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A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection / Xi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 174 (April 2021) . - pp 87 - 104 Titre : A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection Type de document : Article/Communication Auteurs : Xi Wu, Auteur ; Zhenwei Shi, Auteur ; Zhengxia Zou, Auteur Année de publication : 2021 Article en page(s) : pp 87 - 104 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] altitude [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection des nuages [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion de données [Termes IGN] image Gaofen [Termes IGN] information géographique [Termes IGN] latitude [Termes IGN] longitude [Termes IGN] modèle statistique [Termes IGN] neige [Termes IGN] Normalized Difference Snow Index Résumé : (auteur) Geographic information such as the altitude, latitude, and longitude are common but fundamental meta-records in remote sensing image products. In this paper, it is shown that such a group of records provides important priors for cloud and snow detection in remote sensing imagery. The intuition comes from some common geographical knowledge, where many of them are important but are often overlooked. For example, it is generally known that snow is less likely to exist in low-latitude or low-altitude areas, and clouds in different geographic may have various visual appearances. Previous cloud and snow detection methods simply ignore the use of such information, and perform detection solely based on the image data (band reflectance). Due to the neglect of such priors, most of these methods are difficult to obtain satisfactory performance in complex scenarios (e.g., cloud-snow coexistence). In this paper, a novel neural network called “Geographic Information-driven Network (GeoInfoNet)” is proposed for cloud and snow detection. In addition to the use of the image data, the model integrates the geographic information at both training and detection phases. A “geographic information encoder” is specially designed, which encodes the altitude, latitude, and longitude of imagery to a set of auxiliary maps and then feeds them to the detection network. The proposed network can be trained in an end-to-end fashion with dense robust features extracted and fused. A new dataset called “Levir_CS” for cloud and snow detection is built, which contains 4,168 Gaofen-1 satellite images and corresponding geographical records, and is over 20× larger than other datasets in this field. On “Levir_CS”, experiments show that the method achieves 90.74% intersection over union of cloud and 78.26% intersection over union of snow. It outperforms other state of the art cloud and snow detection methods with a large margin. Feature visualizations also show that the method learns some important priors which is close to the common sense. The proposed dataset and the code of GeoInfoNet are available in https://github.com/permanentCH5/GeoInfoNet. Numéro de notice : A2021-209 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.023 Date de publication en ligne : 22/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.023 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97187 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2021041	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2021043	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2021042	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Use of ground penetrating radar in the evaluation of wood structures: A review / Brunela Pollastrelli Rodrigues in Forests, vol 12 n° 4 (April 2021)  

Public [article]  inForests > vol 12 n° 4 (April 2021) . - n° 492 Titre : Use of ground penetrating radar in the evaluation of wood structures: A review Type de document : Article/Communication Auteurs : Brunela Pollastrelli Rodrigues, Auteur ; Christopher Senalik, Auteur ; Xi Wu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 492 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bois [Termes IGN] détection [Termes IGN] humidité du sol [Termes IGN] propriété diélectrique [Termes IGN] qualité du bois [Termes IGN] radar pénétrant GPR Résumé : (auteur) This paper is a review of published studies involving the use of ground penetrating radar (GPR) on wood structures. It also contains background information to help the reader understand how GPR functions. The use of GPR on wood structures began to grow in popularity at the turn of the millennium. GPR has many characteristics that make it attractive as an inspection tool for wood: it is faster than many acoustic and stress wave techniques; it does not require the use of a couplant; while it can also detect the presence of moisture. Moisture detection is of prime concern, and several researchers have labored to measure internal moisture using GPR. While there have been several laboratory studies involving the use of GPR on wood, its use as an inspection tool on large wood structures has been limited. This review identified knowledge gaps that need to be addressed to improve the efficacy of GPR as a reliable inspection tool of wood structure. Chief among these gaps, is the ability to distinguish the type of internal feature from the GPR output and the ability to identify internal decay. Numéro de notice : A2021-349 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f12040492 Date de publication en ligne : 16/04/2021 En ligne : https://doi.org/10.3390/f12040492 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97596 [article]