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An iterative haze optimized transformation for automatic cloud/haze detection of landsat imagery / Shuli Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 5 (May 2016) . - pp 2682 - 2694 Titre : An iterative haze optimized transformation for automatic cloud/haze detection of landsat imagery Type de document : Article/Communication Auteurs : Shuli Chen, Auteur ; Xuehong Chen, Auteur ; Jin Chen, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 2682 - 2694 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] classification floue [Termes IGN] détection de flou [Termes IGN] télédétection Résumé : (Auteur) Most previous haze/cloud detection methods for Landsat imagery, e.g., haze optimized transformation (HOT), cannot adequately suppress land surface information and, in particular, often overestimate haze thickness over bright surfaces. This paper proposes an iterative HOT (IHOT) for improving haze detection with the help of a corresponding clear image. With an iterative procedure of regressions among HOT, the reflectance difference at the top of atmosphere (TOA) between hazy and clear images, and TOA reflectances of hazy and clear images, the land surface information can be removed, and the iterative HOT (IHOT) result is derived to spatially characterize the haze contamination in the Landsat images. A group of Landsat images that were acquired in different landscapes and seasons were used to test IHOT. Visual comparisons indicate that IHOT performed better than previous haze detection methods for images that were acquired in diverse landscapes and also performed robustly for hazy images that were acquired at different seasons when using the same reference clear image. Additionally, two indirect quantitative validations were used to illustrate that IHOT can provide the best transformation for accurately determining haze information. Therefore, it is expected that the proposed IHOT method will be used for automatic cloud/haze detection for large numbers of Landsat images if data sets of clear Landsat imagery are available. Numéro de notice : A2016-845 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2504369 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2504369 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82926 [article]

A spectral gradient difference based approach for land cover change detection / Jun Chen in ISPRS Journal of photogrammetry and remote sensing, vol 85 (November 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 85 (November 2013) . - pp 1 - 12 Titre : A spectral gradient difference based approach for land cover change detection Type de document : Article/Communication Auteurs : Jun Chen, Auteur ; Miao Lu, Auteur ; Xuehong Chen, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 1 - 12 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement de formes [Termes IGN] détection de changement [Termes IGN] gradient [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-TM [Termes IGN] occupation du sol Résumé : (Auteur) Change detection with remotely sensed imagery plays an important role in land cover mapping, process analysis and dynamic information services. Euclidean distance, correlation and other mathematic metrics between spectral curves have been used to calculate change magnitude in most change detection methods. However, many pseudo changes would also be detected because of inter-class spectral variance, which remains a significant challenge for operational remote sensing applications. In general, different land cover types have their own spectral curves characterized by typical spectral values and shapes. These spectral values are widely used for designing change detection algorithms. However, the shape of spectral curves has not yet been fully considered. This paper proposes to use spectral gradient difference (SGD) to quantitatively describe the spectral shapes and the differences in shape between two spectra. Change magnitude calculated in the new spectral gradient space is used to detect the change/no-change areas. Then, a chain model is employed to represent the SGD pattern both qualitatively and quantitatively. Finally, the land cover change types are determined by pattern matching with the knowledgebase of reference SGD patterns. The effectiveness of this SGD-based change detection approach was verified by a simulation experiment and a case study of Landsat data. The results indicated that the SGD-based approach was superior to the traditional methods. Numéro de notice : A2013-604 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.07.009 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.07.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32740 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2013111	RAB	Revue	Centre de documentation	En réserve L003	Disponible