Centre dedocumentation
scientifique

PolSAR ship detection based on neighborhood polarimetric covariance matrix / Tao Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp 4874 - 4887 Titre : PolSAR ship detection based on neighborhood polarimetric covariance matrix Type de document : Article/Communication Auteurs : Tao Liu, Auteur ; Ziyuan Yang, Auteur ; Armando Marino, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 4874 - 4887 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] détection d'objet [Termes IGN] données polarimétriques [Termes IGN] image radar moirée [Termes IGN] image Radarsat [Termes IGN] matrice de covariance [Termes IGN] navire [Termes IGN] polarimétrie radar [Termes IGN] voisinage (relation topologique) Résumé : (auteur) The detection of small ships in polarimetric synthetic aperture radar (PolSAR) images is still a topic for further investigation. Recently, patch detection techniques, such as superpixel-level detection, have stimulated wide interest because they can use the information contained in similarities among neighboring pixels. In this article, we propose a novel neighborhood polarimetric covariance matrix (NPCM) to detect the small ships in PolSAR images, leading to a significant improvement in the separability between ship targets and sea clutter. The NPCM utilizes the spatial correlation between neighborhood pixels and maps the representation for a given pixel into a high-dimensional covariance matrix by embedding spatial and polarization information. Using the NPCM formalism, we apply a standard whitening filter, similar to the polarimetric whitening filter (PWF). We show how the inclusion of neighborhood information improves the performance compared with the traditional polarimetric covariance matrix. However, this is at the expense of a higher computation cost. The theory is validated via the simulated and measured data under different sea states and using different radar platforms. Numéro de notice : A2021-424 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3022181 Date de publication en ligne : 22/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3018638 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97780 [article]

Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification / Tao Liu in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 139 (May 2018) . - pp 154 - 170 Titre : Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification Type de document : Article/Communication Auteurs : Tao Liu, Auteur ; Amr Abd-Elrahman, Auteur Année de publication : 2018 Article en page(s) : pp 154 - 170 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] drone [Termes IGN] orthoimage [Termes IGN] réseau neuronal convolutif [Termes IGN] zone humide Résumé : (Auteur) Deep convolutional neural network (DCNN) requires massive training datasets to trigger its image classification power, while collecting training samples for remote sensing application is usually an expensive process. When DCNN is simply implemented with traditional object-based image analysis (OBIA) for classification of Unmanned Aerial systems (UAS) orthoimage, its power may be undermined if the number training samples is relatively small. This research aims to develop a novel OBIA classification approach that can take advantage of DCNN by enriching the training dataset automatically using multi-view data. Specifically, this study introduces a Multi-View Object-based classification using Deep convolutional neural network (MODe) method to process UAS images for land cover classification. MODe conducts the classification on multi-view UAS images instead of directly on the orthoimage, and gets the final results via a voting procedure. 10-fold cross validation results show the mean overall classification accuracy increasing substantially from 65.32%, when DCNN was applied on the orthoimage to 82.08% achieved when MODe was implemented. This study also compared the performances of the support vector machine (SVM) and random forest (RF) classifiers with DCNN under traditional OBIA and the proposed multi-view OBIA frameworks. The results indicate that the advantage of DCNN over traditional classifiers in terms of accuracy is more obvious when these classifiers were applied with the proposed multi-view OBIA framework than when these classifiers were applied within the traditional OBIA framework. Numéro de notice : A2018-114 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.03.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.03.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89550 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification / Tao Liu in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 110 (December 2015) . - pp 34 – 47 Titre : A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification Type de document : Article/Communication Auteurs : Tao Liu, Auteur ; Jungho Im, Auteur ; Lindi J. Quackenbush, Auteur Année de publication : 2015 Article en page(s) : pp 34 – 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arbre (flore) [Termes IGN] croissance des arbres [Termes IGN] données lidar [Termes IGN] estimation de précision [Termes IGN] forêt Résumé : (auteur) This study provides a novel approach to individual tree crown delineation (ITCD) using airborne Light Detection and Ranging (LiDAR) data in dense natural forests using two main steps: crown boundary refinement based on a proposed Fishing Net Dragging (FiND) method, and segment merging based on boundary classification. FiND starts with approximate tree crown boundaries derived using a traditional watershed method with Gaussian filtering and refines these boundaries using an algorithm that mimics how a fisherman drags a fishing net. Random forest machine learning is then used to classify boundary segments into two classes: boundaries between trees and boundaries between branches that belong to a single tree. Three groups of LiDAR-derived features—two from the pseudo waveform generated along with crown boundaries and one from a canopy height model (CHM)—were used in the classification. The proposed ITCD approach was tested using LiDAR data collected over a mountainous region in the Adirondack Park, NY, USA. Overall accuracy of boundary classification was 82.4%. Features derived from the CHM were generally more important in the classification than the features extracted from the pseudo waveform. A comprehensive accuracy assessment scheme for ITCD was also introduced by considering both area of crown overlap and crown centroids. Accuracy assessment using this new scheme shows the proposed ITCD achieved 74% and 78% as overall accuracy, respectively, for deciduous and mixed forest. Numéro de notice : A2015-891 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.10.002 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2015.10.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79441 [article]