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Urban change detection based on coherence and intensity characteristics of SAR imagery / M. Liao in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 8 (August 2008)

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 8 (August 2008) . - pp 999 - 1006 Titre : Urban change detection based on coherence and intensity characteristics of SAR imagery Type de document : Article/Communication Auteurs : M. Liao, Auteur ; L. Jiang, Auteur ; H. Lin, Auteur ; et al., Auteur Année de publication : 2008 Article en page(s) : pp 999 - 1006 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] cohérence [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image ERS-SAR [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] intensité lumineuse [Termes descripteurs IGN] radargrammétrie [Termes descripteurs IGN] seuillage d'image [Termes descripteurs IGN] Shanghai (Chine) Résumé : (Auteur) In this paper, an unsupervised change-detection approach was proposed to detect new urban areas from multi-temporal SAR images. The novelty of the proposed approach is the joint use of coherence and intensity characteristics of SAR imagery. The approach involves two main steps: (a) the extraction of difference feature containing information on changed areas, and (b) the unsupervised two-dimensional (2D) thresholding. First, two difference features based on the concepts of long-term coherence and backscattering temporal variability are extracted from a series of multitemporal SAR images. Then, the resulting features that represent the INSAR signal temporal variability of changed areas are merged, and a 2D thresholding technique based on the maximum 2D Renyi’s entropy criterion is developed to obtain the change-detection results. The effectiveness of the proposed approach is confirmed with experimental results obtained from a set of six ERS-1/2 SLC SAR images acquired in Shanghai, China. Copyright ASPRS Numéro de notice : A2008-329 Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29322 [article]

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Adjusting for long term anomalous trends in NOAA's Global Vegetation Index datasets / L. Jiang in IEEE Transactions on geoscience and remote sensing, vol 46 n° 2 (February 2008)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 46 n° 2 (February 2008) . - pp 409 - 422 Titre : Adjusting for long term anomalous trends in NOAA's Global Vegetation Index datasets Type de document : Article/Communication Auteurs : L. Jiang, Auteur ; D. Tarpley, Auteur ; K. Mitchell, Auteur ; et al., Auteur Année de publication : 2008 Article en page(s) : pp 409 - 422 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] correction [Termes descripteurs IGN] erreur systématique [Termes descripteurs IGN] image NOAA-AVHRR [Termes descripteurs IGN] modèle météorologique [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] sécheresse [Termes descripteurs IGN] stabilité Résumé : (Auteur) The weekly 0.144° resolution global vegetation index from the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) has a long history, starting late 1981, and has included data derived from Advanced Very High Resolution Radiometer (AVHRR) sensors onboard NOAA-7, -9, -11, -14, -16, -17, and -18 satellites. Even after postlaunch calibration and mathematical smoothing and filtering of the normalized difference vegetation index (NDVI) derived from AVHRR visible and near-infrared channels, the time series of global smoothed NDVI (SMN) still has apparent discontinuities and biases due to sensor degradation, orbital drift [equator crossing time (ECT)], and differences from instrument to instrument in band response functions. To meet the needs of the operational weather and climate modeling and monitoring community for a stable long-term global NDVI data set, we investigated adjustments to substantially reduce the bias of the weekly global SMN series by simple and efficient algorithms that require a minimum number of assumptions about the statistical properties of the interannual global vegetation changes. Of the algorithms tested, we found the adjusted cumulative distribution function (ACDF) method to be a well-balanced approach that effectively eliminated most of the long-term global-scale interannual trend of AVHRR NDVI. Improvements to the global and regional NDVI data stability have been demonstrated by the results of ACDF-adjusted data set evaluated at a global scale, on major land classes, with relevance to satellite ECT, at major continental regions, and at regional drought detection applications. Copyright IEEE Numéro de notice : A2008-072 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29067 [article]

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