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Termes IGN > sciences naturelles > physique > traitement d'image > analyse d'image numérique > extraction de traits caractéristiques
extraction de traits caractéristiquesSynonyme(s)extraction des caractéristiques extraction de primitiveVoir aussi |
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Assessing the shape accuracy of coarse resolution burned area identifications / Michael L. Humber in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)
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Titre : Assessing the shape accuracy of coarse resolution burned area identifications Type de document : Article/Communication Auteurs : Michael L. Humber, Auteur ; Luigi Boschetti, Auteur ; Louis Giglio, Auteur Année de publication : 2020 Article en page(s) : pp 1516 - 1526 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aménagement paysager
[Termes IGN] appariement de formes
[Termes IGN] chevauchement
[Termes IGN] classification pixellaire
[Termes IGN] écologie
[Termes IGN] estimation de précision
[Termes IGN] Etats-Unis
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Terra-MODIS
[Termes IGN] incendie de forêt
[Termes IGN] précision cartographique
[Termes IGN] surveillance forestière
[Termes IGN] zone sinistréeRésumé : (Auteur) Accuracy assessment of burned area maps has been traditionally performed using pixel-based metrics, with the objective of assessing the accuracy and precision of burned area estimates at local and regional scales. While these assessments are helpful for obtaining consistent estimates of the burned area across many fires and over large areas, pixel-based approaches do not necessarily characterize how well individual fires are mapped. At the individual fire scale, other factors like the shape of the fire have significance regarding ecology, fire succession, and landscape management and determining other fire properties such as the spread rate. We propose a method for evaluating wildfire classification maps, which retains the spatially explicit properties of the burn scar. Our method quantifies the edge error (EE) of burned area classifications and reference maps by calculating the average geometric normal of the evaluated burned area boundary along the burn edge and the two nearest neighbor samples from the reference burn boundary. The metric is a physically meaningful quantification of the EE, which represents the average distance between the boundaries of the reference and evaluated burn scars. The methods are demonstrated by comparing MODIS Burned Area (MCD64A1) maps to Monitoring Trends in Burn Severity (MTBS) maps for 173 total wildfires in the United States. The results indicate that when accounting for the minimum achievable EE (MAEE) due to differing spatial resolutions, the mean EE is less than two MODIS pixels and the magnitude of the errors does not appear to be related to fire size. Numéro de notice : A2020-085 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2943901 Date de publication en ligne : 13/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2943901 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94659
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1516 - 1526[article]A discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data / Qingwang Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)
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Titre : A discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data Type de document : Article/Communication Auteurs : Qingwang Wang, Auteur ; Yanfeng Gu, Auteur Année de publication : 2020 Article en page(s) : pp 1568 -1586 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Amérique du nord
[Termes IGN] analyse discriminante
[Termes IGN] calcul tensoriel
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification multibande
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] état de l'art
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image multibande
[Termes IGN] modèle géométrique
[Termes IGN] semis de points
[Termes IGN] tenseur
[Termes IGN] vectorisation
[Termes IGN] voisinage (relation topologique)Résumé : (Auteur) Multispectral light detection and ranging (MS-LiDAR) systems open the door to the possibility in the 3-D land cover classification at a finer scale using only point cloud data. This article proposes a model based on the tensor representation for multispectral point cloud classification. The proposed method combines the 3-D local spatial structure of each multispectral point by characterizing the point with a second-order tensor. The first mode of the tensor indicates the spatial location and spectral information of each point (i.e., the row of the second-order tensor) and the second mode denotes the neighborhood geometric and spectral structures (i.e., the column of the second-order tensor). Then we develop a novel tensor manifold discriminant embedding (TMDE) algorithm to extract the geometric–spectral features for multispectral point clouds classification. TMDE solves the mapping matrices of each mode by preserving the intraclass samples’ distribution further making it more compact and maximizing the distance of different classes. Finally, the support vector machine classifier with the extracted features as input is used to implement the classification of multispectral point clouds. Experiments are conducted on two real multispectral point cloud data sets. The experimental results demonstrate that the proposed method can achieve significant improvements in classification accuracies in comparison with several state-of-the-art algorithms. Numéro de notice : A2020-086 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947081 Date de publication en ligne : 30/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947081 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94660
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1568 -1586[article]Edge-reinforced convolutional neural network for road detection in very-high-resolution remote sensing imagery / Xiaoyan Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)
