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A unified attention paradigm for hyperspectral image classification / Qian Liu in IEEE Transactions on geoscience and remote sensing, vol 61 n° 3 (March 2023)
[article]
Titre : A unified attention paradigm for hyperspectral image classification Type de document : Article/Communication Auteurs : Qian Liu, Auteur ; Zebin Wu, Auteur ; Yang Xu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 5506316 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] précision de la classification
[Termes IGN] séparateur à vaste margeRésumé : (auteur) Attention mechanisms improve the classification accuracies by enhancing the salient information for hyperspectral images (HSIs). However, existing HSI attention models are driven by advanced achievements of computer vision, which are not able to fully exploit the spectral–spatial structure prior of HSIs and effectively refine features from a global perspective. In this article, we propose a unified attention paradigm (UAP) that defines the attention mechanism as a general three-stage process including optimizing feature representations, strengthening information interaction, and emphasizing meaningful information. Meanwhile, we designed a novel efficient spectral–spatial attention module (ESSAM) under this paradigm, which adaptively adjusts feature responses along the spectral and spatial dimensions at an extremely low parameter cost. Specifically, we construct a parameter-free spectral attention block that employs multiscale structured encodings and similarity calculations to perform global cross-channel interactions, and a memory-enhanced spatial attention block that captures key semantics of images stored in a learnable memory unit and models global spatial relationship by constructing semantic-to-pixel dependencies. ESSAM takes full account of the spatial distribution and low-dimensional characteristics of HSIs, with better interpretability and lower complexity. We develop a dense convolutional network based on efficient spectral–spatial attention network (ESSAN) and experiment on three real hyperspectral datasets. The experimental results demonstrate that the proposed ESSAM brings higher accuracy improvement compared to advanced attention models. Numéro de notice : A2023-185 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2023.3257321 Date de publication en ligne : 15/12/2023 En ligne : https://doi.org/10.1109/TGRS.2023.3257321 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102957
in IEEE Transactions on geoscience and remote sensing > vol 61 n° 3 (March 2023) . - n° 5506316[article]Tree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data / Ying Quan in GIScience and remote sensing, vol 60 n° 1 (2023)
[article]
Titre : Tree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data Type de document : Article/Communication Auteurs : Ying Quan, Auteur ; Mingze Li, Auteur ; Yuanshuo Hao, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2171706 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] espèce végétale
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] forêt secondaire
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] semis de pointsRésumé : (auteur) Recent growth in unmanned aerial vehicle (UAV) technology have promoted the detailed mapping of individual tree species. However, the in-depth mining and comprehending of the significance of features derived from high-resolution UAV data for tree species discrimination remains a difficult task. In this study, a state-of-the-art approach combining UAV-borne light detection and ranging (LiDAR) and hyperspectral was used to classify 11 common tree species in a typical natural secondary forest in Northeast China. First, comprehensive relevant structural and spectral features were extracted. Then, the most valuable feature sets were selected by using a hybrid approach combining correlation-based feature selection with the optimized recursive feature elimination algorithm. The random forest algorithm was used to assess feature importance and perform the classification. Finally, the robustness of features derived from point clouds with different structures and hyperspectral images with different spatial resolutions was tested. Our results showed that the best classification accuracy was obtained by combining LiDAR and hyperspectral data (75.7%) compared to that based on LiDAR (60.0%) and hyperspectral (64.8%) data alone. The mean intensity of single returns and the visible atmospherically resistant index for red-edge band were the most influential LiDAR and hyperspectral derived features, respectively. The selected features were robust in point clouds with a density not lower than 5% (~5 pts/m2) and a resolution not lower than 0.3 m in hyperspectral data. Although canopy surface features were slightly different from original LiDAR features, canopy surface information was also important for tree species classification. This study proved the capabilities of UAV-borne LiDAR and hyperspectral data in natural secondary forest tree species discrimination and the potential for this approach to be transferable to other study areas. Numéro de notice : A2023-194 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/15481603.2023.2171706 Date de publication en ligne : 03/02/2023 En ligne : https://doi.org/10.1080/15481603.2023.2171706 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103075
in GIScience and remote sensing > vol 60 n° 1 (2023) . - n° 2171706[article]Bathymetry and benthic habitat mapping in shallow waters from Sentinel-2A imagery: A case study in Xisha islands, China / Wei Huang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 12 (December 2022)
[article]
Titre : Bathymetry and benthic habitat mapping in shallow waters from Sentinel-2A imagery: A case study in Xisha islands, China Type de document : Article/Communication Auteurs : Wei Huang, Auteur ; Jun Zhao, Auteur ; Bin Ai, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4212412 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bathymétrie
[Termes IGN] carte thématique
[Termes IGN] Chine
[Termes IGN] correction atmosphérique
[Termes IGN] fond marin
[Termes IGN] habitat d'espèce
[Termes IGN] image hyperspectrale
[Termes IGN] image Sentinel-MSI
[Termes IGN] profondeur
[Termes IGN] réflectance spectraleRésumé : (auteur) Mapping of benthic habitats and bathymetry is crucial for sustainable development and assessment of climate change and human activities. In this study, Hyperspectral Optimization Process Exemplar (HOPE) was modified, renamed as M-HOPE, to simultaneously obtain bathymetry and benthic habitat in shallow waters in Xisha Island, China. A local lookup table (LUT) for benthic reflectance spectra was established. Validation using in situ measurements demonstrated good performance of M-HOPE with a R2 of 0.76 for bathymetry using the local LUT. Application of M-HOPE to Sentinel-2A imagery further proved good accuracy of M-HOPE derived bathymetry with a R2 of 0.86 against in situ observations and a R2 of 0.92 against ICESat-2 measurements. M-HOPE-derived benthic classification also agreed well with field observations with probability of detection (POD) >0.6 and false alarm ratio (FAR) Numéro de notice : A2022-907 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3229029 Date de publication en ligne : 14/12/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3229029 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102338
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 12 (December 2022) . - n° 4212412[article]Bayesian hyperspectral image super-resolution in the presence of spectral variability / Fei Ye in IEEE Transactions on geoscience and remote sensing, vol 60 n° 12 (December 2022)
[article]
Titre : Bayesian hyperspectral image super-resolution in the presence of spectral variability Type de document : Article/Communication Auteurs : Fei Ye, Auteur ; Zebin Wu, Auteur ; Yang Xu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5545613 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] processus gaussien
[Termes IGN] réflectance
[Termes IGN] signature spectrale
[Termes IGN] théorème de BayesRésumé : (auteur) Synthesizing a high-resolution (HR) hyperspectral image (HSI) by merging a low-resolution (LR) HSI with a corresponding HR multispectral image (MSI) has become a promising HSI super-resolution scheme. Most existing HSI-MSI fusion methods are effective to some extent, while several challenges remain. First, the spectral response of a given material exhibits considerable variability due to different acquisition times and conditions, however, variations in spectral signatures are often neglected. Second, a majority of off-the-shelf methods require predefined degradation operators, which can be unavailable in practice. To tackle the above issues, we introduce a novel fusion approach with a Bayesian framework. Specifically, we regard the up-sampled LR-HSI as the low-frequency component of the underlying HR-HSI. We characterize the texture features of high- and low-frequency components, respectively, which can enlarge modeling capacity and bypass the absence of degradation operators. Furthermore, we depict the relative smoothness of reflectance spectra with the Gaussian process. Extensive experiments on synthesized and real datasets illustrate the superiority of the proposed strategy in terms of fusion performance and robustness to spectral variability. Numéro de notice : A2022-908 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3228313 Date de publication en ligne : 12/12/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3228313 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102339
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 12 (December 2022) . - n° 5545613[article]Hyperspectral imagery and urban areas: results of the HYEP project / Christiane Weber in Revue Française de Photogrammétrie et de Télédétection, n° 224 (2022)
[article]
Titre : Hyperspectral imagery and urban areas: results of the HYEP project Type de document : Article/Communication Auteurs : Christiane Weber, Auteur ; Xavier Briottet , Auteur ; Thomas Houet, Auteur ; Sébastien Gadal, Auteur ; Rahim Aguejdad, Auteur ; Yannick Deville, Auteur ; Mauro Dalla Mura, Auteur ; Clément Mallet , Auteur ; Arnaud Le Bris , Auteur ; et al., Auteur Année de publication : 2022 Projets : HYEP / Weber, Christiane Article en page(s) : pp 75 - 92 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] détection d'objet
