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Assessment of winter season land surface temperature in the Himalayan regions around the Kullu area in India using Landsat-8 data / Divyesh Varade in Geocarto international, vol 35 n° 6 ([01/05/2020])
[article]
Titre : Assessment of winter season land surface temperature in the Himalayan regions around the Kullu area in India using Landsat-8 data Type de document : Article/Communication Auteurs : Divyesh Varade, Auteur ; Onkar Dikshit, Auteur Année de publication : 2020 Article en page(s) : pp 641 - 662 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] emissivité
[Termes IGN] Himalaya
[Termes IGN] hiver
[Termes IGN] image Landsat-8
[Termes IGN] image multibande
[Termes IGN] image Sentinel-3
[Termes IGN] Inde
[Termes IGN] manteau neigeux
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] précision de détermination de surface
[Termes IGN] seuillage
[Termes IGN] température au solRésumé : (auteur) In this study, we propose a modified thresholds method for the determination of land surface emissivity (LSE) for snow covered mountainous areas. The conventional Normalized Differenced Vegetation Index (NDVI) thresholds method (NDVITHM) does not discriminate the snow covered pixels with soil pixels in assigning the LSE based on NDVI thresholds. In the proposed approach, we incorporate different thresholding rules based on the Normalized Differenced Snow Index and the S3 index for incorporating separability in the LSE for the snow covered pixels. The LSE thus derived is used to determine the land surface temperature using the Single Channel Method. The approach was evaluated for a study area around the Kullu Valley in the lower Indian Himalayas for a dataset of the winter season of Landsat-8 multispectral data. The observed coefficient of determination values indicated that the proposed method yielded better results with respect to the conventional NDVITHM approach. Numéro de notice : A2020-203 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1520928 Date de publication en ligne : 26/12/2018 En ligne : https://doi.org/10.1080/10106049.2018.1520928 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94878
in Geocarto international > vol 35 n° 6 [01/05/2020] . - pp 641 - 662[article]Transferring deep learning models for cloud detection between Landsat-8 and Proba-V / Gonzalo Mateo-García in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)
[article]
Titre : Transferring deep learning models for cloud detection between Landsat-8 and Proba-V Type de document : Article/Communication Auteurs : Gonzalo Mateo-García, Auteur ; Valero Laparra, Auteur ; Dan López-Puigdollers, Auteur ; Luis Gómez-Chova, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 17 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage par transformation
[Termes IGN] apprentissage profond
[Termes IGN] conversion de données
[Termes IGN] détection des nuages
[Termes IGN] échantillonnage de données
[Termes IGN] image Landsat-8
[Termes IGN] image multibande
[Termes IGN] image PROBA
[Termes IGN] jeu de données
[Termes IGN] masque
[Termes IGN] réseau neuronal convolutif
[Termes IGN] seuillage de pointsRésumé : (Auteur) Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical properties of the acquired signals and propose a simple transfer learning approach using Landsat-8 and Proba-V sensors, whose images have different but similar spatial and spectral characteristics. Three types of experiments are conducted to demonstrate that transfer learning can work in both directions: (a) from Landsat-8 to Proba-V, where we show that models trained only with Landsat-8 data produce cloud masks 5 points more accurate than the current operational Proba-V cloud masking method, (b) from Proba-V to Landsat-8, where models that use only Proba-V data for training have an accuracy similar to the operational FMask in the publicly available Biome dataset (87.79–89.77% vs 88.48%), and (c) jointly from Proba-V and Landsat-8 to Proba-V, where we demonstrate that using jointly both data sources the accuracy increases 1–10 points when few Proba-V labeled images are available. These results highlight that, taking advantage of existing publicly available cloud masking labeled datasets, we can create accurate deep learning based cloud detection models for new satellites, but without the burden of collecting and labeling a large dataset of images. Numéro de notice : A2020-043 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.024 Date de publication en ligne : 10/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.024 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94522
in ISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 1 - 17[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020021 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020023 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020022 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Application of digital image processing in automated analysis of insect leaf mines / Yee Man Theodora Cho (2020)
Titre : Application of digital image processing in automated analysis of insect leaf mines Type de document : Thèse/HDR Auteurs : Yee Man Theodora Cho, Auteur Editeur : York [Royaume-Uni] : University of York Année de publication : 2020 Importance : 202 p. Format : 21 x 30 cm Note générale : bibliographie
PhD thesis, Electronic Engineering, University of York, United KingdomLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Acer (genre)
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] Castanea (genre)
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] détection de contours
[Termes IGN] diagnostic foliaire
[Termes IGN] image hyperspectrale
[Termes IGN] insecte nuisible
[Termes IGN] modèle de simulation
[Termes IGN] segmentation d'image
[Termes IGN] seuillage
[Termes IGN] surveillance de la végétation
[Termes IGN] taxinomie
[Termes IGN] traitement d'imageRésumé : (auteur) Automated species identificationhas become a popular alternative to manual classification in the past few decades, as a result of advancement in digital image processing techniques and machine learning algorithms. This project aims to devise a new approach for the detection of leaf mines and fungal spots from digital images, and to investigate the possibility of monitoring the growth of leaf mines. Leaf-mining insects primarily belong to the orders of moths (Lepidoptera), flies (Diptera) and beetles (Coleoptera); or the suborders of sawflies (Symphyta) and wasps (Apocrita). Every spring and summer the larvae of leaf-mining insects feed on leaf tissues until maturity and vacate the mines as adults. As most species of leaf miners attack garden plants or crops, they are generally regarded as pests, despiterarely causing severe long-term detrimental effect on their host plants. Increase in human activities has led to the spread of these invasive species globally in recent years, and the demand for an effective classification system to monitor their distribution is rising consistently. Samples from three species of leaf-mining insects were included in this project: horse chestnut leaf miner (Cameraria ohridella), apple leaf miner (Lyonetia clerkella), and holly leaf miner (Phytomyza ilicis). Leaves with tar spots (Rhytisma acerinum)were also introduced as variations.The proposed method uses image processing techniques such as thresholding, conversion between colour spaces, edge detection, image segmentation,and morphological operations. This project also explores the use of machine learning algorithmsas analytical monitoring and predictive tools, using the growth of C. ohridellaleaf mines as an example. Note de contenu : 1- Introduction
2- Background
3- Digital image processing
4- Automated classification
5- Implementation
6- Data analysis
7- ConclusionNuméro de notice : 28552 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : PhD thesis : Electronic Engineering : University of York : 2020 En ligne : https://etheses.whiterose.ac.uk/27749/1/Cho_105036528_Thesis.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97414 Novel adaptive histogram trend similarity approach for land cover change detection by using bitemporal very-high-resolution remote sensing images / Zhi Yong Lv in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)
[article]
Titre : Novel adaptive histogram trend similarity approach for land cover change detection by using bitemporal very-high-resolution remote sensing images Type de document : Article/Communication Auteurs : Zhi Yong Lv, Auteur ; Tong Fei Liu, Auteur ; Zhang Penglin, Auteur ; Jon Atli Benediktsson, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 9554 - 9574 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] changement d'occupation du sol
[Termes IGN] Chine
[Termes IGN] classification pixellaire
[Termes IGN] détection de changement
[Termes IGN] histogramme
[Termes IGN] Hong-Kong
[Termes IGN] image à très haute résolution
[Termes IGN] phénologie
[Termes IGN] seuillage de pointsRésumé : (auteur) Detecting land cover change through very-high-resolution (VHR) remote sensing images is helpful in supporting urban sustainable development, natural disaster evaluation, and environmental assessment. However, the intraclass spectral variance in VHR remote sensing images is usually larger than that of median-low remote sensing images. Furthermore, the bitemporal images are usually acquired under different atmospheric conditions, sun height, soil moisture, and other factors. Consequently, in practical applications, many pseudo changes are presented in the detected map. In this paper, an adaptive histogram trend (AHT) similarity approach is promoted to quantitatively measure the magnitude between the corresponding pixels in bitemporal images in terms of change semantic. In the proposed approach, to reduce the phenological effect on the bitemporal images of land cover change detection (LCCD), we first define the quantitative description of AHT. Second, the change magnitudes between pairwise pixels are quantitatively measured by an improved bin-to-bin (B2B) distance between the corresponding AHTs. Then, the change magnitudes between two entire bitemporal images are measured AHT-by-AHT. Finally, binary threshold methods, such as the Otsu method or the double-window flexible pace search (DFPS) method, are used to divide the change magnitude image into binary change detection maps and obtain the final change detection map. The performance of the AHT-based LCCD approach is verified by four pairs of VHR remote-sensing images that correspond to two types of real land cover change cases. The detected results based on the four pairs of bitemporal VHR images outperformed the compared state-of-the-art LCCD methods. Numéro de notice : A2019-599 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2927659 Date de publication en ligne : 01/08/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2927659 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94593
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 12 (December 2019) . - pp 9554 - 9574[article]Tree size thresholds produce biased estimates of forest biomass dynamics / Eric B. Searle in Forest ecology and management, vol 400 (15 September 2017)
[article]
Titre : Tree size thresholds produce biased estimates of forest biomass dynamics Type de document : Article/Communication Auteurs : Eric B. Searle, Auteur ; Han Y.H. Chen, Auteur Année de publication : 2017 Article en page(s) : pp 468 - 474 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne
[Termes IGN] changement climatique
[Termes IGN] diamètre des arbres
[Termes IGN] échantillonnage
[Termes IGN] erreur systématique
[Termes IGN] estimation statistique
[Termes IGN] forêt boréale
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Manitoba (Canada)
[Termes IGN] placette d'échantillonnage
[Termes IGN] seuillage
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Studies that examine forest biomass dynamics often rely on long-term, spatially extensive, repeatedly measured permanent sample plots. Due to the intensive cost of sampling all trees within these plots, an arbitrary size threshold is typically imposed, which leads to only larger trees being sampled. However, it remains unclear whether the sampling of only large trees is representative of the entirety of stands of diverse sizes; the sampling of only large trees may produce biased estimates of biomass dynamics (growth, ingrowth, and mortality). Using a network of 141 permanent sample plots from Manitoba, Canada, with all trees of >1.3 m in height repeatedly measured, we constructed three distinct data sets, with 10 cm, 5 cm, and no diameter at breast height threshold, to illustrate that total productivity and mortality are increasingly underestimated with increasingly larger diameter at breast height thresholds. This effect is particularly significant in young stands, where productivity estimates peak at least 20 years earlier than the determined estimates under large thresholds. We highlight the need to account for smaller trees in long-term observational studies to ensure unbiased estimates of stand level aboveground biomass productivity and loss. Numéro de notice : A2017-807 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1016/j.foreco.2017.06.042 En ligne : https://doi.org/10.1016/j.foreco.2017.06.042 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89245
in Forest ecology and management > vol 400 (15 September 2017) . - pp 468 - 474[article]The differentiation of point symbols using selected visual variables in the mobile augmented reality system / Łukasz Halik in Cartographic journal (the), Vol 54 n° 2 (May 2017)PermalinkMise en place d’un processus de dessin automatisé de plans d’intérieurs à partir de nuages de points acquis par LIDAR / Léa Talec (2017)PermalinkPrivacy and spatial pattern preservation in masked GPS trajectory data / Dara E. Seidl in International journal of geographical information science IJGIS, vol 30 n° 3-4 (March - April 2016)PermalinkPermalinkAnalytical estimation of map readability / Lars Harrie in ISPRS International journal of geo-information, vol 4 n°2 (June 2015)PermalinkCartographie des végétations herbacées des marais littoraux à partir de données topographiques LiDAR / Sébastien Rapinel in Revue Française de Photogrammétrie et de Télédétection, n° 210 (Avril 2015)PermalinkUsing mobile laser scanning data for automated extraction of road markings / Haiyan Guan in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkA method to generalize stream flowlines in small-scale maps by a variable flow-based pruning threshold / Michael Tinker in Cartography and Geographic Information Science, vol 40 n° 5 (November 2013)PermalinkA multiresolution hierarchical classification algorithm for filtering airborne LiDAR data / Chuanfa Chen in ISPRS Journal of photogrammetry and remote sensing, vol 82 (August 2013)PermalinkSupport vector machine for spatial variation / C. Andris in Transactions in GIS, vol 17 n° 1 (February 2013)Permalink