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Multimorphological superpixel model for hyperspectral image classification / Tianzhu Liu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)
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Titre : Multimorphological superpixel model for hyperspectral image classification Type de document : Article/Communication Auteurs : Tianzhu Liu, Auteur ; Yanfeng Gu, Auteur ; Jocelyn Chanussot, Auteur ; Mauro Dalla Mura, Auteur Année de publication : 2017 Article en page(s) : pp 6950 - 6963 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyauRésumé : (Auteur) With the development of hyperspectral sensors, nowadays, we can easily acquire large amount of hyperspectral images (HSIs) with very high spatial resolution, which has led to a better identification of relatively small structures. Owing to the high spatial resolution, there are much less mixed pixels in the HSIs, and the boundaries between these categories are much clearer. However, the high spatial resolution also leads to complex and fine geometrical structures and high inner-class variability, which make the classification results very “noisy.” In this paper, we propose a multimorphological superpixel (MMSP) method to extract the spectral and spatial features and address the aforementioned problems. To reduce the difference within the same class and obtain multilevel spatial information, morphological features (multistructuring element extended morphological profile or multiattribute filter extended multi-attribute profiles) are first obtained from the original HSI. After that, simple linear iterative clustering segmentation method is performed on each morphological feature to acquire the MMSPs. Then, uniformity constraint is used to merge the MMSPs belonging to the same class which can avoid introducing the information from different classes and acquire spatial structures at object level. Subsequently, mean filtering is utilized to extract the spatial features within and among MMSPs. At last, base kernels are obtained from the spatial features and original HSI, and several multiple kernel learning methods are used to obtain the optimal kernel to incorporate into the support vector machine. Experiments conducted on three widely used real HSIs and compared with several well-known methods demonstrate the effectiveness of the proposed model. Numéro de notice : A2017-767 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2737037 En ligne : https://doi.org/10.1109/TGRS.2017.2737037 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88806
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 12 (December 2017) . - pp 6950 - 6963[article]A batch-mode regularized multimetric active learning framework for classification of hyperspectral images / Zhou Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)
[article]
Titre : A batch-mode regularized multimetric active learning framework for classification of hyperspectral images Type de document : Article/Communication Auteurs : Zhou Zhang, Auteur ; Melba M. Crawford, Auteur Année de publication : 2017 Article en page(s) : pp 6594 - 6609 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification barycentrique
[Termes IGN] image hyperspectrale
[Termes IGN] précision de la classificationRésumé : (Auteur) Techniques that combine multiple types of features, such as spectral and spatial features, for hyperspectral image classification can often significantly improve the classification accuracy and produce a more reliable thematic map. However, the high dimensionality of the input data and the typically limited quantity of labeled samples are two key challenges that affect classification performance of supervised methods. In order to simultaneously deal with these issues, a regularized multimetric active learning (AL) framework is proposed which consists of three main parts. First, a regularized multimetric learning approach is proposed to jointly learn distinct metrics for different types of features. The regularizer incorporates the unlabeled data based on the neighborhood relationship, which helps avoid overfitting at early stages of AL, when the quantity of training data is particularly small. Then, as AL proceeds, the regularizer is also updated through similarity propagation, thus taking advantage of informative labeled samples. Finally, multiple features are projected into a common feature space, in which a new batch-mode AL strategy combining uncertainty and diversity is utilized in conjunction with k-nearest neighbor classification to enrich the set of labeled samples. In order to evaluate the effectiveness of the proposed framework, the experiments were conducted on two benchmark hyperspectral data sets, and the results were compared to those achieved by several other state-of-the-art AL methods. Numéro de notice : A2017-760 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2730583 En ligne : https://doi.org/10.1109/TGRS.2017.2730583 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88788
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6594 - 6609[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)
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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] TrenteRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6354 - 6365[article]Robust minimum volume simplex analysis for hyperspectral unmixing / Shaoquan Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)
[article]
