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Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective / Mohammad D. Hossain in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 150 (April 2019) . - pp 115 - 134 Titre : Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective Type de document : Article/Communication Auteurs : Mohammad D. Hossain, Auteur ; Dongmei Chen, Auteur Année de publication : 2019 Article en page(s) : pp 115 - 134 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] appariement de données localisées [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification hybride [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] objet géographique [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] segmentation par croissance de régions [Termes descripteurs IGN] segmentation par décomposition-fusion Résumé : (Auteur) Image segmentation is a critical and important step in (GEographic) Object-Based Image Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly dependent on the quality of image segmentation. Segmentation has been used in remote sensing image processing since the advent of the Landsat-1 satellite. However, after the launch of the high-resolution IKONOS satellite in 1999, the paradigm of image analysis moved from pixel-based to object-based. As a result, the purpose of segmentation has been changed from helping pixel labeling to object identification. Although several articles have reviewed segmentation algorithms, it is unclear if some segmentation algorithms are generally more suited for (GE)OBIA than others. This article has conducted an extensive state-of-the-art survey on OBIA techniques, discussed different segmentation techniques and their applicability to OBIA. Conceptual details of those techniques are explained along with the strengths and weaknesses. The available tools and software packages for segmentation are also summarized. The key challenge in image segmentation is to select optimal parameters and algorithms that can general image objects matching with the meaningful geographic objects. Recent research indicates an apparent movement towards the improvement of segmentation algorithms, aiming at more accurate, automated, and computationally efficient techniques. Numéro de notice : A2019-138 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.02.009 date de publication en ligne : 23/02/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.02.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92469 [article]

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Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping / Mirco Sturari in European journal of remote sensing, vol 50 n° 1 (2017)  

Public [article]  inEuropean journal of remote sensing > vol 50 n° 1 (2017) . - pp 1 - 17 Titre : Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping Type de document : Article/Communication Auteurs : Mirco Sturari, Auteur ; Emanuele Frontoni, Auteur ; Roberto Pierdicca, Auteur ; Adriano Mancini, Auteur ; Eva Savina Malinverni, Auteur ; Anna Nora Tassetti, Auteur ; Primo Zingaretti, Auteur Année de publication : 2017 Article en page(s) : pp 1 - 17 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] base de données d'occupation du sol [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification hybride [Termes descripteurs IGN] données altimétriques [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] intégration de données [Termes descripteurs IGN] occupation du sol Résumé : (Auteur) The combination of elevation data together with multispectral high-resolution images is a new methodology for obtaining land use/land cover classification. It represents a step forward for both the accuracy and automation of LULC applications and allows users to setup thematic assignments through rules based on feature attributes and human expert interpretation of land usage. The synergy between different types of information means that LiDAR can give new hints at both the segmentation and hybrid classification steps, leading to a joint use of multispectral, spatial and elevation data. The output is a thematic map characterized by a custom-designed legend that is able to discriminate between land cover classes with similar spectral characteristics (level 3 of the CLC legend). Experimental results from a hilly farmland area with some urban structures (Musone river basin, Ancona, Italy) are used to highlight how the proposed methodology enhances land cover classification in heterogeneous environments. Numéro de notice : A2017-043 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article En ligne : http://doi.org/10.1080/22797254.2017.1274572 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84213 [article]

A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images / Anastasios L. Fytsilis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 165- 186 Titre : A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images Type de document : Article/Communication Auteurs : Anastasios L. Fytsilis, Auteur ; Anthony Prokos, Auteur ; Konstantinos D. Koutroumbas, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 165- 186 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] classification hybride [Termes descripteurs IGN] drone [Termes descripteurs IGN] gradient [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] méthodologie [Termes descripteurs IGN] orthorectification automatique [Termes descripteurs IGN] recalage d'image Résumé : (Auteur) In this paper a novel integrated hybrid methodology for unsupervised change detection between Unmanned Aerial Vehicle (UAV) and satellite images, which can be utilized in various fields like security applications (e.g. border surveillance) and damage assessment, is proposed. This is a challenging problem mainly due to the difference in geographic coverage and the spatial resolution of the two images, as well as to the acquisition modes which lead to misregistration errors. The methodology consists of the following steps: (a) pre-processing, where the part of the satellite image that corresponds to the UAV image is determined and the UAV image is ortho-rectified using information provided by a Digital Terrain Model, (b) the detection of potential changes, which is based exclusively on intensity and image gradient information, (c) the generation of the region map, where homogeneous regions are produced by the previous potential changes via a seeded region growing algorithm and placed on the region map, and (d) the evaluation of the above regions, in order to characterize them as true changes or not. The methodology has been applied on demanding real datasets with very encouraging results. Finally, its robustness to the misregistration errors is assessed via extensive experimentation. Numéro de notice : A2016-782 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.06.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82479 [article]

