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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)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 3 (March 2020) . - 26 p. 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 descripteurs IGN] agriculture de précision [Termes descripteurs IGN] données GNSS [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] structure-from-motion [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] télédétection Résumé : (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 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 [article]

The application of bidirectional reflectance distribution function data to recognize the spatial heterogeneity of mixed pixels in vegetation remote sensing: a simulation study / Yanan Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 161 - 167 Titre : The application of bidirectional reflectance distribution function data to recognize the spatial heterogeneity of mixed pixels in vegetation remote sensing: a simulation study Type de document : Article/Communication Auteurs : Yanan Yan, Auteur ; Lei Deng, Auteur ; L. Xian-Lin, Auteur Année de publication : 2020 Article en page(s) : pp 161 - 167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] agrégation spatiale [Termes descripteurs IGN] anisotropie [Termes descripteurs IGN] approche pixel [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] dispersion [Termes descripteurs IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] hétérogénéité spatiale [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle de transfert radiatif [Termes descripteurs IGN] réflectance [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] télédétection Résumé : (auteur) Spectral decomposition of mixed pixels can provide information about the abundance of end members but fails to indicate the spatial distribution of end members in vegetation remote sensing. This work is a significant attempt to use the bidirectional reflectance distribution function (BRDF) characteristics of mixed pixels in the prediction of spatial-heterogeneity metrics. Data sets from this function with different spatial distributions were constructed by the discrete anisotropic radiative transfer model, and three spatial aggregation and dispersion metrics were calculated: percentage of like adjacencies, spatial division index, and aggregation index. A simple linear regression method was used to construct the prediction model of spatial aggregation and dispersion metrics. The potential of multiangle remote sensing model for identifying spatial patterns well was demonstrated, and its importance was found to differ for different spatial aggregation and dispersion metrics. Specifically, the precision of the model based on multiangle reflectance used for predicting the spatial division index could meet a minimum root mean square of 5.95%. The reflectance features from backward observation on the principal plane play the leading role in recognizing the spatial heterogeneity of mixed pixels. The prediction model is sufficiently robust to distinguish the same vegetation with different growth trends, but also performs well when the ground objects have a smaller reflectance difference in the mixed pixels in a certain band. This study is expected to offer a new thought for spatial-heterogeneity identification of ground objects and thus promote the development of remote sensing technology in assessing spatial distribution. Numéro de notice : A2020-146 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.3.161 date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.3.161 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94775 [article]

Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps / Alper Sen in Survey review, vol 52 n° 371 (March 2020)  

Public [article]  inSurvey review > vol 52 n° 371 (March 2020) . - pp 150 - 158 Titre : Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps Type de document : Article/Communication Auteurs : Alper Sen, Auteur ; Baris Suleymanoglu, Auteur ; Metin Soycan, Auteur Année de publication : 2020 Article en page(s) : pp 150 - 158 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] algorithme de filtrage [Termes descripteurs IGN] carte de Kohonen [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] extraction de points [Termes descripteurs IGN] filtre adaptatif [Termes descripteurs IGN] khi carré [Termes descripteurs IGN] pondération [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] zone urbaine Résumé : (auteur) The extraction of artificial and natural features using light detection and ranging (Lidar) data is a fundamental task in many fields of research for environmental science. In this study, the possibility of using self-organising maps (SOM), which is an unsupervised artificial neural network classification method to extract the bare earth surface and features from airborne Lidar data, was investigated for two different urban areas. The effect of the enlargement of the study area was analysed using the proposed approach. The appropriate weights of SOM inputs, which are 3D coordinates and intensity, obtained from a Lidar point cloud were determined by using Pearson's chi-squared independence test. The weighted SOM feature extraction performance was better than that of the unweighted SOM. The filtering results of SOM to separate ground and non-ground data were also compared with those obtained by the adaptive TIN filtering algorithm. Most of the non-ground features could be removed by the weighted SOM. Numéro de notice : A2020-079 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1532704 date de publication en ligne : 12/10/2018 En ligne : https://doi.org/10.1080/00396265.2018.1532704 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94642 [article]

Three-dimensional photogrammetric mapping of cotton bolls in situ based on point cloud segmentation and clustering / Shangpeng Sun in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 195 - 207 Titre : Three-dimensional photogrammetric mapping of cotton bolls in situ based on point cloud segmentation and clustering Type de document : Article/Communication Auteurs : Shangpeng Sun, Auteur ; Changying Li, Auteur ; Peng Wah Chee, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 195 - 207 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] cartographie 3D [Termes descripteurs IGN] classification basée sur les régions [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] gestion de production [Termes descripteurs IGN] Gossypium (genre) [Termes descripteurs IGN] phénologie [Termes descripteurs IGN] rendement agricole [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] structure-from-motion [Termes descripteurs IGN] surveillance de la végétation Résumé : (Auteur) Three-dimensional high throughput plant phenotyping techniques provide an opportunity to measure plant organ-level traits which can be highly useful to plant breeders. The number and locations of cotton bolls, which are the fruit of cotton plants and an important component of fiber yield, are arguably among the most important phenotypic traits but are complex to quantify manually. Hence, there is a need for effective and efficient cotton boll phenotyping solutions to support breeding research and monitor the crop yield leading to better production management systems. We developed a novel methodology for 3D cotton boll mapping within a plot in situ. Point clouds were reconstructed from multi-view images using the structure from motion algorithm. The method used a region-based classification algorithm that successfully accounted for noise due to sunlight. The developed density-based clustering method could estimate boll counts for this situation, in which bolls were in direct contact with other bolls. By applying the method to point clouds from 30 plots of cotton plants, boll counts, boll volume and position data were derived. The average accuracy of boll counting was up to 90% and the R2 values between fiber yield and boll number, as well as fiber yield and boll volume were 0.87 and 0.66, respectively. The 3D boll spatial distribution could also be analyzed using this method. This method, which was low-cost and provided improved site-specific data on cotton bolls, can also be applied to other plant/fruit mapping analysis after some modification. Numéro de notice : A2020-048 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.12.011 date de publication en ligne : 25/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.12.011 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94561 [article]

