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On comparing multifractal and classical features in minimum distance classification of AVHRR imagery / T. Parrinello in International Journal of Remote Sensing IJRS, vol 27 n°18 - 19 - 20 (October 2006)
[article]
Titre : On comparing multifractal and classical features in minimum distance classification of AVHRR imagery Type de document : Article/Communication Auteurs : T. Parrinello, Auteur ; R.A. Vaughan, Auteur Année de publication : 2006 Article en page(s) : pp 3943 - 3959 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification barycentrique
[Termes IGN] classification dirigée
[Termes IGN] Ecosse
[Termes IGN] géométrie fractale
[Termes IGN] image NOAA-AVHRR
[Termes IGN] texture d'imageRésumé : (Auteur) The ability to distinguish between different types of surfaces is the strength of texture descriptors in the analysis of satellite imagery. Although the most common analytical means are based on co-occurrence analysis, considerable progress has been made in understanding the role of fractal and multifractal analysis in remote sensing. After indicating the limitations of using fractal dimensions as the only texture descriptor and introducing the concept of multifractal geometry, we consider the effectiveness of using multifractal and second-order fractal features in image classification. In particular, we present the results of comparing two supervised classifications of an Advanced Very High Resolution Radiometer (AVHRR) image of Scotland using classical texture features and multifractal second-order fractal ones. In terms of percentage correct and Khat statistics, this study provides evidence, with a confidence limit of 95%, that classifications using multifractal and second-order fractal features are more accurate than those using classical features. The classification algorithm used for this study is a typical minimum distance classifier. Copyright Taylor & Francis Numéro de notice : A2006-458 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600685241 En ligne : https://doi.org/10.1080/01431160600685241 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28182
in International Journal of Remote Sensing IJRS > vol 27 n°18 - 19 - 20 (October 2006) . - pp 3943 - 3959[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-06101 RAB Revue Centre de documentation En réserve L003 Disponible A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery / L. Zhang in IEEE Transactions on geoscience and remote sensing, vol 44 n° 10 Tome 2 (October 2006)
[article]
Titre : A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery Type de document : Article/Communication Auteurs : L. Zhang, Auteur ; X. Huang, Auteur Année de publication : 2006 Article en page(s) : pp 2950 - 2961 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes indépendantes
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] fusion de données
[Termes IGN] image à haute résolution
[Termes IGN] image multibande
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)
[Termes IGN] pixel
[Termes IGN] précision de la classification
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] reconnaissance de formes
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Shape and spectra are both important features of high spatial resolution remotely sensed (HSRRS) imagery, and they are concrete manifestation of textures on such imagery. This paper presents a spatial feature index, pixel shape index (PSI), to describe the shape feature in a local area surrounding a pixel. PSI is a pixel-based feature which measures the gray similarity distance in every direction. As merely the shape feature is inadequate for classifying HSRRS imagery, a transformed spectral feature extracted by independent component analysis is added to the input vectors of our classifier, and this replaces the original multispectral bands. Meanwhile, a fast fusion algorithm that integrates both shape and spectral features using the support vector machine has been developed to interpret the complex input vectors. The results by PSI are compared with some spatial features extracted using wavelet transform, gray level co-occurrence matrix, and the length–width extraction algorithm to test its effectiveness. The experiments demonstrate that PSI is capable of describing shape features effectively and result in more accurate classifications than other methods. While it is found that spectral and shape features can complement each other and their integration can improve classification accuracy, the transformed spectral components are also found to be more suitable for classification. Copyright IEEE Numéro de notice : A2006-504 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.876704 En ligne : https://doi.org/10.1109/TGRS.2006.876704 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28228
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 10 Tome 2 (October 2006) . - pp 2950 - 2961[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06101B RAB Revue Centre de documentation En réserve L003 Disponible Training set size requirements for the classification of a specific class / Giles M. Foody in Remote sensing of environment, vol 104 n° 1 (15/09/2006)
[article]
Titre : Training set size requirements for the classification of a specific class Type de document : Article/Communication Auteurs : Giles M. Foody, Auteur ; A. Mathur, Auteur ; et al., Auteur Année de publication : 2006 Article en page(s) : pp 1 - 14 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Gossypium (genre)
[Termes IGN] Inde
[Termes IGN] intelligence artificielle
[Termes IGN] réduction géométriqueRésumé : (Auteur) The design of the training stage of a supervised classification should account for the properties of the classifier to be used. Consideration of the way the classifier operates may enable the training stage to be designed in a manner which ensures that the aim of the classification is satisfied with the use of a small, inexpensive, training set. It may, therefore, be possible to reduce the training set size requirements from that generally expected with the use of standard heuristics. Substantial reductions in training set size may be possible if interest is focused on a single class. This is illustrated for mapping cotton in north-western India by support vector machine type classifiers. Four approaches to reducing training set size were used: intelligent selection of the most informative training samples, selective class exclusion, acceptance of imprecise descriptions for spectrally distinct classes and the adoption of a one-class classifier. All four approaches were able to reduce the training set size required considerably below that suggested by conventional widely used heuristics without significant impact on the accuracy with which the class of interest was classified. For example, reductions in training set size of not, vert, similar 90% from that suggested by a conventional heuristic are reported with the accuracy of cotton classification remaining nearly constant at not, vert, similar 95% and not, vert, similar 97% from the user's and producer's perspectives respectively. Copyright Elsevier Numéro de notice : A2006-392 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.03.004 En ligne : https://doi.org/10.1016/j.rse.2006.03.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28116
in Remote sensing of environment > vol 104 n° 1 (15/09/2006) . - pp 1 - 14[article]Comparison of computational intelligence based classification techniques for remotely sensed optical image classification / D. Stathakis in IEEE Transactions on geoscience and remote sensing, vol 44 n° 8 (August 2006)
[article]
Titre : Comparison of computational intelligence based classification techniques for remotely sensed optical image classification Type de document : Article/Communication Auteurs : D. Stathakis, Auteur ; A. Vasilakos, Auteur Année de publication : 2006 Article en page(s) : pp 2305 - 2318 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] analyse comparative
[Termes IGN] classification dirigée
[Termes IGN] classification floue
[Termes IGN] classification par algorithme génétique
[Termes IGN] classification par réseau neuronal
[Termes IGN] image optique
[Termes IGN] occupation du solRésumé : (Auteur) Several computational intelligence components, namely neural networks (NNs), fuzzy sets, and genetic algorithms (GAs), have been applied separately or in combination to the process of remotely sensed data classification. By applying computational intelligence, we expect increased accuracy through the use of NNs, optimal NN structure and parameter determination via GAs, and transparency using fuzzy sets is expected. This paper systematically reviews and compares several configurations in the particular context of remote sensing for land cover. In addition, some of the configurations used here, such as NEFCASS and CANFIS, have few previous applications in the field. A comparison of the configurations is achieved by testing the different methods with exactly the same case-study data. A thorough assessment of results is performed by constructing an accuracy matrix for each training and testing data set. The evaluation of different methods is not only based on accuracy but also on compactness, completeness, and consistency. The architecture, produced rule set, and training parameters for the specific classification task are presented. Some comments and directions for future work are given. Copyright IEEE Numéro de notice : A2006-397 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.872903 En ligne : https://doi.org/10.1109/TGRS.2006.872903 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28121
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 8 (August 2006) . - pp 2305 - 2318[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06081 RAB Revue Centre de documentation En réserve L003 Disponible Error assessment in two lidar-derived TIN datasets / M.H. Peng in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 8 (August 2006)
[article]
Titre : Error assessment in two lidar-derived TIN datasets Type de document : Article/Communication Auteurs : M.H. Peng, Auteur ; T.Y. Shih, Auteur Année de publication : 2006 Article en page(s) : pp 933 - 947 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] canopée
[Termes IGN] classification dirigée
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] erreur de mesure
[Termes IGN] estimation statistique
[Termes IGN] modèle numérique de surface
[Termes IGN] occupation du sol
[Termes IGN] pente
[Termes IGN] point de vérification
[Termes IGN] précision des données
[Termes IGN] rugosité
[Termes IGN] rugosité du sol
[Termes IGN] semis de points
[Termes IGN] Triangulated Irregular Network
[Termes IGN] variabilité
[Termes IGN] végétationRésumé : (Auteur) An accuracy assessment of two lidar-derived elevation datasets was conducted in areas of rugged terrain (average slope 26.6°). Data from 906 ground checkpoints in various land-cover types were collected in situ as reference points. Analysis of the accuracy of lidar-derived elevation as a function of several factors including terrain slope, terrain aspect, and land-cover types was conducted. This paper attempts to characterize vegetation information derived from lidar data based on variables such as canopy volume, local roughness of point clouds, point spacing of lidar ground returns, and vegetation angle. This information was used to evaluate the accuracy of elevation as a function of vegetation type. The experimental results revealed that the accuracy of elevation was considerably correlated with five factors: terrain slope, vegetation angle, canopy volume, local roughness of point clouds, and point spacing of lidar ground returns. The results show a linear relationship between the elevation accuracy and the combination of vegetation angle and the point spacing of ground returns (r2 > 0.9). The combination of vegetation angle and point spacing of ground returns explains a significant amount of the variability in elevation accuracy. Elevation accuracy varied with different vegetation types. The elevation accuracy was also linearly correlated with the product of the point spacing of ground returns and the tangent of the slope (r2 = 0.9). A greater product value implies a greater elevation error. In addition, with regard to terrain aspect, one dense dataset with extra cross-flight data revealed a lesser impact of aspect on elevation accuracy. Copyright ASPRS Numéro de notice : A2006-312 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.72.8.933 En ligne : https://doi.org/10.14358/PERS.72.8.933 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28036
in Photogrammetric Engineering & Remote Sensing, PERS > vol 72 n° 8 (August 2006) . - pp 933 - 947[article]A patch-based image classification by integrating hyperspectral data with GIS / B. Zhang in International Journal of Remote Sensing IJRS, vol 27 n°15-16 (August 2006)PermalinkSome issues in the classification of DAIS hyperspectral data / M. Pal in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)PermalinkStudy of tectonics in relation to the seismic activity of the Davalt area, Nasik district, Maharashtra, India using remote sensing and GIS techniques / J. Sarup in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)PermalinkUrban land-use classification using variogram-based analysis with an aerial photograph / S.S. Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 7 (July 2006)PermalinkApport de la classification combinée supervisée et non supervisée d'une image Landsat ETM+ à la cartographie géologique de la boutonnière de Kerdous, anti-atlas, Maroc / M. Hakdaoui in Photo interprétation, vol 42 n° 2 (Juin 2006)PermalinkHigh spatial resolution satellite imagery, DEM derivatives, and image segmentation for the detection of mass wasting processes / J. Barlow in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 6 (June 2006)PermalinkMapping built-up areas from multitemporal interferometric SAR images: a segment-based approach / Leena Matikainen in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 6 (June 2006)PermalinkAutomatic building detection using the Dempster-Shafer algorithm / Y.H. Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 4 (April 2006)PermalinkConsideration of smoothing techniques for hyperspectral remote sensing / C. Vaiphasa in ISPRS Journal of photogrammetry and remote sensing, vol 60 n° 2 (April 2006)PermalinkRelevance of hyperspectral data for natural resources management / T.V. Ramachandra in GIS development, vol 10 n° 4 (April 2006)Permalink