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Improved salient feature-based approach for automatically separating photosynthetic and nonphotosynthetic components within terrestrial Lidar point cloud data of forest canopies / Lixia Ma in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 679 - 696 Titre : Improved salient feature-based approach for automatically separating photosynthetic and nonphotosynthetic components within terrestrial Lidar point cloud data of forest canopies Type de document : Article/Communication Auteurs : Lixia Ma, Auteur ; Guang Zheng, Auteur ; Jan U.H. Eitel, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 679 - 696 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse en composantes indépendantes [Termes IGN] canopée [Termes IGN] classification automatique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] feuillu [Termes IGN] Leaf Area Index [Termes IGN] photosynthèse [Termes IGN] Pinophyta [Termes IGN] reconnaissance de formes [Termes IGN] semis de points [Termes IGN] télémétrie laser sur satellite [Termes IGN] zone saillante 3D Résumé : (Auteur) Accurate separation of photosynthetic and nonphotosynthetic components in a forest canopy from 3-D terrestrial laser scanning (TLS) data is a challenging but of key importance to understand the spatial distribution of the radiation regime, photosynthetic processes, and carbon and water exchanges of the forest canopy. The objective of this paper was to improve current methods for separating photosynthetic and nonphotosynthetic components in TLS data of forest canopies by adding two additional filters only based on its geometric information. By comparing the proposed approach with the eigenvalues plus color information-based method, we found that the proposed approach could effectively improve the overall producer's accuracy from 62.12% to 95.45%, and the overall classification producer's accuracy would increase from 84.28% to 97.80% as the forest leaf area index (LAI) decreases from 4.15 to 3.13. In addition, variations in tree species had negligible effects on the final classification accuracy, as shown by the overall producer's accuracy for coniferous (93.09%) and broadleaf (94.96%) trees. To remove quantitatively the effects of the woody materials in a forest canopy for improving TLS-based LAI estimates, we also computed the “woody-to-total area ratio” based on the classified linear class points from an individual tree. Automatic classification of the forest point cloud data set will facilitate the application of TLS on retrieving 3-D forest canopy structural parameters, including LAI and leaf and woody area ratios. Numéro de notice : A2016-114 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2459716 En ligne : https://doi.org/10.1109/TGRS.2015.2459716 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79992 [article]

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Synchrosqueezing S-transform and its application in seismic spectral decomposition / Zhong-lai Huang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 817 - 825 Titre : Synchrosqueezing S-transform and its application in seismic spectral decomposition Type de document : Article/Communication Auteurs : Zhong-lai Huang, Auteur ; Jianzhong Zhang, Auteur ; Tie-hu Zhao, Auteur ; Yunbao Sun, Auteur Année de publication : 2016 Article en page(s) : pp 817 - 825 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] décomposition spectrale [Termes IGN] séisme Résumé : (Auteur) The synchrosqueezing transform (SST) is a novel approach for time-frequency (T-F) representation of non-stationary signals. By synchrosqueezing and reassigning the T-F spectrum of the wavelet transform (WT) or the short time Fourier transform (STFT) of a signal, the SST can obtain a high-resolution T-F spectrum. In the light of the superiority of S-transform (ST) over the WT and the STFT, especially, in representing a high-frequency weak-amplitude signal on its T-F spectrum, we propose a synchrosqueezing S-transform (SSST) which is realized by synchrosqueezing the spectrum of the ST. The formulas for the SSST and its inverse transform are derived. Synthetic examples show that the SSST has obviously higher resolution than the ST, and is superior to the SST like the ST to the WT. We then applied the SSST to perform the spectral decomposition of a marine seismic data for natural gas hydrate exploration. The results illustrate that the SSST can be used to well detect frequency spectral anomalies correlated with the gas hydrate and free-gas accumulations. We can also conclude that the SSST is a good potential technique to assist seismic interpretation. Numéro de notice : A2016-115 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2466660 En ligne : http://dx.doi.org/ 10.1109/TGRS.2015.2466660 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79995 [article]

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Matrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 783 - 794 Titre : Matrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion Type de document : Article/Communication Auteurs : Renlong Hang, Auteur ; Qingshan Liu, Auteur ; Huihui Song, Auteur ; Yubao Sun, Auteur Année de publication : 2016 Article en page(s) : pp 783 - 794 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse discriminante [Termes IGN] classification multibande [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion de données [Termes IGN] image hyperspectrale [Termes IGN] matrice Résumé : (Auteur) Spatial-spectral feature fusion is well acknowledged as an effective method for hyperspectral (HS) image classification. Many previous studies have been devoted to this subject. However, these methods often regard the spatial-spectral high-dimensional data as 1-D vector and then extract informative features for classification. In this paper, we propose a new HS image classification method. Specifically, matrix-based spatial-spectral feature representation is designed for each pixel to capture the local spatial contextual and the spectral information of all the bands, which can well preserve the spatial-spectral correlation. Then, matrix-based discriminant analysis is adopted to learn the discriminative feature subspace for classification. To further improve the performance of discriminative subspace, a random sampling technique is used to produce a subspace ensemble for final HS image classification. Experiments are conducted on three HS remote sensing data sets acquired by different sensors, and experimental results demonstrate the efficiency of the proposed method. Numéro de notice : A2016-116 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2465899 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2465899 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79996 [article]