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] Lituanie
[Termes IGN] milieu urbain
[Termes IGN] panneau photovoltaïque
[Termes IGN] surface imperméable
[Termes IGN] ToulouseRésumé : (Auteur) The HYEP project (ANR HYEP 14-CE22-0016-01 Hyperspectral imagery for Environmental urban Planning - Mobility and Urban Systems Programme - 2014) confirmed the interest of a global approach to the urban environment by remote sensing and in particular by using hyperspectral imaging (HI). The interest of hyperspectral images lies in the range of information provided over wavelengths from 0.4 to 4 μm; they thus provide access to spectral quantities of interest and to chemical or biophysical parameters of the surface. HYEP's objective was to specify this and to propose a panel of methods and treatments taking into account the characteristics of other existing sensors in order to compare their performance. The developments carried out were applied and evaluated on hyperspectral airborne images acquired in Toulouse and Kaunas (Lithuania), also used to synthesize space systems: Sentinel-2, Hypxim/Biodiversity and Pleiades. Among the locks identified were those related to improving the spatial capabilities of the sensors and spatial scale changes, which were partially overcome by fusion and sharpening approaches, which proved to be successful. After a description of our hyperspectral data set acquired over Toulouse, an analysis is conducted on several existing and accessible spectral databases. Then, the chosen methods are presented. They include extraction, fusion and classification methods, which are then applied on our dataset synthetized at different spatial resolution to evaluate the benefits and the complementarity of hyperspectral imagery in comparison with other traditional sensors. Some specific applications are investigated of interest for urban planners: impervious soil map, vegetation species cartography and detection of solar panels. Finally, discussion and perspectives are presented. Numéro de notice : A2022-941 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : Hal Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.52638/rfpt.2022.589 Date de publication en ligne : 22/12/2022 En ligne : https://dx.doi.org/10.52638/rfpt.2022.589 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102831
in Revue Française de Photogrammétrie et de Télédétection > n° 224 (2022) . - pp 75 - 92[article]Cross-guided pyramid attention-based residual hyperdense network for hyperspectral image pansharpening / Jiahui Qu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 11 (November 2022)PermalinkLand use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information / Ozlem Akar in Geocarto international, vol 37 n° 22 ([10/10/2022])PermalinkChallenging the link between functional and spectral diversity with radiative transfer modeling and data / Javier Pacheco-Labradora in Remote sensing of environment, vol 280 (October 2022)PermalinkClassification of pine wilt disease at different infection stages by diagnostic hyperspectral bands / Niwen Li in Ecological indicators, vol 142 (September 2022)PermalinkHyperspectral unmixing using transformer network / Preetam Ghosh in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)PermalinkHyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion / Kun Li in ISPRS Journal of photogrammetry and remote sensing, vol 188 (June 2022)PermalinkPrecise crop classification of hyperspectral images using multi-branch feature fusion and dilation-based MLP / Haibin Wu in Remote sensing, vol 14 n° 11 (June-1 2022)PermalinkA new method to detect targets in hyperspectral images based on principal component analysis / Shahram Sharifi Hashjin in Geocarto international, vol 37 n° 9 ([15/05/2022])PermalinkSpectral-spatial classification method for hyperspectral images using stacked sparse autoencoder suitable in limited labelled samples situation / Seyyed Ali Ahmadi in Geocarto international, vol 37 n° 7 ([15/04/2022])PermalinkWood decay detection in Norway spruce forests based on airborne hyperspectral and ALS data / Michele Dalponte in Remote sensing, vol 14 n° 8 (April-2 2022)PermalinkDeep generative model for spatial–spectral unmixing with multiple endmember priors / Shuaikai Shi in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)PermalinkMeta-learning based hyperspectral target detection using siamese network / Yulei Wang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)PermalinkAboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network / Chen Chen in Remote sensing of environment, vol 270 (March 2022)PermalinkProbabilistic unsupervised classification for large-scale analysis of spectral imaging data / Emmanuel Paradis in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)PermalinkDecision fusion of deep learning and shallow learning for marine oil spill detection / Junfang Yang in Remote sensing, vol 14 n° 3 (February-1 2022)PermalinkDetection and biomass estimation of phaeocystis globosa blooms off Southern China from UAV-based hyperspectral measurements / Xue Li in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)PermalinkPermalinkFusion de données