Centre dedocumentation
scientifique

Tree cover mapping using hybrid fuzzy C-means method and multispectral satellite images / Linda Gulbe in Baltic forestry, vol 25 n° 1 (2019)  

Public [article]  inBaltic forestry > vol 25 n° 1 (2019) . - pp 113 - 123 Titre : Tree cover mapping using hybrid fuzzy C-means method and multispectral satellite images Type de document : Article/Communication Auteurs : Linda Gulbe, Auteur ; Aleksandrs Kozlovs, Auteur ; Janis Donis, Auteur ; Agris Tradkovs, Auteur Année de publication : 2019 Article en page(s) : pp 113 - 123 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] estimation statistique [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Landsat-ETM+ [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] Lettonie Résumé : (auteur) Countrywide up-to-date tree cover maps provide valuable information for planning and management purposes to investigate location of the resources and to identify afforestation and deforestation patterns. Landsat programme offers freely available satellite data with time span more than three decades and it can serve as bases for tree cover map calculation using satellite image classification; however, practical use of classification methods is limited due to lack of user-friendly solutions and complex interpretation of the results. The objective of this study is to evaluate user-friendly hybrid classification scheme for tree cover mapping in Latvia and to explore the nature of the spectral classes and consistency of the results when methodology is applied to images of different dates. Tree cover in this context means the area covered by crown of the tree, which may or may not be considered as forest according to local provisions. Tree cover is estimated using unsupervised fuzzy c-means methods with the stability check to ensure the presence of the same spectral classes in independent tests. Spectral classes are classified into two categories: tree cover and other by employing k-nearest neighbours. Such approach does not require high quality sample data and does not include user defined internal parameters of the algorithms (however, they can be specified if needed). The best overall accuracy achieved for year 2014 was 94.2% with producer's accuracy 98.7% (tree cover), 90.5% (other land cover), user's accuracy 90.0% (tree cover), 98.8% (other land cover) and kappa 0.89. Consistency studies showed high impact (within 10% of overall accuracy) of unique conditions during the image acquisition. Some of the spectral classes represent borderline case between relatively dense tree cover and other land cover types like sparse young stands. Those cases are the main threat to the consistency between the results of different dates and seasons. Numéro de notice : A2019-375 Thématique : FORET Nature : Article DOI : sans En ligne : https://www.balticforestry.mi.lt/bf/PDF_Articles/2019-25%5B1%5D/Baltic%20Forestr [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93417 [article]

Airborne Lidar/INS/GNSS : algorithm uses fuzzy controlled Scale Invariant Feature Transform (SIFT) / Haowei Xu in GPS world, vol 28 n° 3 (March 2017)

Public [article]  inGPS world > vol 28 n° 3 (March 2017) . - pp 26 - 32 Titre : Airborne Lidar/INS/GNSS : algorithm uses fuzzy controlled Scale Invariant Feature Transform (SIFT) Type de document : Article/Communication Auteurs : Haowei Xu, Auteur ; Lian Baowang, Auteur ; Charles K. Toth, Auteur ; Dorota Brzezinska, Auteur Année de publication : 2017 Article en page(s) : pp 26 - 32 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] GNSS-INS [Termes descripteurs IGN] SIFT (algorithme) Résumé : (Auteur) Lidar with its superior performance can replace GNSS in the integration solution by providing fixes for the drifting inertial measurement unit (IMU). Tests show its potential for terrain-referenced navigation due to its high accuracy, resolution, update rate and anti-jamming abilities. A novel algorithm uses scanning lidar ranging data and a reference database to calculate the navigation solution of the platform and then further fuse with the inertial navigation system (INS) output data. Numéro de notice : A2017-290 Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85325 [article]

The D-FCM partitioned D-BSP tree for massive point cloud data access and rendering / Yi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 120 (october 2016) . - pp 25 - 36 Titre : The D-FCM partitioned D-BSP tree for massive point cloud data access and rendering Type de document : Article/Communication Auteurs : Yi Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 25 - 36 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] arbre BSP [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] densité des points [Termes descripteurs IGN] partitionnement binaire [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] traitement de nuage de points [Termes descripteurs IGN] transformation de Karhunen-Loeve [Termes descripteurs IGN] valeur propre Résumé : (Auteur) The spatial partitioning of massive point cloud data involves dividing the space into a multi-tree structure step by step, so as to achieve the purpose of fast access and to render the point cloud. The current methods are based on spatial regularity and equal division, which is not consistent with the irregular and non-uniform distribution of most point clouds. This paper presents a directional fuzzy c-means (D-FCM) method for irregular spatial partitioning. The distance metric is weighted by a direction coefficient, which is determined by the eigenvalue of the point cloud. The orientation of each node is adaptively calculated by principal component analysis of the point cloud, and Karhunen-Loeve (KL) transform is applied to the points coordinates in node. A binary space partitioning (BSP) tree structure is used to partition the point cloud data node by node, and a directional BSP (D-BSP) tree is formed. The D-BSP tree structure was tested with point clouds of 0.1 million to over 2 billion points (up to 60 GB). The experimental results showed that the D-BSP tree can ensure that the bounding boxes are close to the actual spatial distribution of the point cloud, it can completely expand along the spatial configuration of the point cloud without generating unnecessary partitioning, and it can achieve a higher rendering speed with less memory requirement. Numéro de notice : A2016-795 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.08.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82529 [article]

Geographically weighted evidence combination approaches for combining discordant and inconsistent volunteered geographical information / Alexis Comber in Geoinformatica [en ligne], vol 20 n° 3 (July - September 2016)  

