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Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies / Guangqin Song in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)
[article]
Titre : Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies Type de document : Article/Communication Auteurs : Guangqin Song, Auteur ; Shengbiao Wu, Auteur ; Calvin K.F. Lee, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 19 - 33 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme SLIC
[Termes IGN] apprentissage profond
[Termes IGN] canopée
[Termes IGN] classification dirigée
[Termes IGN] diagnostic foliaire
[Termes IGN] Enhanced vegetation index
[Termes IGN] feuille (végétation)
[Termes IGN] forêt tropicale
[Termes IGN] Panama
[Termes IGN] phénologie
[Termes IGN] photosynthèse
[Termes IGN] segmentation sémantique
[Termes IGN] série temporelle
[Termes IGN] superpixel
[Termes IGN] variation saisonnièreRésumé : (auteur) Tropical leaf phenology—particularly its variability at the tree-crown scale—dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red–greenblue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. We first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams. Numéro de notice : A2022-009 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.10.023 Date de publication en ligne : 10/11/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.10.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99057
in ISPRS Journal of photogrammetry and remote sensing > vol 183 (January 2022) . - pp 19 - 33[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2022011 SL Revue Centre de documentation Revues en salle Disponible 081-2022013 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2022012 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Multi-view urban scene classification with a complementary-information learning model / Wanxuan Geng in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 1 (January 2022)
[article]
Titre : Multi-view urban scene classification with a complementary-information learning model Type de document : Article/Communication Auteurs : Wanxuan Geng, Auteur ; Weixun Zhou, Auteur ; Shuanggen Jin, Auteur Année de publication : 2022 Article en page(s) : pp 65 - 72 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] données de terrain
[Termes IGN] données multisources
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion de données multisource
[Termes IGN] image aérienne
[Termes IGN] niveau du sol
[Termes IGN] précision de la classification
[Termes IGN] scène urbaineRésumé : (Auteur) Traditional urban scene-classification approaches focus on images taken either by satellite or in aerial view. Although single-view images are able to achieve satisfactory results for scene classification in most situations, the complementary information provided by other image views is needed to further improve performance. Therefore, we present a complementary information-learning model (CILM) to perform multi-view scene classification of aerial and ground-level images. Specifically, the proposed CILM takes aerial and ground-level image pairs as input to learn view-specific features for later fusion to integrate the complementary information. To train CILM, a unified loss consisting of cross entropy and contrastive losses is exploited to force the network to be more robust. Once CILM is trained, the features of each view are extracted via the two proposed feature-extraction scenarios and then fused to train the support vector machine classifier for classification. The experimental results on two publicly available benchmark data sets demonstrate that CILM achieves remarkable performance, indicating that it is an effective model for learning complementary information and thus improving urban scene classification. Numéro de notice : A2022-063 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00062R2 Date de publication en ligne : 01/01/2022 En ligne : https://doi.org/10.14358/PERS.21-00062R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99708
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 1 (January 2022) . - pp 65 - 72[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2022011 SL Revue Centre de documentation Revues en salle Disponible Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation / Hang Zhang in Pattern recognition, vol 121 (January 2022)
[article]
Titre : Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation Type de document : Article/Communication Auteurs : Hang Zhang, Auteur ; Haili Li, Auteur ; Ning Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108201 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification floue
[Termes IGN] classification pixellaire
[Termes IGN] filtre
[Termes IGN] segmentation d'image
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) Spatial information is often used to enhance the robustness of traditional fuzzy c-means (FCM) clustering algorithms. Although some recently emerged improvements are remarkable, the computational complexity of these algorithms is high, which may lead to lack of practicability. To address this problem, an efficient variant named the fuzzy clustering algorithm with variable multi-pixel fitting spatial information (FCM-VMF) is presented. First, a fuzzy clustering algorithm with multi-pixel fitting spatial information (FCM-MF) is developed. Specifically, by dividing the input image into several filter windows, the spatial information of all pixels in each filter window can be obtained simultaneously by fitting the pixels in its corresponding neighbourhood window, which enormously reduces the computational complexity. However, the FCM-MF may result in the loss of edge information. Therefore, the FCM-VMF integrates a variable window strategy with FCM-MF. In this strategy, to preserve more edge information, the sizes of the filter window and generalized neighbourhood window are adaptively reduced. The experimental results show that FCM-VMF is as effective as some recent algorithms. Notably, the FCM-VMF has extremely high efficiency, which means it has a better prospect of application. Numéro de notice : A2022-100 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.patcog.2021.108201 Date de publication en ligne : 26/07/2021 En ligne : https://doi.org/10.1016/j.patcog.2021.108201 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99564
in Pattern recognition > vol 121 (January 2022) . - n° 108201[article]A 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)
[article]
Titre : A novel unmixing-based hypersharpening method via convolutional neural network Type de document : Article/Communication Auteurs : Xiaochen Lu, Auteur ; Tong Li, Auteur ; Junping Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5503614 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] pouvoir de résolution spectraleRésumé : (auteur) Hypersharpening (namely, hyperspectral (HS) and multispectral (MS) image fusion) aims at enhancing the spatial resolution of HS image via an auxiliary higher resolution MS image. Currently, numerous hypersharpening methods are proposed successively, among which the unmixing-based approaches have been widely researched and demonstrated their effectiveness in the spectral fidelity aspect. However, existing unmixing-based fusion methods substantially employ mathematical techniques to solve the spectral mixture model, without taking full advantage of the collaborative spatial–spectral information that is usually helpful for abundance estimation improvement. To overcome this drawback, in this article, a novel unmixing-based HS and MS image fusion method, via a convolutional neural network (CNN), is proposed to promote spectral fidelity. The main idea of this work is to use CNN to fully explore the spatial information and the spectral information of both HS and MS images simultaneously, thereby enhancing the accuracy of estimating the abundance maps. Experiments on four simulated and real remote sensing data sets demonstrate that the proposed method is beneficial to the spectral fidelity of the fused images compared with some state-of-the-art algorithms. Meanwhile, it is also easy to implement and has a certain advantage in running time. Numéro de notice : A2022-028 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3063105 Date de publication en ligne : 22/03/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3063105 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99264
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 1 (January 2022) . - n° 5503614[article]Optimization of deep neural networks: A functional perspective with applications in image classification / Simon Roburin (2022)
Titre : Optimization of deep neural networks: A functional perspective with applications in image classification Type de document : Thèse/HDR Auteurs : Simon Roburin, Auteur ; Mathieu Aubry, Directeur de thèse Editeur : Champs-sur-Marne : Ecole des Ponts ParisTech Année de publication : 2022 Importance : 141 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de Doctorat de l'Ecole des Ponts ParisTech, spécialité Mathématiques AppliquéesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage profond
[Termes IGN] classification par nuées dynamiques
[Termes IGN] mathématiques appliquées
[Termes IGN] optimisation (mathématiques)
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Despite numerous successes in a wide range of industrial and scientific applications, the learning process of deep neural networks is poorly understood. Loosely speaking, learning aims at finding the network parameters that not only minimize the network errors on a set of training examples but also yield correct predictions on unseen data. Under the prism of optimization, it boils down to minimizing a high dimensional non-convex function. Generalization can generally be expected when one has access to very large datasets and assumes that both training examples and unseen data are sampled from identically independently distributed random variables. The goal of this thesis is to develop analytical tools to better understand neural network optimization and to improve the design of training algorithms in the context of image classification. Note de contenu : 1- Introduction
2- Literature review
3- Impact of Normalization Layers on Optimization
4- Avoid learning spurious correlations
5- ConclusionNuméro de notice : 24098 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Mathématiques Appliquées : Ponts ParisTech : 2022 Organisme de stage : LIGM-IMAGINE En ligne : https://hal.science/tel-03968114v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102573 A PCA-PD fusion method for change detection in remote sensing multi temporal images / Soltana Achour in Geocarto international, vol 37 n° 1 ([01/01/2022])PermalinkPermalinkSelf-attention and generative adversarial networks for algae monitoring / Nhut Hai Huynh in European journal of remote sensing, vol 55 n° 1 (2022)PermalinkSemantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+ / Zhimin Wang in Computers & geosciences, vol 158 (January 2022)PermalinkSenRVM: A multi-modal deep learning regression methodology for continuous vegetation monitoring with dense temporal NDVI time series / Anatol Garioud (2022)PermalinkPermalinkUrban infrastructure audit: an effective protocol to digitize signalized intersections by mining street view images / Xiao Li in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)PermalinkAdaptive feature weighted fusion nested U-Net with discrete wavelet transform for change detection of high-resolution remote sensing images / Congcong Wang in Remote sensing, vol 13 n° 24 (December-2 2021)PermalinkEfficient occluded road extraction from high-resolution remote sensing imagery / Dejun Feng in Remote sensing, vol 13 n° 24 (December-2 2021)PermalinkMapping temperate forest tree species using dense Sentinel-2 time series / Jan Hemmerling in Remote sensing of environment, vol 267 (December-15 2021)Permalink