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Knowledge-based approaches to accurate mapping of mangroves from satellite data / J. Gao in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 11 (November 2004)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 70 n° 11 (November 2004) . - pp 1241 - 1248 Titre : Knowledge-based approaches to accurate mapping of mangroves from satellite data Type de document : Article/Communication Auteurs : J. Gao, Auteur ; H. Chen, Auteur ; Y. Zhang, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 1241 - 1248 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] Auckland [Termes IGN] carte de la végétation [Termes IGN] carte thématique [Termes IGN] classificateur paramétrique [Termes IGN] classification à base de connaissances [Termes IGN] classification dirigée [Termes IGN] image SPOT [Termes IGN] littoral [Termes IGN] mangrove [Termes IGN] précision sémantique [Termes IGN] raisonnement spatial Résumé : (Auteur) Mangroves are difficult to map accurately from satellite data by means of parametric classification because of their spectral similarity to other coastal vegetation despite their habitat being inside coastal waters. This study aims to improve the mapping accuracy through incorporation of such spatial knowledge about mangroves in the Waitemata Harbor of Auckland, New Zealand, from SPOT data. The spatial knowledge was combined with spectral knowledge in the mapping. Supervised classification was found to map stunted and lush mangroves at an accuracy of, respectively, 46.7 percent and 68.3 percent. These accuracy levels rose, respectively, to 83.3 percent and 96.7 percent after the spatial knowledge was sequentially incorporated into the mapping. A similar accuracy level was achieved from knowledge-based spatial reasoning. If integrated simultaneously with spectral knowledge, spatial knowledge did not improve the accuracy noticeably because of difficulty in gaining quality spectral knowledge. It is concluded that knowledge-based, post-classification processing considerably improves the accuracy of mapping mangroves over parametric classification. Numéro de notice : A2004-456 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.70.11.1241 En ligne : https://doi.org/10.14358/PERS.70.11.1241 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26976 [article]

Filtering airborne Laser scanner data: a wavelet-based clustering method / T. Thuy in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 11 (November 2004)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 70 n° 11 (November 2004) . - pp 1267 - 1274 Titre : Filtering airborne Laser scanner data: a wavelet-based clustering method Type de document : Article/Communication Auteurs : T. Thuy, Auteur ; M. Tokunaga, Auteur Année de publication : 2004 Article en page(s) : pp 1267 - 1274 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de groupement [Termes IGN] analyse multirésolution [Termes IGN] données laser [Termes IGN] erreur moyenne quadratique [Termes IGN] filtrage numérique d'image [Termes IGN] modèle de dispersion [Termes IGN] modèle numérique de terrain [Termes IGN] pente [Termes IGN] précision géométrique (imagerie) [Termes IGN] Stuttgart [Termes IGN] télémétrie laser aéroporté [Termes IGN] Tokyo (Japon) [Termes IGN] transformation en ondelettes Résumé : (Auteur) Filtering the airborne laser scanner data is challenging due to the complex distribution of objects on Earth's surface and it is still in development stage. This problem has been investigated so far with varieties of algorithms, but they suffer from different magnitudes of drawbacks. This study proposed a new and improved hybrid method based on multi-resolution analysis. Wavelet was adopted in this multi-resolution clustering approach. It enabled the classification of objects based on their size and the efficiency to filter out unwanted information at a specific resolution, and the proposed algorithm is named the ALSwave (Airborne Laser Scanner Wavelet) method. ALSwave has been tested on two data sets acquired over the urban areas of Tokyo, Japan and Stuttgart, Germany. The results showed a well-filtered, bare earth surface coupled with acceptable computational time. The accuracy assessment was carried out by comparison between the filtered bare earth surface by ALSwave and the manually filtered surface. The Root Mean Square Error (RMSE) follows a linear relationship with respect to terrain slope. This wavelet-based approach has opened a new way to filter the raw laser data that subsequently generates fast and more accurate digital terrain models. Numéro de notice : A2004-457 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.70.11.1267 En ligne : https://doi.org/10.14358/PERS.70.11.1267 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26977 [article]