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Landsat ETM+ image applications to extract information for environmental planning in a Colombian city / L.M. Santana in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°19-20 (October 2007) . - pp 4225 - 4242 Titre : Landsat ETM+ image applications to extract information for environmental planning in a Colombian city Type de document : Article/Communication Auteurs : L.M. Santana, Auteur Année de publication : 2007 Article en page(s) : pp 4225 - 4242 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse de variance [Termes IGN] Colombie [Termes IGN] croissance urbaine [Termes IGN] densité de population [Termes IGN] détection automatique [Termes IGN] image Landsat-ETM+ [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] température au sol [Termes IGN] ville Résumé : (Auteur) Latin American cities have witnessed rapid and unplanned growth causing social, economic and environmental problems. To solve these problems, urban planners require information and indicators that normally are not available. In this study, the applicability of remote sensing data to extract environmental data was examined. A Landsat Enhanced Thematic Mapper (ETM+) image was used to gather information about land surface temperature (Ts) and its relationship with the Normalized Difference Vegetation Index (NDVI) and the Leaf Water Content Index (LWC). A strong negative relationship between Ts and NDVI and between Ts and LWC was observed. Analysis of variance points out statistically significant differences in the averages of Ts, NDVI, and LWC among neighbourhoods. Areas with high density housing, with a deficient urban design and those with commercial establishments had the lowest means of NDVI and LWC, and higher means of Ts. On the other hand, neighbourhoods with a higher proportion of trees and green zones had higher NDVI and LWC, and lower Ts. Finally, all neighbourhoods were classified into those that have lower to higher Ts. Therefore, the greening campaigns and new landscape design of the city should be directed specifically at neighbourhoods with the lowest level of NDVI or LWC. Copyright Taylor & Francis Numéro de notice : A2007-445 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701244856 En ligne : https://doi.org/10.1080/01431160701244856 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28808 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-07111	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Classification of floodplain vegetation by data fusion of spectral (CASI) and LiDAR data / G.W. Geerling in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°19-20 (October 2007) . - pp 4263 - 4284 Titre : Classification of floodplain vegetation by data fusion of spectral (CASI) and LiDAR data Type de document : Article/Communication Auteurs : G.W. Geerling, Auteur ; M. Labrador-Garcia, Auteur ; J. Clevers, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 4263 - 4284 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] classification non dirigée [Termes IGN] données lidar [Termes IGN] flore locale [Termes IGN] fusion de données [Termes IGN] image CASI [Termes IGN] Kappa de Cohen [Termes IGN] lasergrammétrie [Termes IGN] lit majeur [Termes IGN] Pays-Bas [Termes IGN] pixel [Termes IGN] rivière Résumé : (Auteur) To safeguard the goals of flood protection and nature development, a river manager requires detailed and up-to-date information on vegetation structures in floodplains. In this study, remote-sensing data on the vegetation of a semi-natural floodplain along the river Waal in the Netherlands were gathered by means of a Compact Airborne Spectrographic Imager (CASI; spectral information) and LiDAR (structural information). These data were used to classify the floodplain vegetation into eight and five different vegetation classes, respectively. The main objective was to fuse the CASI and LiDAR-derived datasets on a pixel level and to compare the classification results of the fused dataset with those of the non-fused datasets. The performance of the classification results was evaluated against vegetation data recorded in the field. The LiDAR data alone provided insufficient information for accurate classification. The overall accuracy amounted to 41% in the five-class set. Using CASI data only, the overall accuracy was 74% (five-class set). The combination produced the best results, raising the overall accuracy to 81% (five-class set). It is concluded that fusion of CASI and LiDAR data can improve the classification of floodplain vegetation, especially for those vegetation classes which are important to predict hydraulic roughness, i.e. bush and forest. A novel measure, the balance index, is introduced to assess the accuracy of error matrices describing an ordered sequence of classes such as vegetation structure classes that range from bare soil to forest. Copyright Taylor & Francis Numéro de notice : A2007-446 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701241720 En ligne : https://doi.org/10.1080/01431160701241720 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28809 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-07111	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Regenerating boreal forest structure estimation using SPOT-5 pan-sharpened imagery / A.L. Wunderle in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°19-20 (October 2007) . - pp 4351 - 4364 Titre : Regenerating boreal forest structure estimation using SPOT-5 pan-sharpened imagery Type de document : Article/Communication Auteurs : A.L. Wunderle, Auteur ; Steven E. Franklin, Auteur ; X.G. Guo, Auteur Année de publication : 2007 Article en page(s) : pp 4351 - 4364 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Alberta (Canada) [Termes IGN] analyse en composantes principales [Termes IGN] complexité [Termes IGN] forêt boréale [Termes IGN] habitat animal [Termes IGN] image SPOT 5 [Termes IGN] Mammalia [Termes IGN] pansharpening (fusion d'images) [Termes IGN] régression [Termes IGN] texture d'image Résumé : (Auteur) Forested stand structure is an important target variable within the fields of wildlife ecology. Remote sensing has often been suggested as a viable alternative to time consuming field and aerial investigations to determine forest structural attributes. In this study, 44 stands of recently harvested, regenerating, and old growth forest within the Foothills Model Forest in west-central Alberta were selected to test the ability of pan-sharpened SPOT-5 spectral response to classify stand structure. For each stand, a Structural Complexity Index (SCI) was calculated from field data using principal components analysis. To complement the spectral response data set and further increase accuracy, the normalized difference moisture index (NDMI) and three window sizes (55, 1111, and 2525) of first- (mean and standard deviation) and second-order (homogeneity, entropy, contrast, and correlation) textural measures were calculated over the pan-sharpened image. Stepwise multivariate regression analysis was used to determine the best explanatory model of the SCI using the spectral and textural data. The NDMI, first-order standard deviation and second-order correlation texture measures were better able to explain differences in SCI among the 44 forest stands (r2 = 0.79). The most appropriate window size for the texture measures was 55 indicating that this is a measure only detectable at a very high spatial resolution. The resulting classified SCI values were comparable to the actual field level SCI (r2 = 0.74, p = 0.01) and were limited by the strong variability within stands. Future research may find this measure useful either as a separate parameter or as an indicator of forest age for use in wildlife habitat modelling. Copyright Taylor & Francis Numéro de notice : A2007-447 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701244849 En ligne : https://doi.org/10.1080/01431160701244849 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28810 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-07111	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Artificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland / S. Chattopadhyay in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°19-20 (October 2007) . - pp 4471 - 4482 Titre : Artificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland Type de document : Article/Communication Auteurs : S. Chattopadhyay, Auteur ; G. Bandyopadhyay, Auteur Année de publication : 2007 Article en page(s) : pp 4471 - 4482 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse diachronique [Termes IGN] apprentissage automatique [Termes IGN] ozone [Termes IGN] Perceptron multicouche [Termes IGN] pollution atmosphérique [Termes IGN] prédiction [Termes IGN] réseau neuronal artificiel [Termes IGN] Suisse Résumé : (Auteur) The present study deals with the mean monthly total ozone time series over Arosa, Switzerland. The study period is 1932-1971. First of all, the total ozone time series has been identified as a complex system and then Artificial Neural Network models in the form of Multilayer Perceptron with back propagation learning have been developed. The models are Single-hidden-layer and Two-hidden-layer Perceptrons with sigmoid activation function. After sequential learning with a learning rate of 0.9 the peak total ozone period (February-May) concentrations of mean monthly total ozone have been predicted by the two neural net models. After training and validation, both of the models are found to be skillful. But the Two-hidden-layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period. Copyright Taylor & Francis Numéro de notice : A2007-448 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701250440 En ligne : https://doi.org/10.1080/01431160701250440 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28811 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-07111	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Multispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation / A. Agrawal in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°19-20 (October 2007) . - pp 4597 - 4608 Titre : Multispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation Type de document : Article/Communication Auteurs : A. Agrawal, Auteur ; N. Kumar, Auteur ; M. Radhakrishna, Auteur Année de publication : 2007 Article en page(s) : pp 4597 - 4608 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal [Termes IGN] ERDAS Imagine [Termes IGN] image IRS-LISS [Termes IGN] image multibande [Termes IGN] incertitude des données [Termes IGN] Inde [Termes IGN] Kappa de Cohen [Termes IGN] Perceptron multicouche [Termes IGN] sous ensemble flou Résumé : (Auteur) A supervised neural network classification model based on rough-fuzzy membership function, weak fuzzy similarity relation, multilayer perceptron, and back-propagation algorithm is proposed. The described model is capable of dealing with rough uncertainty as well as fuzzy uncertainty associated with the classification of multispectral images. The concept of weak fuzzy similarity relation is used for generation of fuzzy equivalence classes during the calculation of rough-fuzzy membership function. The model allows efficient modelling of indiscernibility and fuzziness between patterns by appropriate weights being assigned using the back-propagated errors depending upon the rough-fuzzy membership values at the corresponding outputs. The effectiveness of the proposed model is demonstrated on classification problem of IRS-P6 LISS IV image of Allahabad area. The results are compared with statistical (minimum distance to means), conventional Multi-Layer Perceptron (MLP) and Fuzzy Multi-Layer Perceptron (FMLP) models. The better overall accuracy, user's and producer's accuracies and kappa coefficient of the proposed classifier in comparison to other considered models demonstrate the effectiveness of this model in multispectral image classification. Copyright Taylor & Francis Numéro de notice : A2007-449 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701244898 En ligne : https://doi.org/10.1080/01431160701244898 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28812 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-07111	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition / Jing Tian in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°19-20 (October 2007) . - pp 4625 - 4644 Titre : Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition Type de document : Article/Communication Auteurs : Jing Tian, Auteur ; D.M. Chen, Auteur Année de publication : 2007 Article en page(s) : pp 4625 - 4644 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bati [Termes IGN] détail topographique artificiel [Termes IGN] eCognition [Termes IGN] image à résolution métrique [Termes IGN] image Ikonos [Termes IGN] image multibande [Termes IGN] Ontario (Canada) [Termes IGN] optimisation (mathématiques) [Termes IGN] segmentation multi-échelle Résumé : (Auteur) Multi-resolution segmentation, as one of the most popular approaches in object-oriented image segmentation, has been greatly enabled by the advent of the commercial software, eCognition. However, the application of multi-resolution segmentation still poses problems, especially in its operational aspects. This paper addresses the issue of optimization of the algorithm-associated parameters in multi-resolution segmentation. A framework starting with the definition of meaningful objects is proposed to find optimal segmentations for a given feature type. The proposed framework was tested to segment three exemplary artificial feature types (sports fields, roads, and residential buildings) in IKONOS multi-spectral images, based on a sampling scheme of all the parameters required by the algorithm. Results show that the feature-type-oriented segmentation evaluation provides an insight to the decision-making process in choosing appropriate parameters towards a high-quality segmentation. By adopting these feature-type-based optimal parameters, multi-resolution segmentation is able to produce objects of desired form to represent artificial features. Copyright Taylor & Francis Numéro de notice : A2007-450 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701241746 En ligne : https://doi.org/10.1080/01431160701241746 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28813 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-07111	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Classified road detection from satellite images based on perceptual organization / J. Yang in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°19-20 (October 2007) . - pp 4653 - 4669 Titre : Classified road detection from satellite images based on perceptual organization Type de document : Article/Communication Auteurs : J. Yang, Auteur ; R.S. Wang, Auteur Année de publication : 2007 Article en page(s) : pp 4653 - 4669 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] algorithme génétique [Termes IGN] axe médian [Termes IGN] classification automatique [Termes IGN] extraction du réseau routier [Termes IGN] image satellite [Termes IGN] lissage de courbe [Termes IGN] méthode heuristique [Termes IGN] objet géographique [Termes IGN] primitive géométrique Résumé : (Auteur) Extracting roads from satellite images is an important task in both research and practice. This work presents an improved model for road detection based on the principles of perceptual organization and classification fusion in human vision system (HVS). The model consists of four levels: pixels, primitives, structures and objects, and two additional sub-processes: automatic classification of road scenes and global integration of multiform roads. Based on the model, a novel algorithm for detecting roads from satellite images is also proposed, in which two types of road primitives, namely blob-like primitive and line-like primitive are defined, measured, extracted and linked using different methods for dissimilar road scenes. A hierarchical search strategy driven by saliency measurement is adopted in both linking processes. The blob primitives are linked using heuristic grouping and the line primitives are connected through genetic algorithm (GA) evolution. Finally, all of the linked road segments are normalized with centre-main lines and integrated into global smooth road curves through tensor voting. Experimental results show that the algorithm is capable of detecting multiform roads from real satellite images with high adaptability and reliability. Copyright Taylor & Francis Numéro de notice : A2007-456 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701250382 En ligne : https://doi.org/10.1080/01431160701250382 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28819 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-07111	RAB	Revue	Centre de documentation	En réserve L003	Disponible