Centre dedocumentation
scientifique

Urban slum detection using texture and spatial metrics derived from satellite imagery / Divyani Kohli in Journal of spatial science, vol 61 n° 2 (December 2016)  

Public [article]  inJournal of spatial science > vol 61 n° 2 (December 2016) . - pp 405 - 426 Titre : Urban slum detection using texture and spatial metrics derived from satellite imagery Type de document : Article/Communication Auteurs : Divyani Kohli, Auteur ; Robert Sliuzas, Auteur ; Alfred Stein, Auteur Année de publication : 2016 Article en page(s) : pp 405 - 426 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] Maharashtra (Inde ; état) [Termes descripteurs IGN] ontologie [Termes descripteurs IGN] spatial metrics [Termes descripteurs IGN] texture d'image [Termes descripteurs IGN] ville Résumé : (auteur) Slum detection from satellite imagery is challenging due to the variability in slum types and definitions. This research aimed at developing a method for slum detection based on the morphology of the built environment. The method consists of segmentation followed by hierarchical classification using object-oriented image analysis and integrating expert knowledge in the form of a local slum ontology. Results show that textural feature contrast derived from a grey-level co-occurrence matrix was useful for delineating segments of slum areas or parts thereof. Spatial metrics such as the size of segments and proportions of vegetation and built-up were used for slum detection. The percentage of agreement between the reference layer and slum classification was 60 percent. This is lower than the accuracy achieved for land cover classification (80.8 percent), due to large variations. We conclude that the method produces useful results and has potential for successful application in contexts with similar morphology. Numéro de notice : A2016--147 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/14498596.2016.1138247 date de publication en ligne : 05/05/2016 En ligne : http://dx.doi.org/10.1080/14498596.2016.1138247 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86334 [article]

GIS based drainage morphometry and its influence on hydrology in parts of Western Ghats region, Maharashtra, India / Dipak R. Samal in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)  

Public [article]  inGeocarto international > vol 30 n° 7 - 8 (August - September 2015) . - pp 755 - 778 Titre : GIS based drainage morphometry and its influence on hydrology in parts of Western Ghats region, Maharashtra, India Type de document : Article/Communication Auteurs : Dipak R. Samal, Auteur ; Shirish S. Gedam, Auteur ; R. Nagarajan, Auteur Année de publication : 2015 Article en page(s) : pp 755 - 778 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] base de données thématiques [Termes descripteurs IGN] bassin hydrographique [Termes descripteurs IGN] carte topographique [Termes descripteurs IGN] corrélation [Termes descripteurs IGN] géomorphométrie [Termes descripteurs IGN] Ghats occidentaux [Termes descripteurs IGN] hydrographie de surface [Termes descripteurs IGN] Inde [Termes descripteurs IGN] intégration de données [Termes descripteurs IGN] Maharashtra (Inde ; état) [Termes descripteurs IGN] matrice [Termes descripteurs IGN] MNS ASTER [Termes descripteurs IGN] système d'information géographique Résumé : (Auteur) Various drainage morphometric parameters in the Upper Bhima river basin and its influence on hydrological processes (e.g. runoff, peak flow, time to peak, infiltration, overland flow, etc.) were discussed using geographical information system (GIS) and remote sensing techniques. Survey of India topographical maps and ASTER digital elevation model was incorporated for thematic database generation and morphometric parameter evaluation in GIS environment. The whole study basin was divided into 8 sub-basins so that the spatial variation of morphological parameters and its influence on hydrology could be analyzed. The interrelationship between morphometric variables were computed (p Numéro de notice : A2015-501 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2014.978903 date de publication en ligne : 15/06/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2014.978903 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77417 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2015041	SL	Revue	Centre de documentation	Revues en salle	Disponible

Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study / Hossein Shafizadeh-Moghadam in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 4 (April 2015) . - pp 606 - 623 Titre : Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study Type de document : Article/Communication Auteurs : Hossein Shafizadeh-Moghadam, Auteur ; Julian Hagenauer, Auteur ; Manuchehr Farajzadeh, Auteur ; Marco Helbich, Auteur Année de publication : 2015 Article en page(s) : pp 606 - 623 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] Bombay [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] fonction de base radiale [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] Perceptron multicouche [Termes descripteurs IGN] performance [Termes descripteurs IGN] test de performance [Termes descripteurs IGN] urbanisation Résumé : (Auteur) The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling. Numéro de notice : A2015-589 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.993989 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.993989 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77875 [article]

