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GIS-based route planning in landslide-prone areas / A.K. Saha in International journal of geographical information science IJGIS, vol 19 n° 10 (november 2005)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 19 n° 10 (november 2005) . - pp 1149 - 1175 Titre : GIS-based route planning in landslide-prone areas Type de document : Article/Communication Auteurs : A.K. Saha, Auteur ; M.K. Arora, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 1149 - 1175 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] couche thématique [Termes IGN] coût [Termes IGN] effondrement de terrain [Termes IGN] Himalaya [Termes IGN] planification [Termes IGN] prévention des risques [Termes IGN] réseau routier [Termes IGN] risque naturel [Termes IGN] système d'information géographique [Termes IGN] système expert [Termes IGN] utilisation du sol Résumé : (Auteur) Many parts of the world with young mountain chains, such as the Himalayas, are highly susceptible to landslides. Due to general ruggedness and steep slopes, roads provide the only way of transportation and connectivity in such terrains. Generally, landslide hazards are overlooked during route planning. In this study, in a test area in the Himalayas, various thematic layers, viz. landslide distribution, landslide hazard zonation, landuse/landcover, drainage order and lithology are generated and integrated using Remote Sensing GIS techniques. The integrated data layer in raster form has been called a 'thematic cost map' and provides an estimate of the cost of route development and maintenance. The relative cost assignment is based on experts' knowledge. Route planning is based on neighbourhood analysis to find various movement possibilities from a pixel to its immediate neighbours. A number of patterns such as those analogous to movements in chess games have been considered. Two new neighbourhood patterns, named here Knight31 and Knight32, have been conceived in addition to commonly used Rook, Bishop and Knight patterns. The neighbourhood movement cost for moving from one pixel to a connected neighbour has been calculated for a 7 x 7 pixel window considering distance, gradient cost and thematic cost. Dijkstra's algorithm has been applied to compute the least-cost route between source and destination points. A few examples are presented to show the utility of this approach for a landslide-safe automatic route planning for a highly rugged hilly terrain. Numéro de notice : A2005-504 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810500105887 En ligne : https://doi.org/10.1080/13658810500105887 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27640 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-05091	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-05092	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Estimating and accommodating uncertainty through the soft classification of remote sensing data / M.A. Ibrahim in International Journal of Remote Sensing IJRS, vol 26 n° 14 (July 2005)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 14 (July 2005) . - pp 2995 - 3007 Titre : Estimating and accommodating uncertainty through the soft classification of remote sensing data Type de document : Article/Communication Auteurs : M.A. Ibrahim, Auteur ; M.K. Arora, Auteur ; Sanjay Kumar Ghosh, Auteur Année de publication : 2005 Article en page(s) : pp 2995 - 3007 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] carte d'occupation du sol [Termes IGN] classification dirigée [Termes IGN] classification floue [Termes IGN] classification par maximum de vraisemblance [Termes IGN] incertitude des données [Termes IGN] précision géométrique (imagerie) [Termes IGN] télédétection spatiale Résumé : (Auteur) Land cover mapping is perhaps the most important application of remote sensing data. The abundance of mixed pixels (representing uncertainties in class allocation), particularly in coarse spatial resolution images, has always been known to lead to difficulties in producing accurate land cover classifications. Soft classification methods may help in quantifying uncertainties in areas of transition between various types of land cover. This study aims to estimate and accommodate uncertainties in all stages of a supervised classification process (i.e. training, allocation and testing) so as to produce accurate and meaningful land cover classifications. Three soft classification methods have been used-a probabilistic maximum likelihood classifier and the two classifiers based on fuzzy set theory (fuzzy (c-means and possibilistic c-means). Uncertainty and accuracy measures based on a fuzzy error matrix have been adopted to evaluate each classifier. All of the classifiers show an increase in classification accuracy when uncertainty is appropriately accounted for in all stages of the supervised classification. In particular, the possibilistic c-means classifier produced the highest map and individual class accuracy and has been found to be more robust to the existence of uncertainties in the datasets. The approach suggested in this paper can be used to generate accurate land cover maps, even in the presence of uncertainties in the form of mixed pixels in remote sensing images. Numéro de notice : A2005-296 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500057806 En ligne : https://doi.org/10.1080/01431160500057806 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27432 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-05141	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas / M.K. Arora in International Journal of Remote Sensing IJRS, vol 25 n° 3 (February 2004)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 25 n° 3 (February 2004) . - pp 559 - 572 Titre : An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas Type de document : Article/Communication Auteurs : M.K. Arora, Auteur ; A.S. Das Gupta, Auteur ; Ravi P. Gupta, Auteur Année de publication : 2004 Article en page(s) : pp 559 - 572 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] cartographie des risques [Termes IGN] effondrement de terrain [Termes IGN] Himalaya [Termes IGN] image IRS [Termes IGN] réseau neuronal artificiel [Termes IGN] risque naturel [Termes IGN] système d'information géographique [Termes IGN] zone à risque Résumé : (Auteur) Landslides are natural hazards that cause havoc to both property and life every year, especially in the Himalayas. Landslide hazard zonation (LHZ) of areas affected by landslides therefore is essential for future developmental planning and organization of various disaster mitigation programmes. The conventional Geographical Information System (GIS)-based approaches for LHZ suffer from the subjective weight rating system where weights are assigned to different causative factors responsible for triggering a landslide. Alternatively, artificial neural networks (ANNs) may be applied. These are considered to be independent of any strict assumptions or bias, and they determine the weights objectively in an iterative fashion. In this study, an ANN has been applied to generate an LHZ map of an area in the Bhagirathi Valley, Himalayas, using spatial data prepared from IRS-IB satellite sensor data and maps from other sources. The accuracy of the LHZ map produced by the ANN is around 80% with a very small training dataset. The distribution of landslide hazard zones derived from ANN shows similar trends as that observed with the existing landslides locations in the field. A comparison of the results with an earlier produced GIS-based LHZ map of the same area by the authors (using the ordinal weight rating method) indicates that ANN results are better than the earlier method. Numéro de notice : A2004-063 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000156819 En ligne : https://doi.org/10.1080/0143116031000156819 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26591 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-04031	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

Unsupervised classification of hyperspectral data: an ICA mixture model based approach / Chintan A. Shah in International Journal of Remote Sensing IJRS, vol 25 n° 2 (January 2004)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 25 n° 2 (January 2004) Titre : Unsupervised classification of hyperspectral data: an ICA mixture model based approach Type de document : Article/Communication Auteurs : Chintan A. Shah, Auteur ; M.K. Arora, Auteur ; P.K. Varshney, Auteur Année de publication : 2004 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes indépendantes [Termes IGN] classification non dirigée [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] précision de la classification Résumé : (Auteur) Conventional unsupervised classification algorithms that model the data in each class with a multivariate Gaussian distribution are often inappropriate, as this assumption is frequently not satisfied by the remote sensing data. In this Letter, a new algorithm based on independent component analysis (ICA) is presented. The ICA mixture model (ICAMM) algorithm that models class distributions as non-Gaussian densities has been employed for unsupervised classification of a test image from the AVIRIS sensor. A number of feature-extraction techniques have also been examined that serve as a preprocessing step to reduce the dimensionality of the hyperspectral data. The proposed ICAMM algorithm results in significant increase in the classification accuracy over that obtained from the conventional K-means algorithm for land cover classification. Numéro de notice : A2004-060 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160310001618040 En ligne : https://doi.org/10.1080/01431160310001618040 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26588 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-04021	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt