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A combined object- and pixel-based image analysis framework for urban land cover classification of VHR imagery / Bahram Salehi in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 11 (November 2013) . - pp 999 - 1014 Titre : A combined object- and pixel-based image analysis framework for urban land cover classification of VHR imagery Type de document : Article/Communication Auteurs : Bahram Salehi, Auteur ; Yun Zhang, Auteur ; Ming Zhong, Auteur Année de publication : 2013 Article en page(s) : pp 999 - 1014 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification par maximum de vraisemblance [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image Ikonos [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] Nouveau-Brunswick (Canada) [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] transformation en ondelettes Résumé : (Auteur) This paper aims at exploiting the advantages of pixel-based and object-based image analysis approaches for urban land cover classification of very high resolution ( VHR ) satellite imagery through a combined object- and pixel-based image analysis framework. The framework starts with segmenting the image resulting in several spectral and spatial features of segments. To overcome the curse of dimensionality, a wavelet- based feature extraction method is proposed to reduce the number of features. The wavelet-based method is automatic, fast, and can preserve local variations in objects' spectral/ spatial signatures. Finally, the extracted features together with the original bands of the image are classified using the conventional pixel-based Maximum Likelihood classification. The proposed method was tested on the WorldView-2, QuickBird, and Ikonos images of the same urban area for comparison purposes. Results show up to 17 percent, 10 percent, and 11 percent improvement in kappa coefficients compared to the case in which only the original bands of the image are used for WV - 2 , QB , and IK , respectively. Furthermore, the objects' spectral features contribute more to increasing classification accuracy than spatial features. Numéro de notice : A2013-596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.11.999 En ligne : https://doi.org/10.14358/PERS.79.11.999 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32732 [article]

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Developing an object-based hyperspatial image classifier with a case study using WorldView-2 data / Harini Sridharan in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 11 (November 2013) . - pp 1027 - 1036 Titre : Developing an object-based hyperspatial image classifier with a case study using WorldView-2 data Type de document : Article/Communication Auteurs : Harini Sridharan, Auteur ; Fang Qiu, Auteur Année de publication : 2013 Article en page(s) : pp 1027 - 1036 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] appariement de formes [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] Dallas (Texas) [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] milieu urbain Résumé : (Auteur) Recent advancements in remote sensing technology have provided a plethora of very high spatial resolution images. From pixel-based processing designed for low spatial resolution data, image processing has shifted towards object-based analysis in order to adapt to the hyperspatial nature of currently available remote sensing data. However, standard object-based classifiers work with only object-level summary statistics of the reflectance values and do not sufficiently exploit within-object reflectance pattern. In this research, a novel approach of utilizing the object-level distribution of reflectance values is presented. A fuzzy Kolmogorov-Smirnov based classifier is proposed to provide an object-to-object matching of the empirical distribution of the reflectance values of each object and derive a fuzzy membership grade to each class. This object-based classifier is tested for urban objects recognition from WorldView-2 data. Results indicate at least 10 percent increase in overall classification accuracy using the proposed classifier in comparison to various popular object- and pixel-based classifiers. Numéro de notice : A2013-597 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.11.1027 En ligne : https://doi.org/10.14358/PERS.79.11.1027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32733 [article]

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Markov land cover change modeling using pairs of time-series satellite images / Priyakant Sinha in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 11 (November 2013) . - pp 1037 - 1051 Titre : Markov land cover change modeling using pairs of time-series satellite images Type de document : Article/Communication Auteurs : Priyakant Sinha, Auteur ; Lalit Kumar, Auteur Année de publication : 2013 Article en page(s) : pp 1037 - 1051 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] flore locale [Termes descripteurs IGN] image Landsat-MSS [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] Nouvelle-Galles du Sud [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] prédiction Résumé : (Auteur) Models of change processes created with the Markov chain model (MCM) can be used in the interpolation of temporal data and in short-term change projections. However, there are two major issues associated with the use of Markov models for land-cover change projections: the stationarity of change and the impact of neighboring cells on the change areas. This study addressed these two issues using an investigation of five time-series land-cover datasets generated between 1972 and 2009 for the Liverpool region of NSW, Australia. Four short- term transition matrices were computed, and the results were used to predict land-cover distributions for the near future. The issue of neighborhood effects was addressed by incorporating spatial components in a Cellular Automata (CA)-based MCM, and the results were compared with those derived from a normal MCM. Given the marginal improvements in the simulation achieved with CA-MCM rather than MCM, and because of the ability of CA-MCM to incorporate spatial variants, CA-MCM was determined to be the more suitable method for predicting land-cover changes for the year 2019. The land-cover projection indicated that future land-cover changes will likely continue to affect the natural vegetation, which will in turn likely decrease through the continued conversion of natural to agricultural lands over the years. Numéro de notice : A2013-598 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.11.1037 En ligne : https://doi.org/10.14358/PERS.79.11.1037 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32734 [article]

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Parcel-level identification of crop types using different classification algorithms and multi-resolution imagery in southeastern Turkey / Ugur Alganci in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 11 (November 2013) . - pp 1053 - 1065 Titre : Parcel-level identification of crop types using different classification algorithms and multi-resolution imagery in southeastern Turkey Type de document : Article/Communication Auteurs : Ugur Alganci, Auteur ; Elif Sertel, Auteur ; Mutlu Ozdogan, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 1053 - 1065 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] carte agricole [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] classification par maximum de vraisemblance [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] classification Spectral angle mapper [Termes descripteurs IGN] cultures [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] image SPOT 5 [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] parcelle agricole [Termes descripteurs IGN] photo-interprétation assistée par ordinateur [Termes descripteurs IGN] Turquie Résumé : (Auteur) This research investigates the accuracy of pixel- and object-based classification techniques across varying spatial resolutions to identify crop types at parcel level and estimate the area at six test sites to find the optimum data source for the identification of crop parcels. Multi-sensor data with spatial resolutions of 2.5 m, 5 m and 10 m from SPOT5 and 30 m from Landsat-5 TM were used. Maximum Likelihood (ML), Spectral Angle Mapper (SAM), and Support Vector Machines (SVM) were used as pixel-based methods in addition to object-based image classification (OBC). Post-classification methods were applied to the output of pixel-based classification to minimize the noise effects and heterogeneity within the agricultural parcels. In addition, processing-time performance of the algorithms was evaluated for the test sites and district scale classification. OBC results provided comparatively the best performance for both parcel identification and area estimation at 10 m and finer spatial resolution levels. SVM followed OBC at 2.5 m and 5 m resolutions but accuracies decreased dramatically with coarser resolutions. ML and SAM results were worse up to 30 m resolution for both crop type identification and area estimation. In general, parcel identification efficiency was strongly correlated with spatial resolution while the classification algorithm was a more effective factor than spatial resolution for area estimation accuracy. Results also provided an opportunity to discuss the effects of image resolution and the classification algorithm independent factors such as parcel size, spatial distribution of crop types and crop patterns. Numéro de notice : A2013-599 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.11.1053 En ligne : https://doi.org/10.14358/PERS.79.11.1053 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32735 [article]

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