Learning with transductive SVM for semisupervised pixel classification of remote sensing imagery / Ujjwal Maulik in ISPRS Journal of photogrammetry and remote sensing, vol 77 (March 2013)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 77 (March 2013) . - pp 66 - 78 Titre : Learning with transductive SVM for semisupervised pixel classification of remote sensing imagery Type de document : Article/Communication Auteurs : Ujjwal Maulik, Auteur ; Debasis Chakraborty, Auteur Année de publication : 2013 Article en page(s) : pp 66 - 78 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] Bombay [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] classification pixellaire [Termes descripteurs IGN] classification semi-dirigée [Termes descripteurs IGN] image infrarouge couleur [Termes descripteurs IGN] image SPOT [Termes descripteurs IGN] Inde [Termes descripteurs IGN] ville Résumé : (Auteur) Land cover classification using remotely sensed data requires robust classification methods for the accurate mapping of complex land cover area of different categories. In this regard, support vector machines (SVMs) have recently received increasing attention. However, small number of training samples remains a bottleneck to design suitable supervised classifiers. On the other hand, adequate number of unlabeled data is available in remote sensing images which can be employed as additional source of information about margins. To fully leverage all of the precious unlabeled data, integration of filtering in a transductive SVM is proposed. Using two labeled image datasets of small size and two large unlabeled image datasets, the effectiveness of the proposed method is explored. Experimental results show that the proposed technique achieves average overall accuracies of around 4.5–7.8%, 0.8–2.6% and 0.9–2.2% more than the standard inductive SVM (ISVM), progressive transductive SVM (PTSVM) and low density separation (LDS) classifiers, respectively on larger domains in case of labeled datasets. Using image datasets, visual interpretation from the classified images as well as the segmentation quality reveal that the proposed method can efficiently filter informative data from the unlabeled samples. Numéro de notice : A2013-116 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32254 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2013031	RAB	Revue	Centre de documentation	En réserve 3L	Disponible

Satellite image classification using genetically guided fuzzy clustering with spatial information / S. Bandyopadhyay in International Journal of Remote Sensing IJRS, vol 26 n° 3 (February 2005)

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 3 (February 2005) . - pp 579 - 593 Titre : Satellite image classification using genetically guided fuzzy clustering with spatial information Type de document : Article/Communication Auteurs : S. Bandyopadhyay, Auteur Année de publication : 2005 Article en page(s) : pp 579 - 593 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] Bombay [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] pixel [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] utilisation du sol Résumé : (Auteur) Land-cover classification of satellite images is an important task in analysis of remote sensing imagery. Segmentation is one of the widely used techniques in this regard. One of the important approaches for segmentation of an image is by clustering the pixels in the spectral domain, where pixels that share some common spectral property are put in the same group, or cluster. However, such spectral clustering completely ignores the spatial information contained in the pixels, which is often an important consideration for good segmentation of images. Moreover, the clustering algorithms often provide locally optimal solutions. In this paper, we propose to perform. image segmentation by a genetically guided unsupervised fuzzy clustering technique where some spatial information of the pixels is incorporated. Two ways of incorporating spatial information are suggested. The characteristic of this technique is that it is able to determine automatically the appropriate number of clusters without making any assumptions regarding the dataset. while attempting to provide globally near optimal solutions. In order to evolve the appropriate number of clusters, the chromosome encoding scheme is enhanced to incorporate the don't care symbol (#). Real-coded genetic algorithm with appropriatly defined operators is used. A cluster validity index is used as a measure of the value of the chromosomes. Results, both quantitative and qualitative are demonstrated for several images, including a satellite image of a part of the city of Mumbai. Numéro de notice : A2005-042 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27180 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-05031	RAB	Revue	Centre de documentation	En réserve 3L	Disponible