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A novel algorithm for differentiating cloud from snow sheets using Landsat 8 OLI imagery / Tingting Wu in Advances in space research, vol 64 n°1 (1 July 2019)  

Public [article]  inAdvances in space research > vol 64 n°1 (1 July 2019) . - pp 79 - 87 Titre : A novel algorithm for differentiating cloud from snow sheets using Landsat 8 OLI imagery Type de document : Article/Communication Auteurs : Tingting Wu, Auteur ; Ling Han, Auteur ; Qing Liu, Auteur Année de publication : 2019 Article en page(s) : pp 79 - 87 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse fractale [Termes IGN] Chine [Termes IGN] classification dirigée [Termes IGN] détection des nuages [Termes IGN] image Landsat-8 [Termes IGN] image Landsat-OLI [Termes IGN] neige [Termes IGN] réflectance [Termes IGN] seuillage d'image [Termes IGN] signature spectrale Résumé : (auteur) The separation of clouds from snow is fundamentally very challenging because of their similar spectral signature. A new algorithm was proposed to detect clouds from snow in Landsat 8 imagery. Taking the Hetian District region, where there is frequent cloud and snow cover, in northwestern China as one of the typical case areas. The typical case is presented in detail to illustrate the approach produces and results. A band math method for cloud and snow discrimination index (CSDI) was firstly conducted in this paper, fractal digital number-frequency (DN-N) algorithm and hotspot analyses were applied to determine the threshold of the CSDI and eliminate false anomalies. The results showed that an overall accuracy exceeding 95% in areas with very bright land surfaces, which indicate that this algorithm is effective for detecting clouds in specific situations where the ground objects have some reflectance characteristics similar to cloud. Numéro de notice : A2019-398 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2019.03.014 Date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1016/j.asr.2019.03.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93512 [article]