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Discriminating ship from radio frequency interference based on noncircularity and non-gaussianity in sentinel-1 SAR imagery / Xiangguang Leng in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 1 (January 2019) . - pp 352 - 363 Titre : Discriminating ship from radio frequency interference based on noncircularity and non-gaussianity in sentinel-1 SAR imagery Type de document : Article/Communication Auteurs : Xiangguang Leng, Auteur ; Kefeng Ji, Auteur ; Shilin Zhou, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 352 - 363 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] interférence [Termes IGN] navire [Termes IGN] radiofréquence Résumé : (Auteur) Complex information in single-channel synthetic aperture radar (SAR) imagery is seldom used. This is a common practice based on the conventional resolution theory. However, with the advent of high-resolution SAR sensors, information in the complex data has been found to be of significance for ocean applications. In particular, we note that there is a special type of instrumental artifact in Sentinel-1 images. It is rarely researched and may be attributed to radio frequency interference (RFI). It has similar intensity with ships and can degrade ocean interpretation performance severely. This paper proposes an innovative method to discriminate ships from RFIs based on noncircularity and non-Gaussianity. Among them, noncircularity is calculated based on the measure called normalized noncircularity, and non-Gaussianity is estimated based on the complex generalized Gaussian distribution. The discrimination rationale is analyzed in detail. The experimental procedure is based on Sentinel-1 interferometric wide swath products. Only cross-polarization data are tested since RFIs are quite weak in co-polarization data. It is found that noncircularity and non-Gaussianity can characterize and identify the difference between ships and RFIs. Ships present larger noncircularity and sup-Gaussianity while RFIs are found to exhibit quite low noncircularity and mainly show sub-Gaussianity. The proposed method achieves quite good performance. These results show that noncircularity and non-Gaussianity are extremely helpful complements for single-channel SAR imagery interpretation. Numéro de notice : A2019-107 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2854661 Date de publication en ligne : 14/08/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2854661 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92414 [article]

Multi-scale object detection in remote sensing imagery with convolutional neural networks / Zhipeng Deng in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 145 - part A (November 2018) . - pp 3 - 22 Titre : Multi-scale object detection in remote sensing imagery with convolutional neural networks Type de document : Article/Communication Auteurs : Zhipeng Deng, Auteur ; Hao Sun, Auteur ; Shilin Zhou, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 3 - 22 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] aéroport [Termes IGN] détection d'objet [Termes IGN] image aérienne [Termes IGN] image optique [Termes IGN] image Sentinel-SAR [Termes IGN] réseau neuronal convolutif [Termes IGN] ville Résumé : (Auteur) Automatic detection of multi-class objects in remote sensing images is a fundamental but challenging problem faced for remote sensing image analysis. Traditional methods are based on hand-crafted or shallow-learning-based features with limited representation power. Recently, deep learning algorithms, especially Faster region based convolutional neural networks (FRCN), has shown their much stronger detection power in computer vision field. However, several challenges limit the applications of FRCN in multi-class objects detection from remote sensing images: (1) Objects often appear at very different scales in remote sensing images, and FRCN with a fixed receptive field cannot match the scale variability of different objects; (2) Objects in large-scale remote sensing images are relatively small in size and densely peaked, and FRCN has poor localization performance with small objects; (3) Manual annotation is generally expensive and the available manual annotation of objects for training FRCN are not sufficient in number. To address these problems, this paper proposes a unified and effective method for simultaneously detecting multi-class objects in remote sensing images with large scales variability. Firstly, we redesign the feature extractor by adopting Concatenated ReLU and Inception module, which can increases the variety of receptive field size. Then, the detection is preformed by two sub-networks: a multi-scale object proposal network (MS-OPN) for object-like region generation from several intermediate layers, whose receptive fields match different object scales, and an accurate object detection network (AODN) for object detection based on fused feature maps, which combines several feature maps that enables small and densely packed objects to produce stronger response. For large-scale remote sensing images with limited manual annotations, we use cropped image blocks for training and augment them with re-scalings and rotations. The quantitative comparison results on the challenging NWPU VHR-10 data set, aircraft data set, Aerial-Vehicle data set and SAR-Ship data set show that our method is more accurate than existing algorithms and is effective for multi-modal remote sensing images. Numéro de notice : A2018-488 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.04.003 Date de publication en ligne : 02/05/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.04.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91224 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018111	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018113	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2018112	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt