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Integrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images / Yijie Tang in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 180 (October 2021) . - pp 130 - 150 Titre : Integrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images Type de document : Article/Communication Auteurs : Yijie Tang, Auteur ; Qunming Wang, Auteur ; Xiaohua Tong, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 130 - 150 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] données spatiotemporelles [Termes IGN] fusion d'images [Termes IGN] image multibande [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-OLCI [Termes IGN] pouvoir de résolution géométrique [Termes IGN] réduction d'échelle [Termes IGN] réflectance Résumé : (auteur) Sentinel-3 is a newly launched satellite implemented by the European Space Agency (ESA) for global observation. The Ocean and Land Colour Imager (OLCI) sensor onboard Sentinel-3 provides 21 band images with a fine spectral resolution and is of great value for ocean, land and atmospheric monitoring. The two platforms (Sentinel-3A and -3B) can provide OLCI images at an almost daily temporal resolution. The coarse spatial resolution of the 21 band OLCI images (i.e., 300 m), however, limits greatly their utility for local, precise monitoring. Sentinel-2, another satellite provided by ESA, carries the Multispectral Imager (MSI) sensor which can supply much finer spatial resolution (e.g., 10 m and 20 m) images. This paper introduces a new fusion framework integrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images, which has two parts. Based on bands with similar wavelengths (i.e., bands 2, 3, 4 and 8a for Sentinel-2 and bands Oa4, Oa6, Oa8 and Oa17 for Sentinel-3), the four Sentinel-3 bands are first downscaled to the spatial resolution of Sentinel-2 images by applying spatio-temporal fusion to Sentinel-2 MSI and Sentinel-3 OLCI images. Then, to take full advantage of all 21 available OLCI bands of the Sentinel-3 images, the extended image pair-based spatio-spectral fusion (EIPSSF) method is proposed in this paper to downscale the other 17 bands. EIPSSF is performed based on the new concept of the extended image pair (EIP) and by exploiting existing spatio-temporal fusion approaches. The framework consisting of spatio-temporal and spatio-spectral fusion is entirely general, which provides a practical solution for comprehensive downscaling of Sentinel-3 OLCI images for fine spatial, temporal and spectral resolution monitoring. Numéro de notice : A2021-654 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.08.012 date de publication en ligne : 24/08/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.08.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98384 [article]

Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery / Run Yu in Forest ecology and management, vol 497 (1 October 2021)  

Public [article]  inForest ecology and management > vol 497 (1 October 2021) . - n° 119493 Titre : Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery Type de document : Article/Communication Auteurs : Run Yu, Auteur ; Youqing Luo, Auteur ; Quan Zhou, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119493 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] dépérissement [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] maladie phytosanitaire [Termes IGN] milieu tropical [Termes IGN] peuplement mélangé [Termes IGN] Pinus (genre) [Termes IGN] Pinus massoniana [Termes IGN] réflectance spectrale [Termes IGN] Ulmus (genre) Résumé : (auteur) Pine wilt disease (PWD) is a global devastating threat to forest ecosystems. Therefore, a feasible and effective approach to precisely monitor PWD infection is indispensable, especially at the early stages. However, a precise definition of “early stage” and a rapid and high-efficiency method to detect PWD infection have not been well established. In this study, we systematically divided the PWD infection into green, early, middle, and late stages based on the needle color, the resin secretion, and whether the pine wood nematode (PWN) was carried. Simultaneously, an unmanned aerial vehicle (UAV) equipped with multispectral cameras was used to obtain images. Two target detection algorithms (Faster R-CNN and YOLOv4) and two traditional machine learning algorithms based on feature extraction (random forest and support vector machine) were employed to realize the recognition of infected pine trees. Moreover, we took into consideration of the influence of green broad-leaved trees on the identification of pine trees at the early stage of PWD infection. We obtained the following results: (1) the accuracy of Faster R-CNN (60.98–66.7%) was higher than that of YOLOv4 (57.07–63.55%), but YOLOv4 outperformed in terms of model size, processing speed, training time, and testing time; (2) although the traditional machine learning models had higher accuracy (73.28–79.64%), they were not able to directly identify the object from the images; (3) the accuracy of early detection of PWD infection showed an increase of 3.72–4.29%, from 42.36–44.59% to 46.08–48.88%, when broad-leaved trees were considered. In this study, the combination of UAV-based multispectral images and target detection algorithms allowed us to monitor the occurrence of PWD and obtain the distribution of infected trees at an early stage, which can provide technical support for the prevention and control of PWD. Numéro de notice : A2021-658 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2021.119493 En ligne : https://doi.org/10.1016/j.foreco.2021.119493 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98395 [article]

Binary space partitioning visibility tree for polygonal and environment light rendering / Hiroki Okuno in The Visual Computer, vol 37 n° 9 - 11 (September 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 9 - 11 (September 2021) . - pp 2499 - 2511 Titre : Binary space partitioning visibility tree for polygonal and environment light rendering Type de document : Article/Communication Auteurs : Hiroki Okuno, Auteur ; Kei Iwasaki, Auteur Année de publication : 2021 Article en page(s) : pp 2499 - 2511 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] arbre BSP [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] éclairage [Termes IGN] éclairement lumineux [Termes IGN] équation intégrale [Termes IGN] intensité lumineuse [Termes IGN] ombre [Termes IGN] polygone [Termes IGN] réflectance [Termes IGN] visibilité (optique) Résumé : (auteur) In this paper, we present a geometric approach to render shadows for physically based materials under polygonal light sources. Direct illumination calculation from a polygonal light source involves the triple product integral of the lighting, the bidirectional reflectance distribution function (BRDF), and the visibility function over the polygonal domain, which is computation intensive. To achieve real-time performance, work on polygonal light shading exploits analytical solutions of boundary integrals along the edges of the polygonal light at the cost of lacking shadowing effects. We introduce a hierarchical representation for the precomputed visibility function to retain the merits of closed-form solutions for boundary integrals. Our method subdivides the polygonal light into a set of polygons visible from a point to be shaded. Experimental results show that our method can render complex shadows with a GGX microfacet BRDF from polygonal light sources at interactive frame rates. In addition, our visibility representation can be easily incorporated into environment lighting. Numéro de notice : A2021-644 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-021-02181-8 date de publication en ligne : 14/06/2021 En ligne : https://doi.org/10.1007/s00371-021-02181-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98345 [article]

A deep translation (GAN) based change detection network for optical and SAR remote sensing images / Xinghua Li in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 179 (September 2021) . - pp 14 - 34 Titre : A deep translation (GAN) based change detection network for optical and SAR remote sensing images Type de document : Article/Communication Auteurs : Xinghua Li, Auteur ; Zhengshun Du, Auteur ; Yanyuan Huang, Auteur ; Zhenyu Tan, Auteur Année de publication : 2021 Article en page(s) : pp 14 - 34 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] détection de changement [Termes IGN] image à très haute résolution [Termes IGN] image optique [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] méthode robuste [Termes IGN] polarisation [Termes IGN] réseau antagoniste génératif [Termes IGN] réseau neuronal profond [Termes IGN] zone d'intérêt Résumé : (Editeur) With the development of space-based imaging technology, a larger and larger number of images with different modalities and resolutions are available. The optical images reflect the abundant spectral information and geometric shape of ground objects, whose qualities are degraded easily in poor atmospheric conditions. Although synthetic aperture radar (SAR) images cannot provide the spectral features of the region of interest (ROI), they can capture all-weather and all-time polarization information. In nature, optical and SAR images encapsulate lots of complementary information, which is of great significance for change detection (CD) in poor weather situations. However, due to the difference in imaging mechanisms of optical and SAR images, it is difficult to conduct their CD directly using the traditional difference or ratio algorithms. Most recent CD methods bring image translation to reduce their difference, but the results are obtained by ordinary algebraic methods and threshold segmentation with limited accuracy. Towards this end, this work proposes a deep translation based change detection network (DTCDN) for optical and SAR images. The deep translation firstly maps images from one domain (e.g., optical) to another domain (e.g., SAR) through a cyclic structure into the same feature space. With the similar characteristics after deep translation, they become comparable. Different from most previous researches, the translation results are imported to a supervised CD network that utilizes deep context features to separate the unchanged pixels and changed pixels. In the experiments, the proposed DTCDN was tested on four representative data sets from Gloucester, California, and Shuguang village. Compared with state-of-the-art methods, the effectiveness and robustness of the proposed method were confirmed. Numéro de notice : A2021-574 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.07.007 date de publication en ligne : 23/07/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.07.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98174 [article]

Variational bayesian compressive multipolarization indoor radar imaging / Van Ha Tang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7459 - 7474 Titre : Variational bayesian compressive multipolarization indoor radar imaging Type de document : Article/Communication Auteurs : Van Ha Tang, Auteur ; Abdesselam Bouzerdoum, Auteur ; Son Lam Phung, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7459 - 7474 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] acquisition comprimée [Termes IGN] détection à travers-le-mur [Termes IGN] estimation bayesienne [Termes IGN] fouillis d'échos [Termes IGN] image radar [Termes IGN] inférence statistique [Termes IGN] modèle stochastique [Termes IGN] polarisation [Termes IGN] positionnement en intérieur [Termes IGN] reconstruction d'image Résumé : (auteur) This article introduces a probabilistic Bayesian model for addressing the problem of compressive multipolarization through-wall radar imaging (TWRI). The proposed approach formulates the task of wall-clutter mitigation and multipolarization image reconstruction as a Bayesian inference problem for a joint distribution between observed radar measurements and latent wall-clutter matrix and indoor target images. The joint probability distribution incorporates three prior beliefs: low-dimensional structure of the wall reflections, group sparsity structure of the target images, and joint sparsity among the polarization images. These signal attributes are modeled through hierarchical priors, whose parameters and hyperparameters are treated with a full Bayesian formulation. Furthermore, this article presents a variational Bayesian inference algorithm that estimates wall-clutter and multipolarization images as posterior distributions and optimizes the model parameters and hyperparameters simultaneously. Experimental results on simulated and real radar data show that the proposed model is very effective at removing wall clutter and enhancing target localization even when the radar measurements are significantly reduced. Numéro de notice : A2021-647 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3051955 date de publication en ligne : 26/01/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3051955 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98354 [article]

Unsupervised 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 ([10/08/2021])  

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Background segmentation in multicolored illumination environments / Nikolas Ladas in The Visual Computer, vol 37 n° 8 (August 2021)  

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Estimation of surface deformation due to Pasni earthquake using RADAR interferometry / Muhammad Ali in Geocarto international, vol 36 n° 14 ([01/08/2021])  

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Improving urban land cover classification with combined use of Sentinel-2 and Sentinel-1 imagery / Bin Hu in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)  

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Spatiotemporal analysis of urban heat island intensification in the city of Minneapolis-St. Paul and Chicago metropolitan areas using Landsat data from 1984 to 2016 / Mbongowo J. Mbuh in Geocarto international, vol 36 n° 14 ([01/08/2021])  

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Leaf and wood separation for individual trees using the intensity and density data of terrestrial laser scanners / Kai Tan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)  

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Detecting high-temperature anomalies from Sentinel-2 MSI images / Yongxue Liu in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)  

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Direct analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) of wood reveals distinct chemical signatures of two species of Afzelia / Peter Kitin in Annals of Forest Science [en ligne], vol 78 n° 2 (June 2021)  

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Evaluating 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)  

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Fractional vegetation cover estimation algorithm for FY-3B reflectance data based on random forest regression method / Duanyang Liu in Remote sensing, vol 13 n° 11 (June-1 2021)  

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