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Clustering of detected changes in high-resolution satellite imagery using a stabilized competitive agglomeration algorithm / O. Sjahputera in IEEE Transactions on geoscience and remote sensing, vol 49 n° 12 Tome 1 (December 2011)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 49 n° 12 Tome 1 (December 2011) . - pp 4687 - 4703 Titre : Clustering of detected changes in high-resolution satellite imagery using a stabilized competitive agglomeration algorithm Type de document : Article/Communication Auteurs : O. Sjahputera, Auteur ; G. Scott, Auteur ; B. Claywell, Auteur ; et al., Auteur Année de publication : 2011 Article en page(s) : pp 4687 - 4703 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse de groupement [Termes IGN] chevauchement [Termes IGN] classification floue [Termes IGN] dalle [Termes IGN] détection de changement [Termes IGN] image à haute résolution [Termes IGN] mosaïque d'images Résumé : (Auteur) The Geospatial Change Detection and exploitation (GeoCDX) is a fully automated system for detection and exploitation of change between multitemporal high-resolution satellite and airborne images. Overlapping multitemporal images are first organized into 256 m x 256 m tiles in a global grid reference system. The system quantifies the overall amount of change in a given tile with a tile change score as an aggregation of pixel-level changes. The tiles are initially ranked by these change scores for retrieval, review, and exploitation in a Web-based application. However, the ranking does not account for the wide variety of change types that are typically observed in the top-ranked change tiles. To automatically organize the wide variety of change patterns observed in multitemporal high-resolution imagery, we perform tile clustering using the competitive agglomeration (CA) algorithm stabilized using the fuzzy c-means (FCM) algorithm. Each resulting cluster contains tiles with a visually similar type of change. By visual inspection of these tile clusters, GeoCDX users can quickly find certain types of change without having to sift through a large number of tiles initially organized solely by their tile change score, thereby reducing the time it takes for users to discover and exploit the change pattern(s) of greatest interest to a given application (e.g., urban growth, disaster assessment, facility monitoring, etc.). The tile clusters also provide a high-level overview of the various types of change that occur between the two observations. This overview is compared with a similar yet more limited view offered by a relevance feedback tool that requires a user to select sample tiles for use as samples in the reranking process. Numéro de notice : A2011-477 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2152847 Date de publication en ligne : 22/12/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2152847 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31371 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2011121A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A multifrequency polarimetric SAR processing chain to observe oil fields in the Gulf of Mexico / M. Migliaccio in IEEE Transactions on geoscience and remote sensing, vol 49 n° 12 Tome 1 (December 2011)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 49 n° 12 Tome 1 (December 2011) . - pp 4729 - 4737 Titre : A multifrequency polarimetric SAR processing chain to observe oil fields in the Gulf of Mexico Type de document : Article/Communication Auteurs : M. Migliaccio, Auteur ; Ferdinando Nunziata, Auteur ; A. Montuori, Auteur ; et al., Auteur Année de publication : 2011 Article en page(s) : pp 4729 - 4737 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] image radar moirée [Termes IGN] marée noire [Termes IGN] Mexique (golfe du) [Termes IGN] objet mobile [Termes IGN] pétrole [Termes IGN] polarimétrie radar [Termes IGN] surveillance écologique Résumé : (Auteur) Within the National Environmental Satellite, Data, and Information Service, National Oceanic and Atmospheric Administration, multiplatform synthetic aperture radar (SAR) imagery is being used to aid post hurricane and postaccident response efforts in the Gulf of Mexico, such as in the case of the recent Deepwater Horizon oil spill. The main areas of interest related to such disasters are the following: (1) to identify oil pipeline leaks and other oil spills at sea and (2) to detect man-made metallic targets over the sea. Within the context of disaster monitoring and response, an innovative processing chain is proposed to observe oil fields (i.e., oil spills and man-made metallic targets) using both Land C-band full-resolution and fully polarimetric SAR data. The processing chain consists of two steps. The first one, based on the standard deviation of the phase difference between the copolarized channels, allows oil monitoring. The second one, based on the different symmetry properties that characterize man-made metallic targets and natural distributed ones, allows man-made metallic target observation. Experiments, accomplished over single-look complex L-band Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and C-band RADARSAT-2 fully polarimetric SAR data gathered in the Gulf of Mexico and related to the Deepwater Horizon accident, show the effectiveness of the proposed approach. Furthermore, the proposed approach, being able to process both Land C-band fully polarimetric and full resolution SAR measurements, can take full benefit of both the ALOS PALSAR and RADARSAT-2 missions, and therefore, it allows enhancing the revisit time and coverage which are very critical issues in oil field observation. Numéro de notice : A2011-478 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2158828 Date de publication en ligne : 22/12/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2158828 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31372 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2011121A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Object-based image analysis of high-resolution satellite images using modified cloud basis function neural network and probabilistic relaxation labeling process / A. Rizvi in IEEE Transactions on geoscience and remote sensing, vol 49 n° 12 Tome 1 (December 2011)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 49 n° 12 Tome 1 (December 2011) . - pp 4815 - 4820 Titre : Object-based image analysis of high-resolution satellite images using modified cloud basis function neural network and probabilistic relaxation labeling process Type de document : Article/Communication Auteurs : A. Rizvi, Auteur ; B. Mohan, Auteur Année de publication : 2011 Article en page(s) : pp 4815 - 4820 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse d'image orientée objet [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal [Termes IGN] estimation de précision [Termes IGN] fonction de base radiale [Termes IGN] image à haute résolution [Termes IGN] processus stochastique [Termes IGN] segmentation sémantique Résumé : (Auteur) Object-based image analysis is quickly gaining acceptance among remote sensing community, and object-based image classification methods are increasingly being used for classification of land use/cover units from high-resolution satellite images with results closer to human interpretation compared to per-pixel classifiers. The problem of nonlinear separability of classes in a feature space consisting of spectral/spatial/textural features is addressed by kernel-based nonlinear mapping of the feature vectors. This facilitates use of linear discriminant functions for classification as used in artificial neural networks (ANNs). In this paper, performance of a recently introduced kernel called cloud basis function (CBF) is investigated with some modification for classification. The CBF has demonstrated superior performance to the tune of about 4% higher classification accuracy compared to conventional radial basis function used in ANN. The results are further improved by using probabilistic relaxation labeling as a postprocessing step. This paper has potential applications in urban planning and urban studies. Numéro de notice : A2011-479 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2171695 Date de publication en ligne : 22/12/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2171695 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31373 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2011121A	RAB	Revue	Centre de documentation	En réserve L003	Disponible