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Estimating forest biomass from TerraSAR-X stripmap radargrammetry / Svein Solberg 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 154 - 161 Titre : Estimating forest biomass from TerraSAR-X stripmap radargrammetry Type de document : Article/Communication Auteurs : Svein Solberg, Auteur ; Gertrud Riegler, Auteur ; Philippe Nonin, Auteur Année de publication : 2015 Article en page(s) : pp 154 - 161 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] biomasse aérienne [Termes IGN] image TerraSAR-X [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Norvège [Termes IGN] Picea (genre) [Termes IGN] radargrammétrie Résumé : (Auteur) Radargrammetry has a potential for forest inventories, based on the relationship between the canopy height model (CHM) and the forest variables such as biomass. The objective of this study is to describe the relationship between above-ground biomass and a stripmap TerraSAR-X radargrammetry CHM, with emphasis on accuracy and straightness of the relationship. The study was carried out in a spruce forest in south Norway, comprising biomass data from 145 plots of 250 m2 within 21 selected stands. Above-ground biomass for the plots ranged from 0 to 338 t/ha. We derived a digital surface model (DSM) from six TerraSAR-X stripmap acquisitions by automatic stereo matching. We subtracted a digital terrain model (DTM) from the DSM and obtained a CHM. We assigned the nearest 10 m × 10 m pixel to each field plot. The height of the CHM increased linearly with biomass with 15 t/ha/m. The rmse values were 23 t/ha (18%) at the stand level and 58 t/ha (44%) at the plot level. The tendency of curvilinearity was so weak that it could hardly be distinguished from a straight linear relationship. The straightness of the relationship may enable monitoring of biomass changes without an external DTM as input. A comparison between radargrammetry and interferometric synthetic aperture radar (InSAR) showed that the relationship between the biomass and their respective CHMs was almost identical in terms of parameter estimates. The strength of the relationship was higher with InSAR. By combining ascending and descending pairs followed by editing, the performance of radargrammetry was equally good as with InSAR. Numéro de notice : A2015-028 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2319853 En ligne : https://doi.org/10.1109/TGRS.2014.2319853 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75109 [article]

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Spectral–spatial classification of hyperspectral data via morphological component analysis-based image separation / Zhaohui Xue 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 70 - 84 Titre : Spectral–spatial classification of hyperspectral data via morphological component analysis-based image separation Type de document : Article/Communication Auteurs : Zhaohui Xue, Auteur ; Jun Li, Auteur ; Liang Cheng, Auteur ; Peijun Du, Auteur Année de publication : 2015 Article en page(s) : pp 70 - 84 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes indépendantes [Termes IGN] classification spectrale [Termes IGN] image hyperspectrale Résumé : (Auteur) This paper presents a new spectral-spatial classification method for hyperspectral images via morphological component analysis-based image separation rationale in sparse representation. The method consists of three main steps. First, the high-dimensional spectral domain of hyperspectral images is reduced into a low-dimensional feature domain by using minimum noise fraction (MNF). Second, the proposed separation method is acted on each features to generate the morphological components (MCs), i.e., the content and texture components. To this end, the dictionaries for these two components are built by using local curvelet and Gabor wavelet transforms within the randomly chosen image partitions. Then, sparse coding of one of the MCs and update of the associated dictionary are sequentially performed with the other one fixed. To better direct the separation process, an undecimated Haar wavelet with soft threshold is performed for the content component to make it smooth. This process is repeated until some stopping criterion is met. Finally, a support vector machine is adopted to obtain the classification maps based on the MCs. The experimental results with hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the proposed scheme provides better performance when compared with other widely used methods. Numéro de notice : A2015-029 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2318332 En ligne : https://doi.org/10.1109/TGRS.2014.2318332 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75110 [article]

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Extended random walker-based classification of hyperspectral images / Xudong Kang 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 144 - 153 Titre : Extended random walker-based classification of hyperspectral images Type de document : Article/Communication Auteurs : Xudong Kang, Auteur ; Shutao Li, Auteur ; Leyuan Fang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 144 - 153 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification spectrale [Termes IGN] graphe [Termes IGN] image hyperspectrale [Termes IGN] segmentation d'image Résumé : (Auteur) This paper introduces a novel spectral-spatial classification method for hyperspectral images based on extended random walkers (ERWs), which consists of two main steps. First, a widely used pixelwise classifier, i.e., the support vector machine (SVM), is adopted to obtain classification probability maps for a hyperspectral image, which reflect the probabilities that each hyperspectral pixel belongs to different classes. Then, the obtained pixelwise probability maps are optimized with the ERW algorithm that encodes the spatial information of the hyperspectral image in a weighted graph. Specifically, the class of a test pixel is determined based on three factors, i.e., the pixelwise statistics information learned by a SVM classifier, the spatial correlation among adjacent pixels modeled by the weights of graph edges, and the connectedness between the training and test samples modeled by random walkers. Since the three factors are all well considered in the ERW-based global optimization framework, the proposed method shows very good classification performances for three widely used real hyperspectral data sets even when the number of training samples is relatively small. Numéro de notice : A2015-030 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2319373 En ligne : https://doi.org/10.1109/TGRS.2014.2319373 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75111 [article]

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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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 1 (January 2015) . - pp 244 - 260 Titre : Hierarchical unsupervised change detection in multitemporal hyperspectral images Type de document : Article/Communication Auteurs : S. Liu, Auteur ; Lorenzo Bruzzone, Auteur ; Francesca Bovolo, Auteur ; Peijun Du, Auteur Année de publication : 2015 Article en page(s) : pp 244 - 260 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] approche hiérarchique [Termes IGN] détection de changement [Termes IGN] image hyperspectrale [Termes IGN] image multitemporelle Résumé : (Auteur) The new generation of satellite hyperspectral (HS) sensors can acquire very detailed spectral information directly related to land surface materials. Thus, when multitemporal images are considered, they allow us to detect many potential changes in land covers. This paper addresses the change-detection (CD) problem in multitemporal HS remote sensing images, analyzing the complexity of this task. A novel hierarchical CD approach is proposed, which is aimed at identifying all the possible change classes present between the considered images. In greater detail, in order to formalize the CD problem in HS images, an analysis of the concept of “change” is given from the perspective of pixel spectral behaviors. The proposed novel hierarchical scheme is developed by considering spectral change information to identify the change classes having discriminable spectral behaviors. Due to the fact that, in real applications, reference samples are often not available, the proposed approach is designed in an unsupervised way. Experimental results obtained on both simulated and real multitemporal HS images demonstrate the effectiveness of the proposed CD method. Numéro de notice : A2015-031 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2321277 En ligne : https://doi.org/10.1109/TGRS.2014.2321277 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75112 [article]

Exemplaires(1)

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Automatic spatial–spectral feature selection for hyperspectral image via discriminative sparse multimodal learning / Qian Zhang 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 261 - 279 Titre : Automatic spatial–spectral feature selection for hyperspectral image via discriminative sparse multimodal learning Type de document : Article/Communication Auteurs : Qian Zhang, Auteur ; Yuan Tian, Auteur ; Yuan Yang, Auteur ; Chunhong Pan, Auteur Année de publication : 2015 Article en page(s) : pp 261 - 279 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse discriminante [Termes IGN] apprentissage (cognition) [Termes IGN] apprentissage dirigé [Termes IGN] classification spectrale [Termes IGN] image hyperspectrale [Termes IGN] matrice [Termes IGN] méthode des moindres carrés [Termes IGN] programmation non linéaire Résumé : (Auteur) Spectral-spatial feature combination for hyperspectral image analysis has become an important research topic in hyperspectral remote sensing applications. A simple and straightforward way to integrate spectral-spatial features is to concatenate heterogeneous features into a long vector. Then, the dimensionality reduction techniques, i.e., feature selection, are applied before subsequent utilizations. However, such representation can introduce redundancy and noise. Moreover, traditional single-feature selection methods treat different features equally and ignore their complementary properties. As a result, the performance of subsequent tasks, i.e., classification, would drop down. In this paper, we propose a novel approach to integrate the spectral-spatial features based on the concatenating strategy, termed discriminative sparse multimodal learning for feature selection (DSML-FS). In the proposed method, joint structured sparsity regularizations are used to exploit the intrinsic data structure and relationships among different features. Discriminative least squares regression is applied to enlarge the distance between classes. Therefore, the weight matrix incorporating the information of feature wise and individual properties is automatically learned for spectral-spatial feature selection. We develop an alternative iterative algorithm to solve the nonlinear optimization problem in DSML-FS with global convergence. We systematically evaluate the proposed algorithm on three available hyperspectral data sets, and the encouraging experimental results demonstrate the effectiveness of DSML-FS. Numéro de notice : A2015-032 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2321277 En ligne : https://doi.org/10.1109/TGRS.2014.2321277 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75114 [article]

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Hyperspectral image denoising via sparse representation and low-rank constraint / Yong-Qiang Zhao 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 296 - 308 Titre : Hyperspectral image denoising via sparse representation and low-rank constraint Type de document : Article/Communication Auteurs : Yong-Qiang Zhao, Auteur ; Jingxiang Yang, Auteur Année de publication : 2015 Article en page(s) : pp 296 - 308 Note générale : Bibliogaphie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] filtrage du bruit [Termes IGN] image hyperspectrale [Termes IGN] redondance de données Résumé : (Auteur) Hyperspectral image (HSI) denoising is an essential preprocess step to improve the performance of subsequent applications. For HSI, there is much global and local redundancy and correlation (RAC) in spatial/spectral dimensions. In addition, denoising performance can be improved greatly if RAC is utilized efficiently in the denoising process. In this paper, an HSI denoising method is proposed by jointly utilizing the global and local RAC in spatial/spectral domains. First, sparse coding is exploited to model the global RAC in the spatial domain and local RAC in the spectral domain. Noise can be removed by sparse approximated data with learned dictionary. At this stage, only local RAC in the spectral domain is employed. It will cause spectral distortion. To compensate the shortcoming of local spectral RAC, low-rank constraint is used to deal with the global RAC in the spectral domain. Different hyperspectral data sets are used to test the performance of the proposed method. The denoising results by the proposed method are superior to results obtained by other state-of-the-art hyperspectral denoising methods. Numéro de notice : A2015-033 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2321557 En ligne : https://doi.org/10.1109/TGRS.2014.2321557 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75115 [article]

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An abundance characteristic-based independent component analysis for hyperspectral unmixing / Nan Wang 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 416 - 428 Titre : An abundance characteristic-based independent component analysis for hyperspectral unmixing Type de document : Article/Communication Auteurs : Nan Wang, Auteur ; Liangpei Zhang, Auteur ; Lifu Zhang, Auteur Année de publication : 2015 Article en page(s) : pp 416 - 428 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] analyse en composantes indépendantes [Termes IGN] image hyperspectrale Résumé : (Auteur) Independent component analysis (ICA) has been recently applied into hyperspectral unmixing as a result of its low computation time and its ability to perform without prior information. However, when applying ICA for hyperspectral unmixing, the independence assumption in the ICA model conflicts with the abundance sum-to-one constraint and the abundance nonnegative constraint in the linear mixture model, which affects the hyperspectral unmixing accuracy. In this paper, we consider an abundance matrix composed of Np-dimensional variables, and we propose a new hyperspectral unmixing approach with an abundance characteristic-based ICA model. Two characteristics of the abundance variables are explored, and the model is constructed by these characteristics. A corresponding gradient descent algorithm is also proposed to solve the proposed objective function. Both the synthetic and real experimental results demonstrate that the proposed method performs better than the other state-of-the-art methods in abundance and endmember extraction. Numéro de notice : A2015-034 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2322862 En ligne : https://doi.org/10.1109/TGRS.2014.2322862 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75116 [article]

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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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Spatial-aware dictionary learning for hyperspectral image classification / Ali Soltani-Farani 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 527 - 541 Titre : Spatial-aware dictionary learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Ali Soltani-Farani, Auteur ; Hamid R. Rabiee, Auteur ; Seyyed Abbas Hosseini, Auteur Année de publication : 2015 Article en page(s) : pp 527 - 541 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'apprentissage [Termes IGN] classification [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image hyperspectrale [Termes IGN] limite de résolution radiométrique [Termes IGN] prise en compte du contexte [Termes IGN] voisinage (relation topologique) Résumé : (Auteur) This paper presents a structured dictionary-based model for hyperspectral data that incorporates both spectral and contextual characteristics of spectral samples. The idea is to partition the pixels of a hyperspectral image into a number of spatial neighborhoods called contextual groups and to model the pixels inside a group as members of a common subspace. That is, each pixel is represented using a linear combination of a few dictionary elements learned from the data, but since pixels inside a contextual group are often made up of the same materials, their linear combinations are constrained to use common elements from the dictionary. To this end, dictionary learning is carried out with a joint sparse regularizer to induce a common sparsity pattern in the sparse coefficients of a contextual group. The sparse coefficients are then used for classification using a linear support vector machine. Experimental results on a number of real hyperspectral images confirm the effectiveness of the proposed representation for hyperspectral image classification. Moreover, experiments with simulated multispectral data show that the proposed model is capable of finding representations that may effectively be used for classification of multispectral resolution samples. Numéro de notice : A2015-037 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2325067 En ligne : https://doi.org/10.1109/TGRS.2014.2325067 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75119 [article]

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SAR-SIFT : a SIFT-like algorithm for SAR images / Flora Dellinger 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 453 - 466 Titre : SAR-SIFT : a SIFT-like algorithm for SAR images Type de document : Article/Communication Auteurs : Flora Dellinger, Auteur ; Julien Delon, Auteur ; Yann Gousseau, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 453 - 466 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] chatoiement [Termes IGN] image radar moirée [Termes IGN] SIFT (algorithme) Résumé : (Auteur) The scale-invariant feature transform (SIFT) algorithm and its many variants are widely used in computer vision and in remote sensing to match features between images or to localize and recognize objects. However, mostly because of speckle noise, it does not perform well on synthetic aperture radar (SAR) images. In this paper, we introduce a SIFT-like algorithm specifically dedicated to SAR imaging, which is named SAR-SIFT. The algorithm includes both the detection of keypoints and the computation of local descriptors. A new gradient definition, yielding an orientation and a magnitude that are robust to speckle noise, is first introduced. It is then used to adapt several steps of the SIFT algorithm to SAR images. We study the improvement brought by this new algorithm, as compared with existing approaches. We present an application of SAR-SIFT to the registration of SAR images in different configurations, particularly with different incidence angles. Numéro de notice : A2015-038 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2323552 En ligne : https://doi.org/10.1109/TGRS.2014.2323552 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75120 [article]

