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Multitemporal/multiband SAR classification of urban areas using spatial analysis: statistical versus neural kernel-based approach / T. Macri Pellizzei in IEEE Transactions on geoscience and remote sensing, vol 41 n° 10 (October 2003)
[article]
Titre : Multitemporal/multiband SAR classification of urban areas using spatial analysis: statistical versus neural kernel-based approach Type de document : Article/Communication Auteurs : T. Macri Pellizzei, Auteur ; Paolo Gamba, Auteur ; P. Lombardo, Auteur ; F. Dell'acqua, Auteur Année de publication : 2003 Article en page(s) : pp 2338 - 2353 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse comparative
[Termes IGN] classification dirigée
[Termes IGN] classification floue
[Termes IGN] classification par réseau neuronal
[Termes IGN] image radar moirée
[Termes IGN] image SIR-C
[Termes IGN] milieu urbain
[Termes IGN] réalité de terrain
[Termes IGN] segmentation d'image
[Termes IGN] test de performanceRésumé : (Auteur) In this paper, we derive two techniques for the classification of Multifrequency/multitemporal polarimetric SAR images, based respectively on a statistical and on a neural approach. Both techniques are especially designed to exploit the spatial structure of the observed scene, thus allowing more stable classification results. Such techniques are useful when looking at medium - to - scale features, like the boundaries between urban and non-urban areas. They are applied to a set of SIR-C images of a urban area, to test their effectiveness in the identification of the different classes that compose the observed scene. A lower and an upper bound to the classification performance are introduced to characterise their limits. They correspond respectively to pixel-by-pixel classification and to the joint classification of the pixels belonging to the different classes identified in the ground truth. The results achieved with the two approaches are quantitatively analysed by comparing them to the ground truth. Moreover, a hybrid approach is presented, where the homogeneous regions identified through statistical segmentation are classified using a neuro-fuzzy technique. Finally, a quantitative analysis of the results achieved with all the proposed techniques is carried out, showing that their classification performance is much higher than the lower bound and reasonably close to the upper bound. This is a consequence of their effectiveness in the exploitation of the spatial information. Numéro de notice : A2003-356 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.818762 En ligne : https://doi.org/10.1109/TGRS.2003.818762 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26436
in IEEE Transactions on geoscience and remote sensing > vol 41 n° 10 (October 2003) . - pp 2338 - 2353[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-03101 RAB Revue Centre de documentation En réserve L003 Disponible Comparing ARTMAP neural network with the maximum-likelihood classifier for detecting urban change / K.C. Seto in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)
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Titre : Comparing ARTMAP neural network with the maximum-likelihood classifier for detecting urban change Type de document : Article/Communication Auteurs : K.C. Seto, Auteur ; W. Liu, Auteur Année de publication : 2003 Article en page(s) : pp 981 - 990 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agrégation spatiale
[Termes IGN] analyse comparative
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par réseau neuronal
[Termes IGN] croissance urbaine
[Termes IGN] détection de changement
[Termes IGN] image Landsat-TM
[Termes IGN] milieu urbainRésumé : (Auteur) Urbanization has profound effects on the environment at local, regional, and global scales. Effective detection of urban change using remote sensing data will be an essential component of global environmental change research, regional planning. and natural resource management. This paper presents results from an ARTMAP neural network to detect urban change with Landsat TM images from two periods. Classification of urban change, and, in particular, conversion of agriculture to urban, was statistically more accurate with ARTMAP than with a more conventional technique, the Bayesian maximum-likelihood classifier (MLC). The effect of different levels of class aggregation on the performance of change detection was also explored with ARTMAP and MLC. Because ARTMAP explicitly allows "many-to-one "mapping, classification using coarse class resolution and fine class resolution training data generated similar results. Together, these results suggest that ARTMAP can reduce labor and computational costs associated with assembling training data while concurrently generating more accurate urban change detection results. Numéro de notice : A2003-228 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.69.9.981 En ligne : http://dx.doi.org/10.14358/PERS.69.9.981 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22523
in Photogrammetric Engineering & Remote Sensing, PERS > vol 69 n° 9 (September 2003) . - pp 981 - 990[article]Improvements to urban area characterization using multitemporal and multiangle SAR images / F. Dell'acqua in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)
[article]
Titre : Improvements to urban area characterization using multitemporal and multiangle SAR images Type de document : Article/Communication Auteurs : F. Dell'acqua, Auteur ; Paolo Gamba, Auteur ; G. Lisini, Auteur Année de publication : 2003 Article en page(s) : pp 1996 - 2004 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] bande C
[Termes IGN] bande X
[Termes IGN] classification floue
[Termes IGN] classification par réseau neuronal
[Termes IGN] extraction du réseau routier
[Termes IGN] histogramme
[Termes IGN] image ERS-SAR
[Termes IGN] image multitemporelle
[Termes IGN] Lombardie
[Termes IGN] milieu urbain
[Termes IGN] précision de la classification
[Termes IGN] réalité de terrain