Public [article]  inGeoinformatica [en ligne] > vol 20 n° 3 (July - September 2016) . - pp 503 – 527 Titre : Geographically weighted evidence combination approaches for combining discordant and inconsistent volunteered geographical information Type de document : Article/Communication Auteurs : Alexis Comber, Auteur ; Cidália Costa Fonte, Auteur ; Giles M. Foody, Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 503 – 527 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse combinatoire (maths) [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] classification de Dempster-Shafer [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données multisources [Termes descripteurs IGN] incertitude des données [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] pondération [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] régression géographiquement pondérée [Termes descripteurs IGN] Wikimapia Résumé : (auteur) There is much interest in being able to combine crowdsourced data. One of the critical issues in information sciences is how to combine data or information that are discordant or inconsistent in some way. Many previous approaches have taken a majority rules approach under the assumption that most people are correct most of the time. This paper analyses crowdsourced land cover data generated by the Geo-Wiki initiative in order to infer the land cover present at locations on a 50 km grid. It compares four evidence combination approaches (Dempster-Shafer, Bayes, Fuzzy Sets and Possibility) applied under a geographically weighted kernel with the geographically weighted average approach applied in many current Geo-Wiki analyses. A geographically weighted approach uses a moving kernel under which local analyses are undertaken. The contribution (or salience) of each data point to the analysis is weighted by its distance to the kernel centre, reflecting Tobler’s 1st law of geography. A series of analyses were undertaken using different kernel sizes (or bandwidths). Each of the geographically weighted evidence combination methods generated spatially distributed measures of belief in hypotheses associated with the presence of individual land cover classes at each location on the grid. These were compared with GlobCover, a global land cover product. The results from the geographically weighted average approach in general had higher correspondence with the reference data and this increased with bandwidth. However, for some classes other evidence combination approaches had higher correspondences possibly because of greater ambiguity over class conceptualisations and / or lower densities of crowdsourced data. The outputs also allowed the beliefs in each class to be mapped. The differences in the soft and the crisp maps are clearly associated with the logics of each evidence combination approach and of course the different questions that they ask of the data. The results show that discordant data can be combined (rather than being removed from analysis) and that data integrated in this way can be parameterised by different measures of belief uncertainty. The discussion highlights a number of critical areas for future research. Numéro de notice : A2016-379 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-016-0248-z date de publication en ligne : 27/02/2016 En ligne : http://dx.doi.org/ 10.1007/s10707-016-0248-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81146 [article]

An iterative haze optimized transformation for automatic cloud/haze detection of landsat imagery / Shuli Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 5 (May 2016) . - pp 2682 - 2694 Titre : An iterative haze optimized transformation for automatic cloud/haze detection of landsat imagery Type de document : Article/Communication Auteurs : Shuli Chen, Auteur ; Xuehong Chen, Auteur ; Jin Chen, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 2682 - 2694 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] détection de flou [Termes descripteurs IGN] télédétection Résumé : (Auteur) Most previous haze/cloud detection methods for Landsat imagery, e.g., haze optimized transformation (HOT), cannot adequately suppress land surface information and, in particular, often overestimate haze thickness over bright surfaces. This paper proposes an iterative HOT (IHOT) for improving haze detection with the help of a corresponding clear image. With an iterative procedure of regressions among HOT, the reflectance difference at the top of atmosphere (TOA) between hazy and clear images, and TOA reflectances of hazy and clear images, the land surface information can be removed, and the iterative HOT (IHOT) result is derived to spatially characterize the haze contamination in the Landsat images. A group of Landsat images that were acquired in different landscapes and seasons were used to test IHOT. Visual comparisons indicate that IHOT performed better than previous haze detection methods for images that were acquired in diverse landscapes and also performed robustly for hazy images that were acquired at different seasons when using the same reference clear image. Additionally, two indirect quantitative validations were used to illustrate that IHOT can provide the best transformation for accurately determining haze information. Therefore, it is expected that the proposed IHOT method will be used for automatic cloud/haze detection for large numbers of Landsat images if data sets of clear Landsat imagery are available. Numéro de notice : A2016-845 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2015.2504369 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82926 [article]

Application of fuzzy combination operators to flood vulnerability assessments in Seoul, Korea / Moung-Jin Lee in Geocarto international, vol 30 n° 9 - 10 (October - November 2015)  

Permalink

Classification of remotely sensed images using the geneSIS fuzzy segmentation algorithm / Stelios Mylonas in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)

Permalink

An intelligent spatial proximity system using neurofuzzy classifiers and contextual information / F. Barouni in Geomatica [en ligne], vol 69 n° 3 (september 2015)  

Permalink

Panorama sur les méthodes de classification des images satellites et techniques d'amélioration de la précision de la classification / O. El Kharki in Revue Française de Photogrammétrie et de Télédétection, n° 210 (Avril 2015)

Permalink

Road hierarchy with integration of attributes using fuzzy-AHP / Fatih Gülgen in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)  

Permalink

Development of fuzzy rule-based parameters for urban object-oriented classification using very high resolution imagery / Alireza Hamedianfar in Geocarto international, vol 29 n° 3 - 4 (June - July 2014)

Permalink

Significance analysis of different types of ancillary geodata utilized in a multisource classification process for forest identification in Germany / Michael Förster in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)

Permalink

Connaissance de la biodiversité végétale / Jan-Bernard Bouzillé (2014)

Permalink

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)  

Permalink

A semi-ellipsoid-model based fuzzy classifier to map grassland in Inner Mongolia, China / Hai Lan in ISPRS Journal of photogrammetry and remote sensing, vol 85 (November 2013)

Permalink