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An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data / Puzhao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019) . - pp 50 - 62 Titre : An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data Type de document : Article/Communication Auteurs : Puzhao Zhang, Auteur ; Andrea Nascetti, Auteur ; Yifang Ban, Auteur ; Maoguo Gong, Auteur Année de publication : 2019 Article en page(s) : pp 50 - 62 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Californie (Etats-Unis) [Termes IGN] carte de la végétation [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de changement [Termes IGN] image à haute résolution [Termes IGN] image multibande [Termes IGN] image multitemporelle [Termes IGN] image radar moirée [Termes IGN] incendie [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] Short Waves InfraRed Résumé : (auteur) Compared with optical sensors, the all-weather and day-and-night imaging ability of Synthetic Aperture Radar (SAR) makes it competitive for burnt area mapping. This study investigates the potential of Sentinel-1 C-band SAR sensors in burnt area mapping with an implicit Radar Convolutional Burn Index (RCBI). Based on multitemporal Sentinel-1 SAR data, a convolutional networks-based classification framework is proposed to learn the RCBI for highlighting the burnt areas. We explore the mapping accuracy level that can be achieved using SAR intensity and phase information for both VV and VH polarizations. Moreover, we investigate the decorrelation of Interferometric SAR (InSAR) coherence to wildfire events using different temporal baselines. The experimental results on two recent fire events, Thomas Fire (Dec., 2017) and Carr Fire (July, 2018) in California, demonstrate that the learnt RCBI has a better potential than the classical log-ratio operator in highlighting burnt areas. By exploiting both VV and VH information, the developed RCBI achieved an overall mapping accuracy of 94.68% and 94.17% on the Thomas Fire and Carr Fire, respectively. Numéro de notice : A2019-545 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.013 Date de publication en ligne : 04/10/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94189 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019121	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019123	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019122	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network / Wei Zhao 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 7066 - 7080 Titre : Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network Type de document : Article/Communication Auteurs : Wei Zhao, Auteur ; Zhirui Wang, Auteur ; Maoguo Gong, Auteur ; Jia Liu, Auteur Année de publication : 2017 Article en page(s) : pp 7066 - 7080 Note générale : Bibliograpie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse discriminante [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal [Termes IGN] détection de changement Résumé : (Auteur) With the application requirement, the technique for change detection based on heterogeneous remote sensing images is paid more attention. However, detecting changes between two heterogeneous images is challenging as they cannot be compared in low-dimensional space. In this paper, we construct an approximately symmetric deep neural network with two sides containing the same number of coupled layers to transform the two images into the same feature space. The two images are connected with the two sides and transformed into the same feature space, in which their features are more discriminative and the difference image can be generated by comparing paired features pixel by pixel. The network is first built by stacked restricted Boltzmann machines, and then, the parameters are updated in a special way based on clustering. The special way, motivated by that two heterogeneous images share the same reality in unchanged areas and retain respective properties in changed areas, shrinks the distance between paired features transformed from unchanged positions, and enlarges the distance between paired features extracted from changed positions. It is achieved through introducing two types of labels and updating parameters by adaptively changed learning rate. This is different from the existing methods based on deep learning that just do operations on positions predicted to be unchanged and extract only one type of labels. The whole process is completely unsupervised without any priori knowledge. Besides, the method can also be applied to homogeneous images. We test our method on heterogeneous images and homogeneous images. The proposed method achieves quite high accuracy. Numéro de notice : A2017-768 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2739800 En ligne : https://doi.org/10.1109/TGRS.2017.2739800 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88807 [article]

Change-detection map learning using matching pursuit / Y. Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 8 (August 2015) . - pp 4712 - 4723 Titre : Change-detection map learning using matching pursuit Type de document : Article/Communication Auteurs : Y. Li, Auteur ; Maoguo Gong, Auteur ; Licheng Jiao, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 4712 - 4723 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage automatique [Termes IGN] apprentissage dirigé [Termes IGN] couple stéréoscopique [Termes IGN] détection de changement [Termes IGN] Fleuve jaune (Chine) [Termes IGN] image ERS-SAR Résumé : (Auteur) Learning can be of great use when dealing with problems in various fields. Inspired by locally linear embedding from manifold, we propose a novel automatic change-detection method through an offline learning approach. The proposed method comprises three steps. First, two coupled dictionaries of the difference image (DI) patches and change-detection map patches are generated from known image pairs. Second, we approximately represent each patch of the input DI with respect to the DI dictionary by using the matching the pursuit algorithm. Third, the coefficients of this representation are applied with the change-detection map dictionary to generate the output change-detection map. This way, we exploit the relationship between the DI patches and the corresponding change-detection map patches based on two coupled dictionaries. In addition, the relationship guides us to construct the change-detection map for any given input DI. Experimental results on real synthetic aperture radar databases show that the proposed method is superior to its counterparts. Our method can obtain promising results, even though the dictionaries are prepared by simple random sampling from fixed training images. Numéro de notice : A2015-388 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2407953 En ligne : https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7059248 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76867 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015081	RAB	Revue	Centre de documentation	En réserve L003	Disponible

SAR change detection based on intensity and texture changes / Maoguo Gong in ISPRS Journal of photogrammetry and remote sensing, vol 93 (July 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 93 (July 2014) . - pp. 123 - 135 Titre : SAR change detection based on intensity and texture changes Type de document : Article/Communication Auteurs : Maoguo Gong, Auteur ; Yu Li, Auteur ; Licheng Jiao, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp. 123 - 135 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] graphe [Termes IGN] image radar moirée [Termes IGN] texture d'image Résumé : (Auteur)In this paper, a novel change detection approach is proposed for multitemporal synthetic aperture radar (SAR) images. The approach is based on two difference images, which are constructed through intensity and texture information, respectively. In the extraction of the texture differences, robust principal component analysis technique is used to separate irrelevant and noisy elements from Gabor responses. Then graph cuts are improved by a novel energy function based on multivariate generalized Gaussian model for more accurately fitting. The effectiveness of the proposed method is proved by the experiment results obtained on several real SAR images data sets. Numéro de notice : A2014-331 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.04.010 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.04.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73698 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2014071	RAB	Revue	Centre de documentation	En réserve L003	Disponible