[Termes IGN] réseau routierRésumé : (Auteur) In this paper, we present some improvements to urban area characterization by means of synthetic aperture radar (SAR) images using multitemporal and multiangle datasets. The first aim of this research is to show that a temporal sequence of satellite SAR data may improve the classification accuracy and the discriminability of land cover classes in an urban area. Similarly, a second point worth discussing is to what extent multiangle SAR data allows extracting complementary urban features, exploiting different acquisition geometries. To these aims, in this paper, we show results on the same urban test site (Pavia, northern Italy), referring to a sequence of European Remote Sensing Satellite 1/2 (ERS1/2) Cband images and to a set of simulated Xband data with a finer spatial resolution and different viewing angles. In particular, the multitemporal data is analyzed by means of a novel procedure based on a neurofuzzy classifier whose input is a subset of the ERS sequence chosen using the histogram distance index. Instead, the multiangle dataset is used to provide a better characterization of the road network in the area, overcoming effects due to the orientation of the SAR sensor. Numéro de notice : A2003-251 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.814631 En ligne : https://doi.org/10.1109/TGRS.2003.814631 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22546
in IEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 1996 - 2004[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-03091 RAB Revue Centre de documentation En réserve L003 Disponible The use of fully polarimetric information for the fuzzy neural classification of SAR images / C.T. Chen in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)
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Titre : The use of fully polarimetric information for the fuzzy neural classification of SAR images Type de document : Article/Communication Auteurs : C.T. Chen, Auteur ; K.S. Chen, Auteur ; Jong-Sen Lee, Auteur Année de publication : 2003 Article en page(s) : pp 2089 - 2100 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification floue
[Termes IGN] classification par réseau neuronal
[Termes IGN] données polarimétriques
[Termes IGN] image AIRSAR
[Termes IGN] matrice de covariance
[Termes IGN] rétrodiffusion
[Termes IGN] utilisation du sol
[Termes IGN] vectorisationRésumé : (Auteur) This paper presents a method, based on a fuzzy neural network, that uses fully polarimetric information for terrain and land-use classification of synthetic aperture radar (SAR) image. The proposed approach makes use of statistical properties of polarimetric data, and takes advantage of a fuzzy neural network. A distance measure, based on a complex Wishart distribution, is applied using the fuzzy c-means clustering algorithm, and the clustering result is then incorporated into the neural network. Instead of preselecting the polarization channels to form a feature vector, all elements of the polarimetric covariance matrix serve as the target feature vector as inputs to the neural network. It is thus expected that the neural network will include fully polarimetric backscattering information for image classification. With the generalization, adaptation, and other capabilities of the neural network, information contained in the covariance matrix, such as the amplitude, the phase difference, the degree of polarization, etc., can be fully explored. A test image, acquired by the Jet Propulsion Laboratory Airborne SAR (AIRSAR) system, is used to demonstrate the advantages of the proposed method. It is shown that the proposed approach can greatly enhance the adaptability and the flexibility giving fully polarimetric SAR for terrain cover classification. The integration of fuzzy c-means (FCM) and fast generalization dynamic learning neural network (DLNN) capabilities makes the proposed algorithm an attractive and alternative method for polarimetric SAR classification. Numéro de notice : A2003-255 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.813494 En ligne : https://doi.org/10.1109/TGRS.2003.813494 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22550
in IEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 2089 - 2100[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-03091 RAB Revue Centre de documentation En réserve L003 Disponible A very quick neural network algorithm for cloud detection / K.R. Al-Rawi in Geocarto international, vol 18 n° 1 (March - May 2003)
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Titre : A very quick neural network algorithm for cloud detection Type de document : Article/Communication Auteurs : K.R. Al-Rawi, Auteur ; José Luis Casanova, Auteur ; A. Vasileisky, Auteur Année de publication : 2003 Article en page(s) : pp 45 - 50 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par réseau neuronal
[Termes IGN] image NOAA-AVHRR
[Termes IGN] nuage
[Termes IGN] radianceRésumé : (Auteur) A very quick neural network algorithm for cloud detection, based on a neural network, is developed. Cloud detection is speeded up through the use of the Class Assigning Space (CAS). The CAS is a classified Radiance Space (RS), which has been built using the trained neural network. The GAS is used to assign a class for each pixel in the image instead of using the ANN to treat every single pixel. The detection time is approximately one second. Channel 1 and channel 5 of N0AA-AVHRR images have been used. The Supervised ARTII artificial neural network has been employed. The system performance has been tested with different training sets and different input data. Numéro de notice : A2003-101 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040308542262 En ligne : https://doi.org/10.1080/10106040308542262 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22397
in Geocarto international > vol 18 n° 1 (March - May 2003) . - pp 45 - 50[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-03011 RAB Revue Centre de documentation En réserve L003 Disponible PermalinkLand cover classification models using Shuttle Imaging Radar (SIR-C) data: a case study in New Hampshire, USA / R. Narayanan in Geocarto international, vol 17 n° 3 (September - November 2002)PermalinkA multiple-cascade-classifier system for a robust and partially unsupervised updating of land-cover maps / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 40 n° 9 (September 2002)PermalinkIntegration of classification methods for improvement of land-cover map accuracy / XiaoHang Liu in ISPRS Journal of photogrammetry and remote sensing, vol 56 n° 4 (July - August 2002)PermalinkReconnaissance de patterns par réseaux de neurones / M.K. Allouche in Revue internationale de géomatique, vol 11 n° 2 (juin - aout 2001)PermalinkGeoComputational modelling / Manfred M. Fischer (2001)PermalinkSpot panchromatic imagery and neural networks for information extraction in a complex mountain environment / M.P. Bishop in Geocarto international, vol 14 n° 2 (June - August 1999)PermalinkAdvances in remote sensing and GIS analysis, [selected papers from a meeting held at the University of Southampton, July 25, 1996] / P.M. Atkinson (1999)PermalinkCoopération et fusion d'opérateurs : application au recalage automatique d'objets cartographiques / Pierre Dhérété (1999)PermalinkRSS 99 Earth observation / P. Pan (1999)Permalink