Centre de documentation
scientifique

Efficient shadow detection of color aerial images based on successive thresholding scheme / K.L. Chung in IEEE Transactions on geoscience and remote sensing, vol 47 n° 2 (February 2009)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 47 n° 2 (February 2009) . - pp 671 - 682 Titre : Efficient shadow detection of color aerial images based on successive thresholding scheme Type de document : Article/Communication Auteurs : K.L. Chung, Auteur ; Y. Lin, Auteur Année de publication : 2009 Article en page(s) : pp 671 - 682 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] détection d'ombre [Termes IGN] image aérienne [Termes IGN] image en couleur [Termes IGN] seuillage d'image [Termes IGN] teinte Résumé : (Auteur) Recently, Tsai presented an efficient algorithm which uses the ratio value of the hue over the intensity to construct the ratio map for detecting shadows of color aerial images. Instead of only using the global thresholding process in Tsai's algorithm, this paper presents a novel successive thresholding scheme (STS) to detect shadows more accurately. In our proposed STS, the modified ratio map, which is obtained by applying the exponential function to the ratio map proposed by Tsai, is presented to stretch the gap between the ratio values of shadow and nonshadow pixels. By performing the global thresholding process on the modified ratio map, a coarse-shadow map is constructed to classify the input color aerial image into the candidate shadow pixels and the nonshadow pixels. In order to detect the true shadow pixels from the candidate shadow pixels, the connected component process is first applied to the candidate shadow pixels for grouping the candidate shadow regions. For each candidate shadow region, the local thresholding process is performed iteratively to extract the true shadow pixels from the candidate shadow region. Finally, for the remaining candidate shadow regions, a fine-shadow determination process is applied to identify whether each remaining candidate shadow pixel is the true shadow pixel or not. Under six testing images, experimental results show that, for the first three testing images, both Tsai's and our proposed algorithms have better detection performance than that of the algorithm of Huang , and the shadow detection accuracy of our proposed STS-based algorithm is comparable to Tsai's algorithm. For the other three testing images, which contain some low brightness objects, our proposed algorithm has better shadow detection accuracy when compared with the previous two shadow detection algorithms proposed by Huang and Tsai. Copyright IEEE Numéro de notice : A2009-025 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2008.2004629 En ligne : https://doi.org/10.1109/TGRS.2008.2004629 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29655 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-09021	RAB	Revue	Centre de documentation	En réserve L003	Disponible