Titre : Robust minimum volume simplex analysis for hyperspectral unmixing Type de document : Article/Communication Auteurs : Shaoquan Zhang, Auteur ; Alexander Agathos, Auteur ; Jun Li, Auteur Année de publication : 2017 Article en page(s) : pp 6431 - 6439 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme du simplexe
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] factorisation
[Termes IGN] image hyperspectrale
[Termes IGN] méthode robusteRésumé : (Auteur) Most blind hyperspectral unmixing methods exploit convex geometry properties of hyperspectral data. The minimum volume simplex analysis (MVSA) is one of such methods, which, as many others, estimates the minimum volume (MV) simplex where the measured vectors live. MVSA was conceived to circumvent the matrix factorization step often implemented by MV-based algorithms and also to cope with outliers, which compromise the results produced by MV algorithms. Inspired by the recently proposed robust MV enclosing simplex (RMVES) algorithm, we herein introduce the robust MVSA (RMVSA), which is a version of MVSA robust to noise. As in RMVES, the robustness is achieved by employing chance constraints, which control the volume of the resulting simplex. RMVSA differs, however, substantially from RMVES in the way optimization is carried out. In this paper, we develop a linearization relaxation of the nonlinear chance constraints, which can greatly lighten the computational complex of chance constraint problems. The effectiveness of RMVSA is illustrated by comparing its performance with the state of the art. Numéro de notice : A2017-749 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2728104 En ligne : https://doi.org/10.1109/TGRS.2017.2728104 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88784
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6431 - 6439[article]Sparse distributed multitemporal hyperspectral unmixing / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)
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Titre : Sparse distributed multitemporal hyperspectral unmixing Type de document : Article/Communication Auteurs : Jakob Sigurdsson, Auteur ; Magnus Orn Ulfarsson, Auteur ; Johannes R. Sveinsson, Auteur ; José M. Bioucas-Dias, Auteur Année de publication : 2017 Article en page(s) : pp 6069 - 6084 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] image hyperspectrale
[Termes IGN] image multitemporelleRésumé : (Auteur) Blind hyperspectral unmixing jointly estimates spectral signatures and abundances in hyperspectral images (HSIs). Hyperspectral unmixing is a powerful tool for analyzing hyperspectral data. However, the usual huge size of HSIs may raise difficulties for classical unmixing algorithms, namely, due to limitations of the hardware used. Therefore, some researchers have considered distributed algorithms. In this paper, we develop a distributed hyperspectral unmixing algorithm that uses the alternating direction method of multipliers and ℓ1 sparse regularization. The hyperspectral unmixing problem is split into a number of smaller subproblems that are individually solved, and then the solutions are combined. A key feature of the proposed algorithm is that each subproblem does not need to have access to the whole HSI. The algorithm may also be applied to multitemporal HSIs with due adaptations accounting for variability that often appears in multitemporal images. The effectiveness of the proposed algorithm is evaluated using both simulated data and real HSIs. Numéro de notice : A2017-742 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2720539 En ligne : https://doi.org/10.1109/TGRS.2017.2720539 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88776
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6069 - 6084[article]Spatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing / Xinyu Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)PermalinkThe Naïve Overfitting Index Selection (NOIS): A new method to optimize model complexity for hyperspectral data / Alby D. Rocha in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)PermalinkHyperspectral dimensionality reduction for biophysical variable statistical retrieval / Juan Pablo Rivera-Caicedo in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)PermalinkHyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables / Sakari Tuominen in Silva fennica, vol 51 n° 5 (2017)PermalinkBand subset selection for anomaly detection in hyperspectral imagery / Lin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkUnsupervised domain adaptation for early detection of drought stress in hyperspectral images / P. Schmitter in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)PermalinkUsing landsat surface reflectance data as a reference target for multiswath hyperspectral data collected over mixed agricultural rangeland areas / Cooper McCann in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkColour Helmholtz stereopsis for reconstruction of dynamic scenes with arbitrary unknown reflectance / Nadejda Roubtsova in International journal of computer vision, vol 124 n° 1 (August 2017)PermalinkFrom subpixel to superpixel : a novel fusion framework for hyperspectral image classification / Ting Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkIntersensor statistical matching for pansharpening : theoretical issues and practical solutions / Luciano Alparone in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkLearning and transferring deep joint spectral–spatial features for hyperspectral classification / Jingxiang Yang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkLearning a discriminative distance metric with label consistency for scene classification / Yuebin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkLearning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks / Shaohui Mei in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkMorphologically decoupled structured sparsity for rotation-invariant hyperspectral image analysis / Saurabh Prasad in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkA novel preunmixing framework for efficient detection of linear mixtures in hyperspectral images / Andrea Marinoni in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkSuperpixel-based intrinsic image decomposition of hyperspectral images / Xudong Jin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkDeveloping detailed age-specific thematic maps for coffee (Coffea arabica L.) in heterogeneous agricultural landscapes using random forests applied on Landsat 8 multispectral sensor / Abel Chemura in Geocarto international, vol 32 n° 7 (July 2017)PermalinkJoint hyperspectral superresolution and unmixing with interactive feedback / Chen Yi in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)PermalinkNorthern conifer forest species classification using multispectral data acquired from an unmanned aerial vehicle / Steven E. Franklin in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 7 (July 2017)PermalinkTotal variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing / Wei He in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)PermalinkLearning to diversify deep belief networks for hyperspectral image classification / Ping Zhong in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)PermalinkA novel semisupervised active-learning algorithm for hyperspectral image classification / Zengmao Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)PermalinkObject-based analysis of multispectral airborne laser scanner data for land cover classification and map updating / Leena Matikainen in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)PermalinkPan-sharpening of Landsat-8 images and its application in calculating vegetation greenness and canopy water contents / Khan Rubayet Rahaman in ISPRS International journal of geo-information, vol 6 n° 6 (June 2017)PermalinkSemiautomatic detection and classification of materials in historic buildings with low-cost photogrammetric equipment / Javier Sanchez in Journal of Cultural Heritage, vol 25 (May - June 2017)PermalinkComplétion d'image exploitant des données multispectrales / Frédéric Bousefsaf in Revue Française de Photogrammétrie et de Télédétection, n° 215 (mai - août 2017)PermalinkDimensionality reduction and classification of hyperspectral images using ensemble discriminative local metric learning / Yanni Dong in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)PermalinkEvaluation of multisource data for glacier terrain mapping : a neural net approach / Aparna Shukla in Geocarto international, vol 32 n° 5 (May 2017)PermalinkHyperspectral and lidar intensity data fusion : A framework for the rigorous correction of illumination, anisotropic effects, and cross calibration / Maximilian Brell in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)PermalinkSelf-taught feature learning for hyperspectral image classification / Ronald Kemker in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)PermalinkSuperpixel-based multitask learning framework for hyperspectral image classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)PermalinkUrban land use/land cover discrimination using image-based reflectance calibration methods for hyperspectral data / Shailesh S. Deshpande in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 5 (May 2017)PermalinkEvaluation of pan-sharpening methods for spatial and spectral quality / Jagalingam Pushparaj in Applied geomatics, vol 9 n° 1 (March 2017)PermalinkForestry applications of UAVs in Europe: a review / Chiara Torresan in International Journal of Remote Sensing IJRS, vol 38 n° 8-10 (April 2017)PermalinkHyperspectral band selection from statistical wavelet models / Siwei Feng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)PermalinkMultilayer NMF for blind unmixing of hyperspectral imagery with additional constraints / L. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 4 (April 2017)PermalinkSemantic segmentation of forest stands of pure species combining airborne lidar data and very high resolution multispectral imagery / Clément Dechesne in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)PermalinkToward optimum fusion of thermal hyperspectral and visible images in classification of urban area / Farhad Samadzadegan in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 4 (April 2017)PermalinkTransferability of multi- and hyperspectral optical biocrust indices / Emilio Rodríguez-Caballero in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)PermalinkAdaptive linear spectral mixture analysis / Chein-I Chang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkAttribute profiles on derived features for urban land cover classification / Bharath Bhushan Damodaran in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)PermalinkDictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification / Minchao Ye in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkDiscriminative low-rank Gabor filtering for spectral–spatial hyperspectral image classification / Lin He in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkExtracting target spectrum for hyperspectral target detection : an adaptive weighted learning method using a self-completed background dictionary / Yubin Niu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkHyperspectral SAR / Matthew Ferrara in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)Permalink