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analyse des mélanges spectrauxSynonyme(s)SMA démélange spectral |
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Learning disentangled representations of satellite image time series in a weakly supervised manner / Eduardo Hugo Sanchez (2021)
Titre : Learning disentangled representations of satellite image time series in a weakly supervised manner Type de document : Thèse/HDR Auteurs : Eduardo Hugo Sanchez, Auteur ; Mathieu Serrurier, Directeur de thèse ; Mathias Ortner, Directeur de thèse Editeur : Toulouse : Université de Toulouse 3 Paul Sabatier Année de publication : 2021 Importance : 176 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, Spécialité Informatique et TélécommunicationsLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] analyse des mélanges temporels
[Termes IGN] apprentissage automatique
[Termes IGN] classification dirigée
[Termes IGN] classification non dirigée
[Termes IGN] image Sentinel-MSI
[Termes IGN] réseau antagoniste génératif
[Termes IGN] segmentation d'image
[Termes IGN] série temporelleIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This work focuses on learning data representations of satellite image time series via an unsupervised learning approach. The main goal is to enforce the data representation to capture the relevant information from the time series to perform other applications of satellite imagery. However, extracting information from satellite data involves many challenges since models need to deal with massive amounts of images provided by Earth observation satellites. Additionally, it is impossible for human operators to label such amount of images manually for each individual task (e.g. classification, segmentation, change detection, etc.). Therefore, we cannot use the supervised learning framework which achieves state-of-the-art results in many tasks.To address this problem, unsupervised learning algorithms have been proposed to learn the data structure instead of performing a specific task. Unsupervised learning is a powerful approach since no labels are required during training and the knowledge acquired can be transferred to other tasks enabling faster learning with few labels.In this work, we investigate the problem of learning disentangled representations of satellite image time series where a shared representation captures the spatial information across the images of the time series and an exclusive representation captures the temporal information which is specific to each image. We present the benefits of disentangling the spatio-temporal information of time series, e.g. the spatial information is useful to perform time-invariant image classification or segmentation while the knowledge about the temporal information is useful for change detection. To accomplish this, we analyze some of the most prevalent unsupervised learning models such as the variational autoencoder (VAE) and the generative adversarial networks (GANs) as well as the extensions of these models to perform representation disentanglement. Encouraged by the successful results achieved by generative and reconstructive models, we propose a novel framework to learn spatio-temporal representations of satellite data. We prove that the learned disentangled representations can be used to perform several computer vision tasks such as classification, segmentation, information retrieval and change detection outperforming other state-of-the-art models. Nevertheless, our experiments suggest that generative and reconstructive models present some drawbacks related to the dimensionality of the data representation, architecture complexity and the lack of disentanglement guarantees. In order to overcome these limitations, we explore a recent method based on mutual information estimation and maximization for representation learning without relying on image reconstruction or image generation. We propose a new model that extends the mutual information maximization principle to disentangle the representation domain into two parts. In addition to the experiments performed on satellite data, we show that our model is able to deal with different kinds of datasets outperforming the state-of-the-art methods based on GANs and VAEs. Furthermore, we show that our mutual information based model is less computationally demanding yet more effective. Finally, we show that our model is useful to create a data representation that only captures the class information between two images belonging to the same category. Disentangling the class or category of an image from other factors of variation provides a powerful tool to compute the similarity between pixels and perform image segmentation in a weakly-supervised manner. Note de contenu : Introduction
1- Background
2- Representation disentanglement via VAEs/GANs
3- Representation disentanglement via mutual information estimation
ConclusionNuméro de notice : 24065 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique et Télécommunications : Toulouse 3 : 2021 Organisme de stage : nstitut de Recherche en Informatique de Toulouse IRIT DOI : sans En ligne : http://thesesups.ups-tlse.fr/4971/1/2021TOU30032.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101822 Spectral variability in hyperspectral unmixing : Multiscale, tensor, and neural network-based approaches / Ricardo Augusto Borsoi (2021)
Titre : Spectral variability in hyperspectral unmixing : Multiscale, tensor, and neural network-based approaches Type de document : Thèse/HDR Auteurs : Ricardo Augusto Borsoi, Auteur ; Cédric Richard, Directeur de thèse ; José Carlos Moreira Bermudez, Directeur de thèse Editeur : Nice : Université Côte d'Azur Année de publication : 2021 Importance : 187 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse présentée en vue de l'obtention du grade de docteur science pour l’ingénieur de l’Université Côte d'AzurLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de mélange spectral d’extrémités multiples
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] image hyperspectrale
[Termes IGN] image multitemporelle
[Termes IGN] réseau antagoniste génératif
[Termes IGN] signature spectrale
[Termes IGN] tenseurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EMs), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an image. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the endmembers, what propagates significant mismodeling errors throughout the whole unmixing process and compromises the quality of the estimated abundances. Therefore, significant effort have been recently dedicated to mitigate the effects of spectral variability in SU. However, many challenges still remain in how to best explore a priori information about the problem in order to improve the quality, the robustness and the efficiency of SU algorithms that account for spectral variability. In this thesis, new strategies are developed to address spectral variability in SU. First, an (over)-segmentation-based multiscale regularization strategy is proposed to explore spatial information about the abundance maps more effectively. New algorithms are then proposed for both semi-supervised and blind SU, leading to improved abundance reconstruction performance at a small computational complexity. Afterwards, three new models are proposed to represent spectral variability of the EMs in SU, using parametric, tensor, and neural network-based representations for EM spectra at each image pixel. The parametric model introduces pixel-dependent scaling factors over a reference EM matrix to model arbitrary spectral variability, while the tensor-based representation allows one to exploit the high-dimensional nature of the data by means of its underlying low-rank structure. Generative neural networks (such as variational autoencoders or generative adversarial networks) finally allow one to model the low-dimensional manifold of the spectral signatures of the materials more effectively. The proposed models are used to devise three new blind SU algorithms, and to perform data augmentation in library-based SU. Finally, we provide a brief overview of work which extends the proposed strategies to new problems in SU and in hyperspectral image analysis. This includes the use of the multiscale abundance regularization in nonlinear SU, modeling spectral variability and accounting for sudden changes when performing SU and change detection of multitemporal hyperspectral images, and also accounting for spectral variability and changes in the multimodal (i.e., hyperspectral and multispectral) image fusion problem. Note de contenu : 1- Introduction
2- Origin of linear mixing model spectral variability in hyperspectral images
3- A ultiscale spatial regularization for fast unmixing with spectral librairies
4- A data dependent multiscale model for spectral unmixing with specral variability
5- Generalized linear mixing model accounting for endmember variability
6- Low-rank tensor modeling for spectral unmixing accounting for spectral variability
7- Deep generative endmembers modeling: An application to unsupervised spectral unmixing
8- Deep generative models for library augmentation in multiple endmember spectral mixture analysis
9- And now for something different...
10- ConclusionsNuméro de notice : 28487 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : thèse de Doctorat : Sciences pour l'Ingénieur : Côte d'Azur : 2021 Organisme de stage : Laboratoire J.-L. Lagrange, Observatoire de la Côte d’Azur DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03253631/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99188 Unmixing-based Sentinel-2 downscaling for urban land cover mapping / Fei Xu in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)
[article]
Titre : Unmixing-based Sentinel-2 downscaling for urban land cover mapping Type de document : Article/Communication Auteurs : Fei Xu, Auteur ; Ben Somers, Auteur Année de publication : 2021 Article en page(s) : pp 133 - 154 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] bande spectrale
[Termes IGN] Berlin
[Termes IGN] Bruxelles
[Termes IGN] cartographie urbaine
[Termes IGN] Cologne
[Termes IGN] corrélation
[Termes IGN] fusion d'images
[Termes IGN] image Sentinel-MSI
[Termes IGN] matrice de co-occurrence
[Termes IGN] occupation du solRésumé : (auteur) With the launch of Sentinel-2 new opportunities for large scale urban mapping arise. However, the spectral information embedded in the Sentinel-2 20 m spatial resolution bands cannot yet be fully explored in heterogeneous urban landscapes. The 20 m image pixels are often composed of different land covers, resulting in a difficult to interpret mixed pixel spectrum. Here, we propose an unmixing-based image fusion algorithm (UnFuSen2) that self-adapts to the spectral variability of varying land covers and improves the image fusion accuracy by constraining the unmixing equations on the basis of spectral mixing models and the correlation between spectral bands of coarse and fine spatial resolution, respectively. When compared to alternative state-of-the-art downscaling methods UnFuSen2 consistently showed the highest accuracy when applied across test sites in three different European cities (RMSEUnFuSen2 = 203 vs RMSEalternatives = [252, 337]). In a next step, we applied Multiple Endmember Spectral Mixture Analysis (MESMA) on the downscaled Sentinel-2 image cube (i.e. ten 10 m bands) to generate subpixel urban land cover fractions. We compared our MESMA results against the traditional MESMA output as applied on the original Sentinel-2 image cube (i.e. four 10 m bands and six 20 m bands) and tested its robustness against reference data obtained over all three study sites. Results revealed an average decrease in RMSE of respectively 18% and 8% for impervious surface and vegetation fractions when our approach was compared to the traditional MESMA outcomes. Numéro de notice : A2021-015 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.009 Date de publication en ligne : 26/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.009 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96419
in ISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 133 - 154[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021011 SL Revue Centre de documentation Revues en salle Disponible 081-2021013 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021012 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Mapping tree species deciduousness of tropical dry forests combining reflectance, spectral unmixing, and texture data from high-resolution imagery / Astrid Helena Huechacona-Ruiz in Forests, vol 11 n°11 (November 2020)
[article]
Titre : Mapping tree species deciduousness of tropical dry forests combining reflectance, spectral unmixing, and texture data from high-resolution imagery Type de document : Article/Communication Auteurs : Astrid Helena Huechacona-Ruiz, Auteur ; Juan Manuel Dupuy, Auteur ; Naomi B. Schwartz, Auteur Année de publication : 2020 Article en page(s) : n° 1234 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] arbre caducifolié
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] distribution spatiale
[Termes IGN] forêt tropicale
[Termes IGN] image proche infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] réflectance
[Termes IGN] texture d'image
[Termes IGN] YucatanRésumé : (auteur) In tropical dry forests, deciduousness (i.e., leaf shedding during the dry season) is an important adaptation of plants to cope with water limitation, which helps trees adjust to seasonal drought. Deciduousness is also a critical factor determining the timing and duration of carbon fixation rates, and affecting energy, water, and carbon balance. Therefore, quantifying deciduousness is vital to understand important ecosystem processes in tropical dry forests. The aim of this study was to map tree species deciduousness in three types of tropical dry forests along a precipitation gradient in the Yucatan Peninsula using Sentinel-2 imagery. We propose an approach that combines reflectance of visible and near-infrared bands, normalized difference vegetation index (NDVI), spectral unmixing deciduous fraction, and several texture metrics to estimate the spatial distribution of tree species deciduousness. Deciduousness in the study area was highly variable and decreased along the precipitation gradient, while the spatial variation in deciduousness among sites followed an inverse pattern, ranging from 91.5 to 43.3% and from 3.4 to 9.4% respectively from the northwest to the southeast of the peninsula. Most of the variation in deciduousness was predicted jointly by spectral variables and texture metrics, but texture metrics had a higher exclusive contribution. Moreover, including texture metrics as independent variables increased the variance of deciduousness explained by the models from R2 = 0.56 to R2 = 0.60 and the root mean square error (RMSE) was reduced from 16.9% to 16.2%. We present the first spatially continuous deciduousness map of the three most important vegetation types in the Yucatan Peninsula using high-resolution imagery. Numéro de notice : A2020-756 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11111234 Date de publication en ligne : 23/11/2020 En ligne : https://doi.org/10.3390/f11111234 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96468
in Forests > vol 11 n°11 (November 2020) . - n° 1234[article]Hyperspectral 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)
[article]
Titre : Hyperspectral unmixing using orthogonal sparse prior-based autoencoder with hyper-laplacian loss and data-driven outlier detection Type de document : Article/Communication Auteurs : Zeyang Dou, Auteur ; Kun Gao, Auteur ; Xiaodian Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6550 - 6564 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] distribution de Gauss
[Termes IGN] erreur
[Termes IGN] image hyperspectrale
[Termes IGN] reconstruction d'image
[Termes IGN] valeur aberranteRésumé : (auteur) Hyperspectral unmixing, which estimates end-members and their corresponding abundance fractions simultaneously, is an important task for hyperspectral applications. In this article, we propose a new autoencoder-based hyperspectral unmixing model with three novel components. First, we propose a new sparse prior to abundance maps. The proposed prior, called orthogonal sparse prior (OSP), is based on the observations that different abundance maps are close to orthogonal because, generally, no more than two end-members are mixed within one pixel. As opposed to the conventional norm-based sparse prior that assumes the abundance maps are independent, the proposed OSP explores the orthogonality between the abundance maps. Second, we propose the hyper-Laplacian loss to model the reconstruction error. The key observation is that the reconstruction error distribution usually has a heavy-tailed shape, which is better modeled by the hyper-Laplacian distribution rather than the commonly used Gaussian distribution. Third, to ease the side effect of outliers for end-member initializations, we develop a data-driven approach to detect outliers from the raw hyperspectral images. Extensive experiments on both synthetic and real-world data sets show that the proposed method significantly and consistently outperforms the compared state-of-the-art methods, with up to more than 50% improvements. Numéro de notice : A2020-532 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2977819 Date de publication en ligne : 16/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2977819 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95715
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6550 - 6564[article]Monitoring narrow mangrove stands in Baja California Sur, Mexico using linear spectral unmixing / Jonathan B. Thayn in Marine geodesy, Vol 43 n° 5 (September 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)PermalinkAssessing environmental impacts of urban growth using remote sensing / John C. Trinder in Geo-spatial Information Science, vol 23 n° 1 (March 2020)Permalink10th Colour and Visual Computing Symposium 2020 (CVCS 2020), Gjøvik, Norway, and Virtual, September 16-17, 2020 / Jean-Baptiste Thomas (2020)PermalinkPotential of Landsat-8 and Sentinel-2A composite for land use land cover analysis / Divyesh Varade in Geocarto international, vol 34 n° 14 ([30/10/2019])PermalinkPartial linear NMF-based unmixing methods for detection and area estimation of photovoltaic panels in urban hyperspectral remote sensing data / Moussa Sofiane Karoui in Remote sensing, vol 11 n° 18 (September 2019)PermalinkBurn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data / Alfonso Fernández-Manso in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)PermalinkHyperspectral analysis of soil polluted with four types of hydrocarbons / Laura A. Reséndez-Hernández in Geocarto international, vol 34 n° 9 ([15/06/2019])Permalink3D hyperspectral point cloud generation: Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction / Maximilian Brell in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkSpectral unmixing with perturbed endmembers / Reza Arablouei in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)Permalink