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Titre : Edge-reinforced convolutional neural network for road detection in very-high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Xiaoyan Lu, Auteur ; Yanfei Zhong, Auteur ; Zhuo Zheng, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 153 - 160 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] accentuation de contours
[Termes IGN] analyse multiéchelle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] filtrage du bruit
[Termes IGN] image à très haute résolution
[Termes IGN] ombre
[Termes IGN] segmentation d'imageRésumé : (auteur) Road detection in very-high-resolution remote sensing imagery is a hot research topic. However, the high resolution results in highly complex data distributions, which lead to much noise for road detection—for example, shadows and occlusions caused by disturbance on the roadside make it difficult to accurately recognize road. In this article, a novel edge-reinforced convolutional neural network, combined with multiscale feature extraction and edge reinforcement, is proposed to alleviate this problem. First, multiscale feature extraction is used in the center part of the proposed network to extract multiscale context information. Then edge reinforcement, applying a simplified U-Net to learn additional edge information, is used to restore the road information. The two operations can be used with different convolutional neural networks. Finally, two public road data sets are adopted to verify the effectiveness of the proposed approach, with experimental results demonstrating its superiority. Numéro de notice : A2020-145 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.3.153 Date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.3.153 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94774
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 153 - 160[article]Integrated edge detection and terrain analysis for agricultural terrace delineation from remote sensing images / Wen Dai in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)
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Titre : Integrated edge detection and terrain analysis for agricultural terrace delineation from remote sensing images Type de document : Article/Communication Auteurs : Wen Dai, Auteur ; Jiaming Na, Auteur ; Nan Huang, Auteur Année de publication : 2020 Article en page(s) : pp 484 - 503 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse des risques
[Termes IGN] cartographie automatique
[Termes IGN] Chine
[Termes IGN] délimitation
[Termes IGN] détection de contours
[Termes IGN] effet d'ombre
[Termes IGN] érosion
[Termes IGN] Google Earth
[Termes IGN] humidité du sol
[Termes IGN] image satellite
[Termes IGN] image Worldview
[Termes IGN] méthode robuste
[Termes IGN] MNS ASTER
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] production agricole
[Termes IGN] superposition d'images
[Termes IGN] terrasseRésumé : (auteur) Agricultural terraces are important for agricultural production and soil-and-water conservation. They comprise treads and risers that require manual construction and maintenance. If managed improperly, risers will collapse, causing soil loss, gully erosion, and cultivation threats. However, mapping terrace risers remains a challenge. This study presents a novel approach to automatically map terrace risers by combining remote sensing images and digital elevation models (DEMs). First, a terraced hillslope was extracted via a hill-shading method and edges in the image were detected using a Canny edge detector. Next, the DEM was used to generate the contour direction, and edges along this direction were searched and coded as candidate terrace risers via directional detection. Finally, the results of directional detection and the edge image obtained from the Canny detector were overlaid to backtrack complete terrace risers. The approach was validated using four study areas with different topographic characteristics in the Loess Plateau, China. The results verify that the approach achieves outstanding performance and robustness in mapping terrace risers. The precision, recall, and F-measure were 90.81%–97.57%, 88.53%–94.10%, and 90.13%–95.80%, respectively. This approach is flexible and applicable with freely available images and DEM sources. Numéro de notice : A2020-105 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1650363 Date de publication en ligne : 22/08/2019 En ligne : https://doi.org/10.1080/13658816.2019.1650363 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94701
in International journal of geographical information science IJGIS > vol 34 n° 3 (March 2020) . - pp 484 - 503[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2020031 RAB Revue Centre de documentation En réserve L003 Disponible 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)
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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]Sea-land segmentation using deep learning techniques for Landsat-8 OLI imagery / Ting Yang in Marine geodesy, Vol 43 n° 2 (March 2020)
PermalinkThe 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)
PermalinkUnsupervised extraction of urban features from airborne lidar data by using self-organizing maps / Alper Sen in Survey review, vol 52 n° 371 (March 2020)
PermalinkAutomated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference / Heidar Rastiveis in ISPRS Journal of photogrammetry and remote sensing, vol 160 (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)
PermalinkThree-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)
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)
PermalinkPermalinkAnalyse automatique du couvert végétal pour la gestion du risque végétation en milieu ferroviaire à partir d'imagerie aérienne / Hélène Rouillon (2020)
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