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Combining 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)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n° 3 (15 - 22 janvier 2020) . - pp 813 - 838 Titre : Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods Type de document : Article/Communication Auteurs : Liheng Peng, Auteur ; Kai Liu, Auteur ; Jingjing Cao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 813 - 838 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] boosting adapté [Termes descripteurs IGN] Chine, mer de [Termes descripteurs IGN] classification et arbre de régression [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] écosystème [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] île [Termes descripteurs IGN] image Gaofen [Termes descripteurs IGN] image RapidEye [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] Rotation Forest classification Résumé : (auteur) Mangrove forests are important constitutions for sustainable development of coastal ecosystems, and they are often mapped and monitored with remote sensing approaches. Satellite images allow detailed studies of the distribution and composition of mangrove forests, and therefore facilitate the management and conservation of the ecosystems. The combination of multiple types of satellite images with different spatial and spectral resolutions is helpful in mangrove forests extraction and mangrove species discrimination as it reduces sampling workload and increases classification accuracies. In this study, the 1.0-m-resolution Gaofen-2 (GF-2) and the 5.0-m-resolution RapidEye-4 (RE-4) satellite images, acquired in February 2017 and November 2016 respectively, were used with ensemble machine-learning and object-oriented methods for mangroves mapping at both the community and species levels of the Qi’ao Island, Zhuhai, China. First, the mangroves on the island were segmented from the GF-2 image on a large scale, and then they were extracted combining with their digital elevation model (DEM) data. Second, the GF-2 image was further processed on a fine scale, in which object-oriented features from both the GF-2 and RE-4 images were extracted for each mangrove species. Third, it is followed by the mangrove species classification process which involves three ensemble machine-learning methods: the adaptive boosting (AdaBoost), the random forest (RF) and the rotation forest (RoF). These three methods employed a classification and regression tree (CART) as the base classifier. The results show that the overall accuracy (OA) of mangrove area extraction on the Qi’ao Island with the auxiliary data, DEM, achieves 98.76% (Kappa coefficient (κ) = 0.9289). The features extracted by the GF-2 and RE-4 images were shown to be beneficial for mangrove species discrimination. A maximum improvement in the OA of approximately 8% and a κκ of approximately 0.10 were achieved when employing RoF (OA = 92.01%, κ = 0.9016). Ensemble-learning methods can significantly improve the classification accuracy of CART, and the use of a bagging scheme (RF and RoF) is shown as a better way to map mangrove species than adaptive boosting (AdaBoost). In addition, RoF performed well in mangrove species classification but it was not as robust as the RF, whose average OA and κκ were 80.59% and 0.7608, respectively, while the RoF’s were 77.45% and 0.7214, respectively, in the 10-fold cross-validation. Numéro de notice : A2020-212 Thématique : IMAGERIE Nature : Article DOI : 10.1080/01431161.2019.1648907 date de publication en ligne : 30/07/2019 En ligne : https://doi.org/10.1080/01431161.2019.1648907 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94897 [article]

A novel method for separating woody and herbaceous time series / Qiang Zhou in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (July 2019)  

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Polarimétrie radar complète et partielle pour le suivi des surfaces terrestres / Pierre-Louis Frison in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)

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A new waveform decomposition method for multispectral LiDAR / Shalei Song in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

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Integration of lidar data and GIS data for point cloud semantic enrichment at the point level / Harith Aljumaily in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

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DEM refinement by low vegetation removal based on the combination of full waveform data and progressive TIN densification / Hongchao Ma in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

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Détection de changement par imagerie radar sur les zones naturelles et agricoles en milieu tropical / Jérôme Lebreton (2018) 

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On the fusion of lidar and aerial color imagery to detect urban vegetation and buildings / Madhurima Bandyopadhyay in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 2 (February 2017)  

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International benchmarking of the individual tree detection methods for modeling 3-D canopy structure for silviculture and forest ecology using airborne laser scanning / Yunsheng Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)    

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Cartographie du châtaignier en Alsace par imagerie satellite multi-date / Colette Meyer in Revue Française de Photogrammétrie et de Télédétection, n° 211 - 212 (juillet - décembre 2015)

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A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data / Victor F. Strimbu in ISPRS Journal of photogrammetry and remote sensing, vol 104 (June 2015)  

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