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065-2016021	SL	Revue	Centre de documentation	Revues en salle	Disponible

DEM-assisted RFM block adjustment of pushbroom nadir viewing HRS imagery / Yongjun Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 1025 - 1034 Titre : DEM-assisted RFM block adjustment of pushbroom nadir viewing HRS imagery Type de document : Article/Communication Auteurs : Yongjun Zhang, Auteur ; Yi Wan, Auteur ; Xinhui Huang, Auteur Année de publication : 2016 Article en page(s) : pp 1025 - 1034 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] altitude [Termes IGN] angle nadiral [Termes IGN] capteur en peigne [Termes IGN] compensation par bloc [Termes IGN] image à haute résolution [Termes IGN] image ZiYuan-3 [Termes IGN] modèle numérique de surface [Termes IGN] modèle par fonctions rationnelles [Termes IGN] mosaïquage d'images [Termes IGN] point d'appui [Termes IGN] points homologues [Termes IGN] réalité de terrain Résumé : (Auteur) Nadir viewing satellite image is an effective data source to generate orthomosaics. Because of the georeferencing error of satellite images, block adjustment is the first step of orthomosaic generation over a large area. However, the geometric relationship of the neighboring orbits of the nadir viewing images is not rigid enough. This paper proposes a new rational function model (RFM) block adjustment approach that constrains the tie point elevation to enhance the relative geometric rigidity. By interpolating the elevations of tie points in a digital elevation model (DEM) and estimating the a priori errors of the interpolated elevations, better overall relative accuracy is obtained, and the local optimal solution problem is avoided. By constraining the adjusted model parameters according to the a priori error of RFMs, block adjustment without ground control point (GCP) is performed. By optimal initializing the object-space positions of tie points with multi-backprojection method, the needed iteration times of block adjustment are reduced. The proposed approach is investigated with 46 Ziyuan-3 sensor-corrected images, a 1:50 000 scale DEM, and 586 GCPs. Compared with Teo's approach that constrains the horizontal coordinates and elevations of tie points, the approach in this paper converges much faster when the GCPs are sparse, and meanwhile, the absolute and relative accuracy of the two approaches are almost the same. The result of block adjustment with only four GCPs shows that no accuracy degeneration occurred in the test area and the root-mean-square error of independent check point reaches about 1.5 ground resolutions. Different DEMs and number of tie points are used to investigate whether the block adjustment result is influenced by these factors. The results show that better DEM accuracy and denser tie points do improve the accuracy when the images have large side-sway angles. The proposed approach is also tested with 5118 IKONOS-2 images that cover the southern Europe without GCP. The result shows that the relative mosaicking accuracy is much better than that of Grodecki's approach. Numéro de notice : A2016-117 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2472498 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2472498 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79997 [article]

Exemplaires(1)

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065-2016021	SL	Revue	Centre de documentation	Revues en salle	Disponible

Space–time adaptive processing and motion parameter estimation in multistatic passive radar using sparse Bayesian learning / Qisong Wu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 944 - 957 Titre : Space–time adaptive processing and motion parameter estimation in multistatic passive radar using sparse Bayesian learning Type de document : Article/Communication Auteurs : Qisong Wu, Auteur ; Yimin D. Zhang, Auteur ; Moeness G. Amin, Auteur ; Brahim Himed, Auteur Année de publication : 2016 Article en page(s) : pp 944 - 957 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage dirigé [Termes IGN] capteur passif [Termes IGN] estimation bayesienne [Termes IGN] estimation des paramètres [Termes IGN] filtre adaptatif [Termes IGN] image radar [Termes IGN] matrice de covariance Mots-clés libres : sparse Bayesian learning Résumé : (Auteur) Conventional space-time adaptive processing suffers from the requirement of a large number of secondary samples. In this paper, a novel method is proposed to accurately estimate the clutter covariance matrix based on a small number of secondary samples, by exploiting the common clutter support across nearby range cells in the angle-Doppler domain. By taking advantage of the intrinsic sparsity of the clutter in the angle-Doppler domain, the recently developed sparse Bayesian learning technique is employed for high-resolution clutter profile estimation. The proposed method does not require the independent and identically distributed secondary sample assumption, and the required number of secondary data samples can be significantly reduced. In addition, we propose a sparse reconstruction-based approach to acquire the 2-D motion parameters of moving targets, by exploiting their group sparsity in the velocity domain in the multistatic passive radar systems. Simulation results verify the effectiveness of the proposed algorithm. Numéro de notice : A2016-118 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2470518 En ligne : https://doi.org/10.1109/TGRS.2015.2470518 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79998 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2016021	SL	Revue	Centre de documentation	Revues en salle	Disponible