hyperspectrales et panchromatiques dans le domaine réflectif / Yohann Constans (2022)PermalinkGlobal and climate challenges, graph-based data analysis for multisource information extraction / Morgane Batelier (2022)PermalinkA novel unmixing-based hypersharpening method via convolutional neural network / Xiaochen Lu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)PermalinkPreparation of the VENµS satellite data over Israel for the input into the GRASP data treatment algorithm / Maeve Blarel (2022)PermalinkSelf-attention and generative adversarial networks for algae monitoring / Nhut Hai Huynh in European journal of remote sensing, vol 55 n° 1 (2022)PermalinkEarly detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany / Kathrin Einzmann in Remote sensing of environment, vol 266 (December 2021)PermalinkFlexible Gabor-based superpixel-level unsupervised LDA for hyperspectral image classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)PermalinkBagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: A comparative evaluation / Hamid Jafarzadeh in Remote sensing, vol 13 n° 21 (November-1 2021)PermalinkAdaptive edge preserving maps in Markov random fields for hyperspectral image classification / Chao Pan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkEndmember bundle extraction based on multiobjective optimization / Rong Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkHyperspectral image fusion and multitemporal image fusion by joint sparsity / Han Pan in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)PermalinkLarge-area inventory of species composition using airborne laser scanning and hyperspectral data / Hans Ole Ørka in Silva fennica, vol 55 n° 4 (September 2021)PermalinkMulti-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)PermalinkTwo hidden layer neural network-based rotation forest ensemble for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 16 ([01/09/2021])PermalinkUnsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification / Divyesh Varade in Geocarto international, vol 36 n° 15 ([15/08/2021])PermalinkTarget-constrained interference-minimized band selection for hyperspectral target detection / Xiaodi Shang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkAn incremental isomap method for hyperspectral dimensionality reduction and classification / Yi Ma in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)PermalinkEvaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities / Jingjing Zhou in Remote sensing, vol 13 n° 11 (June-1 2021)PermalinkMultiscale context-aware ensemble deep KELM for efficient hyperspectral image classification / Bobo Xi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)PermalinkUnsupervised multi-level feature extraction for improvement of hyperspectral classification / Qiaoqiao Sun in Remote sensing, vol 13 n° 8 (April-2 2021)PermalinkAnti-cross validation technique for constructing and boosting random subspace neural network ensembles for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 6 ([01/04/2021])PermalinkAutomatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network / Jian Sun in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkHyperspectral image denoising via clustering-based latent variable in variational Bayesian framework / Peyman Azimpour in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkApplication of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring / Gopal Krishna in Geocarto international, vol 36 n° 5 ([15/03/2021])PermalinkDetection of subpixel targets on hyperspectral remote sensing imagery based on background endmember extraction / Xiaorui Song in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)PermalinkImproving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR / Kabir Peerbhay in Geocarto international, vol 36 n° 4 ([01/03/2021])PermalinkCorrentropy-based spatial-spectral robust sparsity-regularized hyperspectral unmixing / Xiaorun Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)Permalink3D urban scene understanding by analysis of LiDAR, color and hyperspectral data / David Duque-Arias (2021)PermalinkApport des méthodes : imagerie drone, LiDAR et imagerie hyperspectrale pour l’étude du littoral vendéen / Mathis Baudis (2021)PermalinkApport de la modélisation physique pour la cartographie de la biodiversité végétale en forêts tropicales par télédétection optique / Dav Ebengo Mwampongo (2021)PermalinkPermalinkHyperspectral and multispectral image fusion via graph Laplacian-guided coupled tensor decomposition / Yuanyang Bu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkModel based signal processing techniques for nonconventional optical imaging systems / Daniele Picone (2021)PermalinkReconnaissance spécifique et cartographie des arbres de la canopée en forêt tropicale en Guyane française par fusion de données lidar et hyperspectrales appliquées aux besoins de la gestion forestière / Anthony Laybros (2021)PermalinkRemote sensing and GIS / Basudeb Bhatta (2021)PermalinkSpectral variability in hyperspectral unmixing : Multiscale, tensor, and neural network-based approaches / Ricardo Augusto Borsoi (2021)PermalinkPermalinkTélédétection hyperspectrale pour l’identification et la caractérisation de