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)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 85 (November 2013) . - pp 21 - 31 Titre : A semi-ellipsoid-model based fuzzy classifier to map grassland in Inner Mongolia, China Type de document : Article/Communication Auteurs : Hai Lan, Auteur ; Yichun Xie, Auteur Année de publication : 2013 Article en page(s) : pp 21 - 31 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] classification hybride [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image CBERS [Termes descripteurs IGN] image Landsat-ETM+ [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] Mongolie intérieure (Chine) [Termes descripteurs IGN] prairie Résumé : (Auteur) Remote sensing techniques offer effective means for mapping plant communities. However, mapping grassland with fine vegetative classes over large areas has been challenging for either the coarse resolutions of remotely sensed images or the high costs of acquiring images with high-resolutions. An improved hybrid-fuzzy-classifier (HFC) derived from a semi-ellipsoid-model (SEM) is developed in this paper to achieve higher accuracy for classifying grasslands with Landsat images. The Xilin River Basin, Inner Mongolia, China, is chosen as the study area, because an acceptable volume of ground truthing data was previously collected by multiple research communities. The accuracy assessment is based on the comparison of the classification outcomes from four types of image sets: (1) Landsat ETM+ August 14, 2004, (2) Landsat TM August 12, 2009, (3) the fused images of ETM+ with CBERS, and (4) TM with CBERS, respectively, and by three classifiers, the proposed HFC-SEM, the tetragonal pyramid model (TPM) based HFC, and the support vector machine method. In all twelve classification experiments, the HFC-SEM classifier had the best overall accuracy statistics. This finding indicates that the medium resolution Landsat images can be used to map grassland vegetation with good vegetative detail when the proper classifier is applied. Numéro de notice : A2013-605 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32741 [article]

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A hybrid classification matching method for geospatial services / Yandong Wang in Transactions in GIS, vol 16 n° 6 (December 2012)  

Public [article]  inTransactions in GIS > vol 16 n° 6 (December 2012) . - pp 781 - 805 Titre : A hybrid classification matching method for geospatial services Type de document : Article/Communication Auteurs : Yandong Wang, Auteur ; Hao Li, Auteur ; An Luo, Auteur Année de publication : 2012 Article en page(s) : pp 781 - 805 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] classification hybride [Termes descripteurs IGN] service web géographique [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] taxinomie Résumé : (Auteur) With the development of the Internet and GIS, large volumes of spatial data and powerful computing resources are increasingly published in the form of Web services. Given the variety and number of geospatial services advertised online, finding appropriate geospatial services has become a tremendous challenge for potential users. Geospatial service classification provides a basis for developing matching criteria to improve the efficiency of service discovery. At present, most classification-based matching methods require users to provide classification descriptions using a specified taxonomy. These requirements seriously limit the application of classification-based matching. To solve these kinds of problems, this article presents a hybrid geospatial service classification-matching method. Based on the differences in classification descriptions, three strategies are proposed: (1) the existing classification matching method is used for requests with classifications described using homogeneous taxonomies; (2) a formal-concept-analysis-based service classification matching method is proposed for service requests with classifications described using heterogeneous taxonomies; and (3) an interface-similarity-based service classification decision method is proposed for requests without classification descriptions. The feasibility of the hybrid geospatial service classification matching method is verified by two sets of experiments. The results reveal that this method can effectively broaden the application of classification-based matching in geospatial service discovery. Numéro de notice : A2012-616 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2012.01348.x En ligne : https://doi.org/10.1111/j.1467-9671.2012.01348.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32062 [article]

A supervised and fuzzy-based approach determine optimal multi-resolution image segmentation parameters / H. Tong in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 10 (October 2012)  

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A hybrid classification scheme for mining multisource geospatial data / R. Vatsavai in Geoinformatica, vol 15 n° 1 (January 2011)  

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Application de la classification floue (fuzzy k-NN) à l'étude de l'occupation du sol d'une zone urbaine : le cas de la région de Genève / S. Rakotoniaina in Photo interpretation, European journal of applied remote sensing, vol 46 n° 2 (juin 2010)

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An adaptive thresholding multiple classifiers system for remote sensing image classification / Y. Tzeng in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 6 (June 2009)

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Feature reduction using a singular value decomposition for the iterative guided spectral class rejection hybrid classifier / R. Philipps in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 1 (January - February 2009)

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Neuro-fuzzy based analysis of hyperspectral imagery / F. Qiu in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)

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Apport de deux méthodes de suivi d'évolution de la zone urbaine par imagerie / R. Bouchiha in Revue Française de Photogrammétrie et de Télédétection, n° 190 (Septembre 2008)

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Fuzzy classification: a case study using Landsat TM images in Iran / A.M. Lak in GIM international, vol 20 n° 7 (July 2006)

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Apport 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)

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Detection of land use/land cover changes for the northern part of the Nile delta (Burullus region), Egypt / Kh. M. Dewidar in International Journal of Remote Sensing IJRS, vol 25 n° 20 (October 2004)

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