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Sig-NMS-based faster R-CNN combining transfer learning for small target detection in VHR optical remote sensing imagery / Ruchan Dong in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 11 (November 2019) . - pp 8534 - 8545 Titre : Sig-NMS-based faster R-CNN combining transfer learning for small target detection in VHR optical remote sensing imagery Type de document : Article/Communication Auteurs : Ruchan Dong, Auteur ; Dazhuan Xu, Auteur ; Jin Zhao, Auteur ; Licheng Jiao, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 8534 - 8545 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal [Termes IGN] détection d'objet [Termes IGN] détection de cible [Termes IGN] image à très haute résolution [Termes IGN] régression [Termes IGN] zone d'intérêt Résumé : (auteur) Small target detection is a challenging task in veryhigh-resolution (VHR) optical remote sensing imagery, because small targets occupy a minuscule number of pixels and are easily disturbed by backgrounds or occluded by others. Although current convolutional neural network (CNN)-based approaches perform well when detecting normal objects, they are barely suitable for detecting small ones. Two practical problems stand in their way. First, current CNN-based approaches are not specifically designed for the minuscule size of small targets (~15 or ~10 pixels in extent). Second, no well-established data sets include labeled small targets and establishing one from scratch is labor-intensive and time-consuming. To address these two issues, we propose an approach that combines Sig-NMS-based Faster R-CNN with transfer learning. Sig-NMS replaces traditional non-maximum suppression (NMS) in the stage of region proposal network and decreases the possibility of missing small targets. Transfer learning can effectively label remote sensing images by automatically annotating both object classes and object locations. We conduct an experiment on three data sets of VHR optical remote sensing images, RSOD, LEVIR, and NWPU VHR-10, to validate our approach. The results demonstrate that the proposed approach can effectively detect small targets in the VHR optical remote sensing images of about 10 × 10 pixels and automatically label small targets as well. In addition, our method presents better mean average precisions than other state-of-the-art methods: 1.5% higher when performing on the RSOD data set, 17.8% higher on the LEVIR data set, and 3.8% higher on NWPU VHR-10. Numéro de notice : A2019-595 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2921396 Date de publication en ligne : 15/07/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2921396 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94587 [article]

Multilayer projective dictionary pair learning and sparse autoencoder for PolSAR image classification / Yanqiao Chen in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 12 (December 2017) . - pp 6683 - 6694 Titre : Multilayer projective dictionary pair learning and sparse autoencoder for PolSAR image classification Type de document : Article/Communication Auteurs : Yanqiao Chen, Auteur ; Licheng Jiao, Auteur ; Yangyang Li, Auteur ; Jin Zhao, Auteur Année de publication : 2017 Article en page(s) : pp 6683 - 6694 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage dirigé [Termes IGN] extraction de traits caractéristiques [Termes IGN] image radar moirée [Termes IGN] Perceptron multicouche [Termes IGN] polarimétrie radar Résumé : (Auteur) Polarimetric synthetic aperture radar (PolSAR) image classification is a vital application in remote sensing image processing. In general, PolSAR image classification is actually a high-dimensional nonlinear mapping problem. The methods based on sparse representation and deep learning have shown a great potential for PolSAR image classification. Therefore, a novel PolSAR image classification method based on multilayer projective dictionary pair learning (MDPL) and sparse auto encoder (SAE) is proposed in this paper. First, MDPL is used to extract features, and the abstract degree of the extracted features is high. Second, in order to get the nonlinear relationship between elements of feature vectors in an adaptive way, SAE is also used in this paper. Three PolSAR images are used to test the effectiveness of our method. Compared with several state-of-the-art methods, our method achieves very competitive results in PolSAR image classification. Numéro de notice : A2017-764 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2727067 En ligne : https://doi.org/10.1109/TGRS.2017.2727067 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88800 [article]