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Classification of hyperspectral and LiDAR data using coupled CNNs / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 7 (July 2020) . - pp 4939 - 4950 Titre : Classification of hyperspectral and LiDAR data using coupled CNNs Type de document : Article/Communication Auteurs : Renlong Hang, Auteur ; Zhu Li, Auteur ; Pedram Ghamisi, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 4939 - 4950 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données hétérogènes [Termes IGN] données lidar [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion de données [Termes IGN] Houston (Texas) [Termes IGN] image hyperspectrale [Termes IGN] occupation du sol [Termes IGN] Perceptron multicouche [Termes IGN] précision de la classification [Termes IGN] semis de points [Termes IGN] Trente [Termes IGN] utilisation du sol Résumé : (auteur) In this article, we propose an efficient and effective framework to fuse hyperspectral and light detection and ranging (LiDAR) data using two coupled convolutional neural networks (CNNs). One CNN is designed to learn spectral–spatial features from hyperspectral data, and the other one is used to capture the elevation information from LiDAR data. Both of them consist of three convolutional layers, and the last two convolutional layers are coupled together via a parameter-sharing strategy. In the fusion phase, feature-level and decision-level fusion methods are simultaneously used to integrate these heterogeneous features sufficiently. For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy. For the decision-level fusion, a weighted summation strategy is adopted, where the weights are determined by the classification accuracy of each output. The proposed model is evaluated on an urban data set acquired over Houston, USA, and a rural one captured over Trento, Italy. On the Houston data, our model can achieve a new record overall accuracy (OA) of 96.03%. On the Trento data, it achieves an OA of 99.12%. These results sufficiently certify the effectiveness of our proposed model. Numéro de notice : A2020-391 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2969024 Date de publication en ligne : 06/02/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2969024 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95374 [article]

Integrating dendrochronology and geomatics to monitor natural hazards and landscape changes / Marco Ciolli in Applied geomatics, vol 11 n° 1 (March 2019)  

Public [article]  inApplied geomatics > vol 11 n° 1 (March 2019) . - pp 39 - 52 Titre : Integrating dendrochronology and geomatics to monitor natural hazards and landscape changes Type de document : Article/Communication Auteurs : Marco Ciolli, Auteur ; Marco Bezzi, Auteur ; Giovanni Comunello, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 39 - 52 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] Albanie [Termes IGN] analyse diachronique [Termes IGN] avalanche [Termes IGN] couvert forestier [Termes IGN] dendrochronologie [Termes IGN] forêt [Termes IGN] GRASS [Termes IGN] IDRISI [Termes IGN] lit majeur [Termes IGN] Pinus sylvestris [Termes IGN] positionnement par GPS [Termes IGN] QGIS [Termes IGN] reboisement [Termes IGN] risque naturel [Termes IGN] Trente Résumé : (Auteur) The monitoring of natural hazards is of extreme importance in the areas of Italy where there are high hydrogeological and avalanche risks. Despite the fact that records of past events are sometimes available, some of their data are often incomplete and show that the monitoring and mapping of these phenomena are never enough to avoid damage. We present the results of different studies where an integrated approach has been used by combining geomatics and dendrochronology techniques. In particular, we refer to case studies concerning avalanches, debris flows, natural reforestation in Italy and riverbed path changes in Albania. The position of all the plants sampled for dendrochronology was taken by GPS (Global Positioning System). The cartographic information used in these studies was provided by official sources from public organisations or processed by extracting them from aerial photographs or satellite imagery. With the Geographic Information System, it was possible to spatialise and analyse the information from dendrochronological sampling through the creation of multi-temporal morphological and potential risk maps showing the effects of the phenomena on forest cover. The GIS software used in these studies are GRASS, QGIS and IDRISI. The results showed that avalanches, debris flow, riverbed and landscape change can be studied effectively by integrating geomatics and dendrochronological techniques. This integration enabled spatial and temporal modelling, including the reconstruction of paths and volumes of past phenomena. The analysis of growth disturbances over time also enabled the reconstruction of the frequency of avalanches and debris flow activity over the last 50 years and, in some areas, over the last century. A detailed analysis of one of the avalanche tracks provided interesting results regarding the reconstruction of avalanche dynamics. Analysis of scars on buried stems of Pinus sylvestris also provided interesting results in terms of debris volume estimation. The dendrochronological reconstruction of the patterns of natural reforestation led to the determination of forest expansion rates that were used for modelling future scenarios and refining the changes of river morphology. Dendrochronology strongly improved the results of GIS satellite imagery analysis. These reconstructions are particularly important for the areas that are more exposed to the direct risk of avalanches, debris flows and floods in order to prevent the consequences of such phenomena in a changing climate. Numéro de notice : A2019-160 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-018-0236-0 Date de publication en ligne : 21/08/2018 En ligne : https://doi.org/10.1007/s12518-018-0236-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92521 [article]

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 IGN] arbre dominant [Termes IGN] détection d'arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] forêt [Termes IGN] houppier [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] Pinophyta [Termes IGN] projection [Termes IGN] segmentation [Termes 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 Affiliation des auteurs : non IGN 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]

Fusion of hyperspectral and LiDAR data using sparse and low-rank component analysis / Behnood Rasti in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6354 - 6365 Titre : Fusion of hyperspectral and LiDAR data using sparse and low-rank component analysis Type de document : Article/Communication Auteurs : Behnood Rasti, Auteur ; Pedram Ghamisi, Auteur ; Javier Plaza, Auteur Année de publication : 2017 Article en page(s) : pp 6354 - 6365 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse en composantes principales [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] fusion de données [Termes IGN] Houston (Texas) [Termes IGN] image hyperspectrale [Termes IGN] Trente Résumé : (Auteur) The availability of diverse data captured over the same region makes it possible to develop multisensor data fusion techniques to further improve the discrimination ability of classifiers. In this paper, a new sparse and low-rank technique is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR)-derived features. The proposed fusion technique consists of two main steps. First, extinction profiles are used to extract spatial and elevation information from hyperspectral and LiDAR data, respectively. Then, the sparse and low-rank technique is utilized to estimate the low-rank fused features from the extracted ones that are eventually used to produce a final classification map. The proposed approach is evaluated over an urban data set captured over Houston, USA, and a rural one captured over Trento, Italy. Experimental results confirm that the proposed fusion technique outperforms the other techniques used in the experiments based on the classification accuracies obtained by random forest and support vector machine classifiers. Moreover, the proposed approach can effectively classify joint LiDAR and hyperspectral data in an ill-posed situation when only a limited number of training samples are available. Numéro de notice : A2017-748 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2726901 En ligne : https://doi.org/10.1109/TGRS.2017.2726901 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88783 [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 IGN] analyse en composantes principales [Termes IGN] classification dirigée [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt [Termes IGN] houppier [Termes IGN] modèle de croissance végétale [Termes IGN] modèle géométrique [Termes IGN] Pinophyta [Termes IGN] structure d'un peuplement forestier [Termes 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 Affiliation des auteurs : non IGN 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]

Energy planning tools and CityGML-based 3D virtual city models: experiences from Trento (Italy) / Giorgio Agugiaro in Applied geomatics, vol 8 n° 1 (March 2016)  

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Radiometric and geometric evaluation of GeoEye-1, WorldView-2 and Pléiades-1A stereo images for 3D information extraction / Daniela Poli in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)  

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Fusion of multi-spectral SPOT-5 images and very high resolution texture information extracted from digital orthophotos for automatic classification of complex Alpine areas / C. Mariz in International Journal of Remote Sensing IJRS, vol 30 n°11-12 (June 2009)  

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