minéraux industriels / Ronan Rialland (2021)PermalinkTélédétection synchronisée hyperspectrale et LiDAR à retour d’onde complet : application au suivi des littoraux sableux de la région Pays-de-la-Loire / Giovanni Frati (2021)PermalinkHyperspectral band selection via optimal neighborhood reconstruction / Qi Wang in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)PermalinkNonlocal graph convolutional networks for hyperspectral image classification / Lichao Mou in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)PermalinkLearning-based hyperspectral imagery compression through generative neural networks / Chubo Deng in Remote sensing, vol 12 n° 21 (November 2020)PermalinkVNIR-SWIR superspectral mineral mapping: An example from Cuprite, Nevada / Kathleen E. Johnson in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 11 (November 2020)PermalinkDrought stress detection in juvenile oilseed rape using hyperspectral imaging with a focus on spectra variability / Wiktor R. Żelazny in Remote sensing, vol 12 n° 20 (October-2 2020)PermalinkComparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands / Bappa Das in Geocarto international, vol 35 n° 13 ([01/10/2020])PermalinkA novel spectral–spatial based adaptive minimum spanning forest for hyperspectral image classification / Jing Lv in Geoinformatica, vol 24 n° 4 (October 2020)PermalinkHyperspectral unmixing using orthogonal sparse prior-based autoencoder with hyper-laplacian loss and data-driven outlier detection / Zeyang Dou in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)PermalinkX-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkClassification of hyperspectral and LiDAR data using coupled CNNs / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkComplete and accurate data correction for seamless mosaicking of airborne hyperspectral images: A case study at a mining site in Inner Mongolia, China / Kun Tan in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)PermalinkSubpixel-pixel-superpixel-based multiview active learning for hyperspectral images classification / Yu Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkUsing spectral indices to estimate water content and GPP in sphagnum moss and other peatland vegetation / Kirsten J. Lees in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkEnsemble learning for hyperspectral image classification using tangent collaborative representation / Hongjun Su in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)PermalinkHyperspectral classification with noisy label detection via superpixel-to-pixel weighting distance / Bing Tu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)PermalinkAn integrated approach to registration and fusion of hyperspectral and multispectral images / Yuan Zhou in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)PermalinkA convolutional neural network with mapping layers for hyperspectral image classification / Rui Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)PermalinkHyperspectral image clustering with Albedo recovery Fuzzy C-Means / Peyman Azimpour in International Journal of Remote Sensing IJRS, vol 41 n° 16 (01-10 May 2020)PermalinkAbove-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging / Bo Li in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)PermalinkDetection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis / T. Poblete in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)PermalinkMultichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization / Puhong Duan in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkMultiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images / Hao Cui in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkA Single Model CNN for Hyperspectral Image Denoising / Alessandro Maffei in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkA novel nonlinear hyperspectral unmixing approach for images of oil spills at sea / Ying Li in International Journal of Remote Sensing IJRS, vol 41 n° 12 (20 - 30 March 2020)PermalinkDimension reduction methods applied to coastline extraction on hyperspectral imagery / Ozan Arslan in Geocarto international, vol 35 n° 4 ([15/03/2020])PermalinkAn original method for tree species classification using multitemporal multispectral and hyperspectral satellite data / Olga Grigorieva in Silva fennica, vol 54 n° 2 (March 2020)PermalinkGeneralized tensor regression for hyperspectral image classification / Jianjun Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkA restrictive polymorphic ant colony algorithm for the optimal band selection of hyperspectral remote sensing images / Xiaohui Ding in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)Permalink10th Colour and Visual Computing Symposium 2020 (CVCS 2020), Gjøvik, Norway, and Virtual, September 16-17, 2020 / Jean-Baptiste Thomas (2020)PermalinkApplication of digital image processing in automated analysis of insect leaf mines / Yee Man Theodora Cho (2020)PermalinkComparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)PermalinkFusion of 3D point clouds and hyperspectral data for the extraction of geometric and radiometric features of trees / Eduardo Alejandro Tusa Jumbo (2020)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalink