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Integration of remote sensing and GIS to extract plantation rows from a drone-based image point cloud digital surface model / Nadeem Fareed in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)
[article]
Titre : Integration of remote sensing and GIS to extract plantation rows from a drone-based image point cloud digital surface model Type de document : Article/Communication Auteurs : Nadeem Fareed, Auteur ; Khushbakht Rehman, Auteur Année de publication : 2020 Article en page(s) : 26 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] agriculture de précision
[Termes IGN] données GNSS
[Termes IGN] données lidar
[Termes IGN] extraction automatique
[Termes IGN] extraction de la végétation
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à très haute résolution
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] modèle dynamique
[Termes IGN] modèle numérique de surface
[Termes IGN] semis de points
[Termes IGN] structure-from-motion
[Termes IGN] système d'information géographique
[Termes IGN] télédétectionRésumé : (auteur) Automated feature extraction from drone-based image point clouds (DIPC) is of paramount importance in precision agriculture (PA). PA is blessed with mechanized row seedlings to attain maximum yield and best management practices. Therefore, automated plantation rows extraction is essential in crop harvesting, pest management, and plant grow-rate predictions. Most of the existing research is consists on red, green, and blue (RGB) image-based solutions to extract plantation rows with the minimal background noise of test study sites. DIPC-based DSM row extraction solutions have not been tested frequently. In this research work, an automated method is designed to extract plantation row from DIPC-based DSM. The chosen plantation compartments have three different levels of background noise in UAVs images, therefore, methodology was tested under different background noises. The extraction results were quantified in terms of completeness, correctness, quality, and F1-score values. The case study revealed the potential of DIPC-based solution to extraction the plantation rows with an F1-score value of 0.94 for a plantation compartment with minimal background noises, 0.91 value for a highly noised compartment, and 0.85 for a compartment where DIPC was compromised. The evaluation suggests that DSM-based solutions are robust as compared to RGB image-based solutions to extract plantation-rows. Additionally, DSM-based solutions can be further extended to assess the plantation rows surface deformation caused by humans and machines and state-of-the-art is redefined. Numéro de notice : A2020-260 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9030151 Date de publication en ligne : 06/03/2020 En ligne : https://doi.org/10.3390/ijgi9030151 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95020
in ISPRS International journal of geo-information > vol 9 n° 3 (March 2020) . - 26 p.[article]Large-scale two-phase estimation of wood production by poplar plantations exploiting Sentinel-2 data as auxiliary information / Agnese Marcelli in Silva fennica, vol 54 n° 2 (March 2020)
[article]
Titre : Large-scale two-phase estimation of wood production by poplar plantations exploiting Sentinel-2 data as auxiliary information Type de document : Article/Communication Auteurs : Agnese Marcelli, Auteur ; Walter Mattioli, Auteur ; Nicola Puletti, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] acteurs de la filière bois-forêt
[Termes IGN] bois sur pied
[Termes IGN] échantillonnage
[Termes IGN] image à haute résolution
[Termes IGN] image Sentinel-MSI
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Italie
[Termes IGN] Populus (genre)
[Termes IGN] récolte de bois
[Termes IGN] régression linéaire
[Termes IGN] tessellation
[Termes IGN] volume en boisRésumé : (auteur) Growing demand for wood products, combined with efforts to conserve natural forests, have supported a steady increase in the global extent of planted forests. Here, a two-phase sampling strategy for large-scale assessment of the total area and the total wood volume of fast-growing forest tree crops within agricultural land is presented. The first phase is performed using tessellation stratified sampling on high-resolution remotely sensed imagery and is sufficient for estimating the total area of plantations by means of a Monte Carlo integration estimator. The second phase is performed using stratified sampling of the plantations selected in the first phase and is aimed at estimating total wood volume by means of an approximation of the first-phase Horvitz-Thompson estimator. Vegetation indices from Sentinel-2 are exploited as freely available auxiliary information in a linear regression estimator to improve the design-based precision of the estimator based on the sole sample data. Estimators of the totals and of the design-based variances of total estimators are presented. A simulation study is developed in order to check the design-based performance of the two alternative estimators under several artificial distributions supposed for poplar plantations (random, clustered, spatially trended). An application in Northern Italy is also reported. The regression estimator turns out to be invariably better than that based on the sole sample information. Possible integrations of the proposed sampling scheme with conventional national forest inventories adopting tessellation stratified sampling in the first phase are discussed. Numéro de notice : A2020-323 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10247 Date de publication en ligne : 20/03/2020 En ligne : https://doi.org/10.14214/sf.10247 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95197
in Silva fennica > vol 54 n° 2 (March 2020)[article]Reducing shadow effects on the co-registration of aerial image pairs / Matthew Plummer in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)
[article]
Titre : Reducing shadow effects on the co-registration of aerial image pairs Type de document : Article/Communication Auteurs : Matthew Plummer, Auteur ; Douglas A. Stow, Auteur ; Emmanuel Storey, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 177 - 186 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de données
[Termes IGN] correction des ombres
[Termes IGN] détection automatique
[Termes IGN] détection de changement
[Termes IGN] effet d'ombre
[Termes IGN] enregistrement de données
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] image multitemporelle
[Termes IGN] intensité lumineuse
[Termes IGN] masque
[Termes IGN] Ransac (algorithme)
[Termes IGN] SIFT (algorithme)Résumé : (auteur) Image registration is an important preprocessing step prior to detecting changes using multi-temporal image data, which is increasingly accomplished using automated methods. In high spatial resolution imagery, shadows represent a major source of illumination variation, which can reduce the performance of automated registration routines. This study evaluates the statistical relationship between shadow presence and image registration accuracy, and whether masking and normalizing shadows leads to improved automatic registration results. Eighty-eight bitemporal aerial image pairs were co-registered using software called Scale Invariant Features Transform (SIFT) and Random Sample Consensus (RANSAC) Alignment (SARA). Co-registration accuracy was assessed at different levels of shadow coverage and shadow movement within the images. The primary outcomes of this study are (1) the amount of shadow in a multi-temporal image pair is correlated with the accuracy/success of automatic co-registration; (2) masking out shadows prior to match point select does not improve the success of image-to-image co-registration; and (3) normalizing or brightening shadows can help match point routines find more match points and therefore improve performance of automatic co-registration. Normalizing shadows via a standard linear correction provided the most reliable co-registration results in image pairs containing substantial amounts of relative shadow movement, but had minimal effect for pairs with stationary shadows. Numéro de notice : A2020-147 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.4.177 Date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.4.177 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94776
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 177 - 186[article]Sea-land segmentation using deep learning techniques for Landsat-8 OLI imagery / Ting Yang in Marine geodesy, Vol 43 n° 2 (March 2020)
[article]
Titre : Sea-land segmentation using deep learning techniques for Landsat-8 OLI imagery Type de document : Article/Communication Auteurs : Ting Yang, Auteur ; Zhonghua Hong, Auteur ; Yun Zhang, Auteur Année de publication : 2020 Article en page(s) : pp 105 - 133 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Landsat-OLI
[Termes IGN] littoral
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantique
[Termes IGN] trait de côteRésumé : (auteur) Automated coastline extraction from optical satellites is fundamental to coastal mapping, and sea-land segmentation is the core technology of coastline extraction. Deep convolutional neural networks (DCNNs) have performed well in semantic segmentation in recent years. However, sea-land segmentation using deep learning techniques remains a challenging task, due to the lack of a benchmark dataset and the difficulty of deciding which semantic segmentation model to use. We present a comparative framework of sea-land segmentation to Landsat-8 OLI imagery via semantic segmentation in deep learning techniques. Three issues are investigated: (1) constructing a sea-land benchmark dataset using Landsat-8 Operational Land Imager (OLI) imagery consisting of 18,000 km2 of coastline around China; (2) evaluating the feasibility and performance of sea-land segmentation by comparing the accuracy assessment, time complexity, spatial complexity and stability of state-of-the-art DCNNs methods; (3) choosing the most suitable semantic segmentation model for sea-land segmentation in accordance with Akaike information criterion (AIC) and Bayesian information criterion (BIC) model selection. Results show that the average test accuracy achieves over 99% accuracy, and the mean Intersection over Unions (mean IoU) is above 92%. These findings demonstrate that the Fully Convolutional DenseNet (FC-enseNet) performs better than other state-of-the-art methods in sea-land segmentation, based on both AIC and BIC. Considering training time efficiency, DeeplabV3+ performs better for sea-land segmentation. The sea-land segmentation benchmark dataset is available at: https://pan.baidu.com/s/1BlnHiltOLbLKe4TG8lZ5xg. Numéro de notice : A2020-220 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01490419.2020.1713266 Date de publication en ligne : 20/01/2020 En ligne : https://doi.org/10.1080/01490419.2020.1713266 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94917
in Marine geodesy > Vol 43 n° 2 (March 2020) . - pp 105 - 133[article]Spectral–spatial–temporal MAP-based sub-pixel mapping for land-cover change detection / Da He in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)
[article]
Titre : Spectral–spatial–temporal MAP-based sub-pixel mapping for land-cover change detection Type de document : Article/Communication Auteurs : Da He, Auteur ; Yanfei Zhong, Auteur ; Liangpei Zhang, Auteur Année de publication : 2020 Article en page(s) : pp 1696 - 1717 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] changement d'occupation du sol
[Termes IGN] classification du maximum a posteriori
[Termes IGN] détection de changement
[Termes IGN] distribution spatiale
[Termes IGN] données spatiotemporelles
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-TM
[Termes IGN] image multibande
[Termes IGN] image Quickbird
[Termes IGN] image Terra-MODIS
[Termes IGN] modèle dynamique
[Termes IGN] optimisation spatiale
[Termes IGN] précision infrapixellaire
[Termes IGN] série temporelle
[Termes IGN] urbanisation
[Termes IGN] Wuhan (Chine)
[Termes IGN] zone urbaineRésumé : (Auteur) The maximum a posteriori (MAP) estimation model-based sub-pixel mapping (SPM) method is an alternative way to solve the ill-posed SPM problem. The MAP estimation model has been proven to be an effective SPM approach and has been extensively developed over the past few years, as a result of its effective regularization capability that comes from the spatial regularization model. However, various spatial regularization models do not always truly reflect the detailed spatial distribution in a real situation, and the over-smoothing effect of the spatial regularization model always tends to efface the detailed structural information. In this article, under the scenario of time-series observation by remote sensing imagery, the joint spectral–spatial–temporal MAP-based (SST_MAP) model for SPM is proposed. In SST_MAP, a newly developed temporal regularization model is added to the MAP model, based on the prerequisite for a temporally close fine image covering the same study region. This available fine image can provide the specific spatial structures most closely conforming to the ground truth for a more precise constraint, thereby reducing the over-smoothing effect. Furthermore, the three dimensions are mutually balanced and mutually constrained, to reach an equilibrium point and achieve restoration of both smooth areas for the homogeneous land-cover classes and a detailed structure for the heterogeneous land-cover classes. Four experiments were designed to validate the proposed SST_MAP: three synthetic-image experiments and one real-image experiment. The restoration results confirm the superiority of the proposed SST_MAP model. Notably, under the background of time-series observation, SST_MAP provides an alternative way of land-cover change detection (LCCD), achieving both detailed spatial-scale and high-frequency temporal LCCD observation for the study case of urbanization analysis within the city of Wuhan in China. Numéro de notice : A2020-088 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947708 Date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947708 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94662
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1696 - 1717[article]Thermal unmixing based downscaling for fine resolution diurnal land surface temperature analysis / Jiong Wang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)PermalinkCloud detection by luminance and inter-band parallax analysis for pushbroom satellite imagers / Tristan Dagobert in IPOL Journal, Image Processing On Line, vol 10 (2020)PermalinkComplex deformation at shallow depth during the 30 October 2016 Mw6.5 Norcia earthquake: interferencebetween tectonic and gravity processes? / Arthur Delorme in Tectonics, vol 39 n° 2 (February 2020)PermalinkComputer vision-based framework for extracting tectonic lineaments from optical remote sensing data / Ehsan Farahbakhsh in International Journal of Remote Sensing IJRS, vol 41 n°5 (01 - 08 février 2020)PermalinkEstimating wheat yields in Australia using climate records, satellite image time series and machine learning methods / Elisa Kamir in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)PermalinkLandslide displacement mapping based on ALOS-2/PALSAR-2 data using image correlation techniques and SAR interferometry: application to the Hell-Bourg landslide (Salazie Circle, La Réunion Island) / Daniel Raucoules in Geocarto international, vol 35 n° 2 ([01/02/2020])PermalinkLandslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya / Vijendra Kumar Pandey in Geocarto international, vol 35 n° 2 ([01/02/2020])PermalinkA LiDAR–optical data fusion approach for identifying and measuring small stream impoundments and dams / Benjamin Swan in Transactions in GIS, Vol 24 n° 1 (February 2020)PermalinkMapping precipitable water vapor time series from Sentinel-1 interferometric SAR / Pedro Mateus in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkMODIS-based land surface temperature for climate variability and change research: the tale of a typical semi-arid to arid environment / Salahuddin M. Jaber in European journal of remote sensing, vol 53 n° 1 (2020)PermalinkMulti-Spatial Resolution Satellite and sUAS Imagery for Precision Agriculture on Smallholder Farms in Malawi / Brad G. Peter in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 2 (February 2020)PermalinkA novel fire index-based burned area change detection approach using Landsat-8 OLI data / Sicong Liu in European journal of remote sensing, vol 53 n° 1 (2020)PermalinkPrediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series / Mathieu Fauvel in Remote sensing of environment, Vol 237 (February 2020)PermalinkRed-edge band vegetation indices for leaf area index estimation from Sentinel-2/MSI imagery / Yuanheng Sun in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkSome thoughts on measuring earthquake deformation using optical imagery / Min Huang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkStatistical assessment of cartographic product from photogrammetry and fixed-wing UAV acquisition / Ademir Marques Junior in European journal of remote sensing, vol 53 n° 1 (2020)PermalinkThe "Incense Road" from Petra to Gaza: an analysis using GIS and Cost functions / Motti Zohar in International journal of geographical information science IJGIS, vol 34 n° 2 (February 2020)PermalinkThe potentiality of Sentinel-2 to assess the effect of fire events on Mediterranean mountain vegetation / Walter de Simone in Plant sociology, vol 57 n° 1 ([01/02/2020])PermalinkTransferring 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)PermalinkUsing Ranked Probability Skill Score (RPSS) as Nonlocal Root-Mean-Square Errors (RMSEs) for Mitigating Wet Bias of Soil Moisture Ocean Salinity (SMOS) Soil Moisture / Ju Hyoung Lee in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 2 (February 2020)PermalinkCombining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods / Liheng Peng in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)PermalinkExtracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model / Xiaoping Wang in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)PermalinkArtificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey / Alkan Günlü in Geocarto international, Vol 35 n° 1 ([02/01/2020])Permalink3D iterative spatiotemporal filtering for classification of multitemporal satellite data sets / Hessah Albanwan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 1 (January 2020)PermalinkPermalinkAnimation d’images avant-après pour communiquer les transformations géographiques [diaporama] / María-Jesús Lobo (2020)PermalinkApplication of geographic Information system and remote sensing in multiple criteria analysis to identify priority areas for biodiversity conservation in Vietnam / Xuan Dinh Vu (2020)PermalinkPermalinkPermalinkArctic sea ice thickness retrievals from CryoSat-2: seasonal and interannual comparisons of three different products / Mengmeng Li in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)PermalinkAutomatic scale estimation of structure from motion based 3D models using laser scalers in underwater scenarios / Klemen Istenič in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)PermalinkCartographie des essences forestières à partir de séries temporelles d’images satellitaires à hautes résolutions : stabilité des prédictions, autocorrélation spatiale et cohérence avec la phénologie observée in situ / Nicolas Karasiak (2020)PermalinkClassification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery / H. Tombul in Journal of geodetic science, vol 10 n° 1 (January 2020)PermalinkClassification of time series of Sentinel-2 images for large scale mapping in Cameroon / Hermann Tagne (2020)PermalinkCombination of linear regression lines to understand the response of Sentinel-1 dual polarization SAR data with crop phenology - case study in Miyazaki, Japan / Emal Wali in Remote sensing, vol 12 n° 1 (January 2020)PermalinkComparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)PermalinkContext-aware convolutional neural network for object detection in VHR remote sensing imagery / Yiping Gong in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)PermalinkPermalinkPermalinkDeep learning for remote sensing images with open source software / Rémi Cresson (2020)PermalinkPermalinkDétermination conjointe des inondations et du type d’eau au moyen de l’imagerie multi-spectrale / Sabrine Amzil (2020)PermalinkDistribution spatiale et dynamique de la population de palmiers rôniers, Borassus aethiopum Mart., par approche de la télédétection et du Système d’Information Géographique (SIG) de la réserve de Lamto (Centre de la Côte d’Ivoire) / Kouakou Guy-Casimir Douffi (2020)PermalinkEstimation et suivi de la ressource en bois en France métropolitaine par valorisation des séries multi-temporelles à haute résolution spatiale d'images optiques (Sentinel-2) et radar (Sentinel-1, ALOS-PALSAR) / David Morin (2020)PermalinkPermalink