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Automatic construction of 3-D building model from airborne LIDAR data through 2-D snake algorithm / Jianhua Yan 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 3 - 14 Titre : Automatic construction of 3-D building model from airborne LIDAR data through 2-D snake algorithm Type de document : Article/Communication Auteurs : Jianhua Yan, Auteur ; Keqi Zhang, Auteur ; Chengcui Zhang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 3 - 14 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme snake [Termes IGN] données lidar [Termes IGN] modèle numérique du bâti [Termes IGN] reconstruction 2D du bâti [Termes IGN] reconstruction 3D du bâti [Termes IGN] topologie Résumé : (Auteur) The snake algorithm has been proposed to solve many remote sensing and computer vision problems such as object segmentation, surface reconstruction, and object tracking. This paper introduces a framework for 3-D building model construction from LIDAR data based on the snake algorithm. It consists of nonterrain object identification, building and tree separation, building topology extraction, and adjustment by the snake algorithm. The challenging task in applying the snake algorithm to building topology adjustment is to find the global minima of energy functions derived for 2-D building topology. The traditional snake algorithm uses dynamic programming for computing the global minima of energy functions which is limited to snake problems with 1-D topology (i.e., a contour) and cannot handle problems with 2-D topology. In this paper, we have extended the dynamic programming method to address the snake problems with a 2-D planar topology using a novel graph reduction technique. Given a planar snake, a set of reduction operations is defined and used to simplify the graph of the planar snake into a set of isolated vertices while retaining the minimal energy of the graph. Another challenging task for 3-D building model reconstruction is how to enforce different kinds of geometric constraints during building topology refinement. This framework proposed two energy functions, deviation and direction energy functions, to enforce multiple geometric constraints on 2-D topology refinement naturally and efficiently. To examine the effectiveness of the framework, the framework has been applied on different data sets to construct 3-D building models from airborne LIDAR data. The results demonstrate that the proposed snake algorithm successfully found the global optima in polynomial time for all of the building topologies and generated satisfactory 3-D models for most of the buildings in the study areas. Numéro de notice : A2015-039 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2312393 En ligne : https://doi.org/10.1109/TGRS.2014.2312393 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75121 [article]

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065-2015011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Empirical waveform decomposition and radiometric calibration of a terrestrial full-waveform laser scanner / Preston J. Hartzell 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 162 - 172 Titre : Empirical waveform decomposition and radiometric calibration of a terrestrial full-waveform laser scanner Type de document : Article/Communication Auteurs : Preston J. Hartzell, Auteur ; Craig L. Glennie, Auteur ; David C. Finnegan, Auteur Année de publication : 2015 Article en page(s) : pp 162 - 172 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] décomposition [Termes IGN] données lidar [Termes IGN] étalonnage de capteur (imagerie) [Termes IGN] étalonnage radiométrique [Termes IGN] forme d'onde pleine [Termes IGN] instrumentation Riegl [Termes IGN] Lidar [Termes IGN] méthode des moindres carrés [Termes IGN] modèle empirique [Termes IGN] onde lidar [Termes IGN] télémètre laser terrestre Résumé : (Auteur) The parametric models used in Light Detection And Ranging (LiDAR) waveform decomposition routines are inherently estimates of the sensor's system response to backscattered laser pulse power. This estimation can be improved with an empirical system response model, yielding reduced waveform decomposition residuals and more precise echo ranging. We develop an empirical system response model for a Riegl VZ-400 terrestrial laser scanner, from a series of observations to calibrated reflectance targets, and present a numerical least squares method for decomposing waveforms with the model. The target observations are also used to create an empirical radiometric calibration model that accommodates a nonlinear relationship between received optical power and echo peak amplitude, and to examine the temporal stability of the instrument. We find that the least squares waveform decomposition based on the empirical system response model decreases decomposition fitting errors by an order of magnitude for high-amplitude returns and reduces range estimation errors on planar surfaces by 17% over a Gaussian model. The empirical radiometric calibration produces reflectance values self-consistent to within 5% for several materials observed at multiple ranges, and analysis of multiple calibration data sets collected over a one-year period indicates that echo peak amplitude values are stable to within ±3% for target ranges up to 125 m. Numéro de notice : A2015-040 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2320134 En ligne : https://doi.org/10.1109/TGRS.2014.2320134 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75122 [article]

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065-2015011	RAB	Revue	Centre de documentation	En réserve L003	Disponible