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Attribute profiles on derived features for urban land cover classification / Bharath Bhushan Damodaran in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 3 (March 2017) . - pp 183 - 193 Titre : Attribute profiles on derived features for urban land cover classification Type de document : Article/Communication Auteurs : Bharath Bhushan Damodaran, Auteur ; Joachim Höhle, Auteur ; Sébastien Lefèvre, Auteur Année de publication : 2017 Article en page(s) : pp 183 - 193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attribut [Termes IGN] classification dirigée [Termes IGN] données auxiliaires [Termes IGN] hauteur ellipsoïdale [Termes IGN] image aérienne [Termes IGN] image multibande [Termes IGN] indice de végétation [Termes IGN] occupation du sol [Termes IGN] précision de la classification [Termes IGN] zone urbaine Résumé : (Auteur) This research deals with the automatic generation of 2D land cover maps of urban areas using very high resolution multispectral aerial imagery. The appropriate selection of classifier and attributes is important to achieve high thematic accuracies. In this paper, new attributes are generated to increase the discriminative power of auxiliary information provided by remote sensing images. The generated attributes are derived from the vegetation index and elevation information using morphological attribute profiles. The extended experimental evaluation and comparison of attribute profile-based mapping solutions is conducted to derive the optimal combinations of attributes required for classification and to understand the genericity of attributes on a range of classifiers, i.e., various combinations of attributes and classifiers. Experimental results with two high resolution images show that the proposed attributes derived on auxiliary information outperform the existing attribute profiles computed on original image and its principal components. Numéro de notice : A2017-087 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.3.183 En ligne : https://doi.org/10.14358/PERS.83.3.183 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84422 [article]

Spatial-spectral unsupervised convolutional sparse auto-encoder classifier for hyperspectral imagery / Xiaobing Han in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 3 (March 2017) . - pp 195 - 206 Titre : Spatial-spectral unsupervised convolutional sparse auto-encoder classifier for hyperspectral imagery Type de document : Article/Communication Auteurs : Xiaobing Han, Auteur ; Yanfei Zhong, Auteur ; Liangpei Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 195 - 206 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classificateur non paramétrique [Termes IGN] cohérence (physique) [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale Résumé : (Auteur) The traditional spatial-spectral classification methods applied to hyperspectral remote sensing imagery are conducted by combining the spatial information vector and the spectral information vector in a separate manner, which may cause information loss and concatenation deficiency between the spatial and spectral information. In addition, the traditional morphological-based spatial-spectral classification methods require the design of handcrafted features according to experience, which is far from automatic and lacks generalization ability. To automatically represent the spatial-spectral features around the central pixel within a spatial neighborhood window, a novel spatial-spectral feature classification method based on the unsupervised convolutional sparse auto-encoder (UCSAE) with a window-in-window strategy is proposed in this study. The UCSAE algorithm features a unique spatial-spectral feature extraction approach which is executed in two stages. The first stage represents the spatial-spectral features within a spatial neighborhood window on the basis of spatial-spectral feature extraction of sub-windows with a sparse auto-encoder (SAE). The second stage exploits the spatial-spectral feature representation with a convolution mechanism for the larger outer windows. The UCSAE algorithm was validated by two widely used hyperspectral imagery datasets (the Pavia University dataset and the Washington DC Mall dataset) obtaining accuracies of 90.03 percent and 96.88 percent, respectively, which are better results than those obtained by the traditional hyperspectral spatial-spectral classification approaches. Numéro de notice : A2017-088 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.3.195 En ligne : https://doi.org/10.14358/PERS.83.3.195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84423 [article]

Unsupervised object-based differencing for land-cover change detection / Jinxia Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 3 (March 2017) . - pp 225 - 236 Titre : Unsupervised object-based differencing for land-cover change detection Type de document : Article/Communication Auteurs : Jinxia Zhu, Auteur ; Yanjun Su, Auteur ; Qinghua Guo, Auteur ; Thomas C. Harmon, Auteur Année de publication : 2017 Article en page(s) : pp 225 - 236 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme espérance-maximisation [Termes IGN] altération [Termes IGN] autocorrélation [Termes IGN] changement d'occupation du sol [Termes IGN] Chine [Termes IGN] classification non dirigée [Termes IGN] classification orientée objet [Termes IGN] détection de changement [Termes IGN] image multitemporelle [Termes IGN] image SPOT-HRV [Termes IGN] occupation du sol [Termes IGN] traitement d'image Résumé : (Auteur) One main problem of the spectral decomposition-based change detection method is the lack of efficient automatic techniques for developing the difference image. Traditional techniques generally assume that gray-level values in a difference image are independent and multitemporal images are co-registered/rectified perfectly without error. However, such assumptions are often violated because of the inevitable image misregistration and the interference of correlations between spectral bands. This study proposes an automated method based on the object-based multivariate alteration detection/maximum autocorrelation factor approach and the Gaussian mixture model-expectation maximization algorithm to obtain unsupervised difference images. This procedure is applied to bi-temporal (2005 and 2006) SPOT-HRV images at Panyu District Ponds, China. Results show that the proposed method successfully excludes the correlations of spectral bands and the influence of misregistration, as evidenced by a higher accuracy (up to 93.6 percent). These unique technical characteristics make this analytical framework suitable for detecting changes. Numéro de notice : A2017-089 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.3.225 En ligne : https://doi.org/10.14358/PERS.83.3.225 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84424 [article]