A wavelet-based echo detector for waveform LiDAR data / Cheng-Kai Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 757 - 769 Titre : A wavelet-based echo detector for waveform LiDAR data Type de document : Article/Communication Auteurs : Cheng-Kai Wang, Auteur ; Yi-Hsing Tseng, Auteur ; Chi-Kuei Wang, Auteur Année de publication : 2016 Article en page(s) : pp 757 - 769 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] forme d'onde [Termes IGN] modèle numérique de surface [Termes IGN] onde lidar [Termes IGN] ondelette [Termes IGN] semis de points [Termes IGN] signal laser Résumé : (Auteur) This paper presents a wavelet-based (WB) echo detector that can recover the echoes missed by a light detection and ranging (LiDAR) system via on-the-fly detection. An on-the-fly detection method normally utilizes a simple threshold (TH) to register a target point. Points that belong to weak and/or overlapping echoes are much complicated and are easily missed by TH approaches. The proposed detector based on wavelet transformation is robust to noise and is capable of resolving overlapping echoes. It is thus expected to be good at handling missing echoes. A simulated waveform data set and a real waveform data set of a forest area were both used in this paper. The simulated waveform data were utilized to compare the proposed detector with zero crossing (ZC) and Gaussian decomposition (GD) detectors in terms of their ability to deal with weak or overlapping echoes. The real waveform data set acquired from Leica ALS60 was used to demonstrate a WB algorithm for exploring the missing echoes. Experiments using the simulated data showed that the WB and GD detectors are superior to the ZC detector in finding overlapping echoes. The WB algorithm performs well when dealing with overlapping echoes with a low signal-to-noise ratio. Experiments using the real waveform data show that 31.5% additional weak or overlapping echoes can be detected by the WB detector compared with the point cloud provided by the system. With such additional points, the mean and root-mean-square errors of the digital elevation model differences can be improved from 0.72 and 0.79 m to 0.16 and 0.59 m, respectively. Numéro de notice : A2016-119 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2465148 En ligne : https://doi.org/10.1109/TGRS.2015.2465148 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79999 [article]

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Object classification and recognition from mobile laser scanning point clouds in a road environment / Matti Lehtomäki in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 1226 - 1239 Titre : Object classification and recognition from mobile laser scanning point clouds in a road environment Type de document : Article/Communication Auteurs : Matti Lehtomäki, Auteur ; Anttoni Jaakkola, Auteur ; Juha Hyyppä, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 1226 - 1239 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage automatique [Termes IGN] classification automatique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] histogramme [Termes IGN] reconnaissance d'objets [Termes IGN] réseau routier [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] télémétrie laser mobile [Termes IGN] télémétrie laser terrestre Résumé : (Auteur) Automatic methods are needed to efficiently process the large point clouds collected using a mobile laser scanning (MLS) system for surveying applications. Machine-learning-based object recognition from MLS point clouds in a road and street environment was studied in order to create maps from the road environment infrastructure. The developed automatic processing workflow included the following phases: the removal of the ground and buildings, segmentation, segment classification, and object location estimation. Several novel geometry-based features, which were previously applied in autonomous driving and general point cloud processing, were applied for the segment classification of MLS point clouds. The features were divided into three sets, i.e., local descriptor histograms (LDHs), spin images, and general shape and point distribution features, respectively. These were used in the classification of the following roadside objects: trees, lamp posts, traffic signs, cars, pedestrians, and hoardings. The accuracy of the object recognition workflow was evaluated using a data set that contained more than 400 objects. LDHs and spin images were applied for the first time for machine-learning-based object classification in MLS point clouds in the surveying applications of the road and street environment. The use of these features improved the classification accuracy by 9.6% (resulting in 87.9% accuracy) compared with the accuracy obtained using 17 general shape and point distribution features that represent the current state of the art in the field of MLS; therefore, significant improvement in the classification accuracy was achieved. Connected component segmentation and ground extraction were the cause of most of the errors and should be thus improved in the future. Numéro de notice : A2016-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2476502 En ligne : https://doi.org/10.1109/TGRS.2015.2476502 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80000 [article]

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