reveal important structural information of the forest components. LiDAR data have been successfully used to distinguish between coniferous and deciduous/broadleaved tree species. However, species classification within a class (e.g., the conifer class) using LiDAR data is a challenging problem when considering the tree external crown characteristics only. This paper presents a novel method for conifer species classification based on the use of geometric features describing both the internal and external structures of the crown. The internal crown geometric features (IGFs) are defined based on a novel internal branch structure model, which uses 3-D region growing and principal component analysis to delineate the branch structure of a conifer tree accurately. IGFs are used together with external crown geometric features to perform conifer species classification. Three different support vector machines have been considered for classification performance evaluation. The experimental analysis conducted on high-density LiDAR data acquired over a portion of the Trentino region in Italy proves the effectiveness of the proposed method. Numéro de notice : A2017-471 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2656152 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2656152 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86394 [article]

Unsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images / Sicong Liu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 5 (May 2016) . - pp 2733 - 2748 Titre : Unsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images Type de document : Article/Communication Auteurs : Sicong Liu, Auteur ; Lorenzo Bruzzone, Auteur ; Francesca Bovolo, Auteur ; Peijun Du, Auteur Année de publication : 2016 Article en page(s) : pp 2733 - 2748 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] analyse infrapixellaire [Termes descripteurs IGN] composition spectrale du pixel [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image multitemporelle Résumé : (Auteur) This paper presents a novel multitemporal spectral unmixing (MSU) approach to address the challenging multiple-change detection problem in bitemporal hyperspectral (HS) images. Differently from the state-of-the-art methods that are mainly designed at a pixel level, the proposed technique investigates the spectral-temporal variations at a subpixel level. The considered change detection (CD) problem is analyzed in a multitemporal domain, where a bitemporal spectral mixture model is defined to analyze the spectral composition within a pixel. Distinct multitemporal endmembers (MT-EMs) are extracted according to an automatic and unsupervised technique. Then, a change analysis strategy is designed to distinguish the change and no-change MT-EMs. An endmember-grouping scheme is applied to the changed MT-EMs to detect the unique change classes. Finally, the considered multiple-change detection problem is solved by analyzing the abundances of the change and no-change classes and their contribution to each pixel. The proposed approach has been validated on both simulated and real multitemporal HS data sets presenting multiple changes. Experimental results confirmed the effectiveness of the proposed method. Numéro de notice : A2016-846 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2015.2505183 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82927 [article]

An approach to fine coregistration between very high resolution multispectral images based on registration noise distribution / Youkyung Han in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 12 (December 2015) . - pp 6650 - 6662 Titre : An approach to fine coregistration between very high resolution multispectral images based on registration noise distribution Type de document : Article/Communication Auteurs : Youkyung Han, Auteur ; Francesca Bovolo, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2015 Article en page(s) : pp 6650 - 6662 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] appariement de points [Termes descripteurs IGN] bruit (théorie du signal) [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] raccord d'images [Termes descripteurs IGN] superposition d'images Résumé : (auteur) Even after applying effective coregistration methods, multitemporal images are likely to show a residual misalignment, which is referred to as registration noise (RN). This is because coregistration methods from the literature cannot fully handle the local dissimilarities induced by differences in the acquisition conditions (e.g., the stability of the acquisition platform, the off-nadir angle of the sensor, the structure of the considered scene, etc.). This paper addresses the problem of reducing such a residual misalignment by proposing a fine automatic coregistration approach for very high resolution (VHR) multispectral images. The proposed method takes advantage of the properties of the residual misalignment itself. To this end, RN is first extracted in the change vector analysis (CVA) polar domain according to the behaviors of the specific multitemporal images considered. Then, a local analysis of RN pixels (i.e., those showing residual misalignment) is conducted for automatically extracting control points (CPs) and matching them according to their estimated displacement. Matched CPs are used for generating a deformation map by interpolation. Finally, one VHR image is warped to the coordinates of the other through a deformation map. Experiments carried out on simulated and real multitemporal VHR images confirm the effectiveness of the proposed approach. Numéro de notice : A2015-846 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79196 [article]

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Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images / Sicong Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 8 (August 2015) . - pp 4363 - 4378 Titre : Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images Type de document : Article/Communication Auteurs : Sicong Liu, Auteur ; Lorenzo Bruzzone, Auteur ; Francesca Bovolo, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 4363 - 4378 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] image AVIRIS [Termes descripteurs IGN] image EO1-Hyperion [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] méthode des vecteurs de changement [Termes descripteurs IGN] représentation du changement Résumé : (Auteur) This paper presents an effective semiautomatic method for discovering and detecting multiple changes (i.e., different kinds of changes) in multitemporal hyperspectral (HS) images. Differently from the state-of-the-art techniques, the proposed method is designed to be sensitive to the small spectral variations that can be identified in HS images but usually are not detectable in multispectral images. The method is based on the proposed sequential spectral change vector analysis, which exploits an iterative hierarchical scheme that at each iteration discovers and identifies a subset of changes. The approach is interactive and semiautomatic and allows one to study in detail the structure of changes hidden in the variations of the spectral signatures according to a top-down procedure. A novel 2-D adaptive spectral change vector representation (ASCVR) is proposed to visualize the changes. At each level this representation is optimized by an automatic definition of a reference vector that emphasizes the discrimination of changes. Finally, an interactive manual change identification is applied for extracting changes in the ASCVR domain. The proposed approach has been tested on three hyperspectral data sets, including both simulated and real multitemporal images showing multiple-change detection problems. Experimental results confirmed the effectiveness of the proposed method. Numéro de notice : A2015-385 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76861 [article]

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An adaptive semisupervised approach to the detection of user-defined recurrent changes in image time series / Daniel Zanotta in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)

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Hierarchical unsupervised change detection in multitemporal hyperspectral images / S. Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)

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A hierarchical approach to change detection in very high resolution SAR images for surveillance applications / Francesca Bovolo in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 1 (April 2013)

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Updating land-cover maps by classification of image time series: A novel change-detection-driven transfer learning approach / B. Demir ; Francesca Bovolo ; Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)

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A framework for automatic and unsupervised detection of multiple changes in multitemporal images / Francesca Bovolo in IEEE Transactions on geoscience and remote sensing, vol 50 n° 6 (June 2012)

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Semisupervised one-class support vector machine for classification of remote sensing data / Jordi Munoz-Mari in IEEE Transactions on geoscience and remote sensing, vol 48 n° 8 (August 2010)

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