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Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran / Hossein Shafizadeh-Moghadam in Computers, Environment and Urban Systems, vol 87 (May 2021)
[article]
Titre : Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran Type de document : Article/Communication Auteurs : Hossein Shafizadeh-Moghadam, Auteur ; Masoud Minaei, Auteur ; Robert Gilmore Pontius Jr, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101595 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] croissance urbaine
[Termes IGN] extrapolation
[Termes IGN] image Landsat
[Termes IGN] modèle de simulation
[Termes IGN] occupation du sol
[Termes IGN] Téhéran
[Termes IGN] utilisation du solRésumé : (auteur) This paper couples a Forward Feature Selection algorithm with Random Forest (FFS-RF) to create a transition index map, which then guides the spatial allocation for the extrapolation of urban growth using a Cellular Automata model. We used Landsat imagery to generate land cover maps at the years 1998, 2008, and 2018 for the Tehran-Karaj Region (TKR) in Iran. The FFS-RF considered the independent variables of slope, altitude, and distances from urban, crop, greenery, barren, and roads. The FFS-RF revealed temporal non-stationary of drivers from 1998–2008 to 2008–2018. The FFS-RF detected that altitude and distance from greenery were the most important drivers of urban growth during 1998–2008, then distances from crop and barren were the most important drivers during 2008–2018. We used the Total Operating Characteristic to evaluate the transition index maps. Validation during 2008–2018 showed that FFS-RF produced a transition index map that had predictive power no better than an allocation of urban growth near existing urban. Simulation to 2060 extrapolated that Tehran, Karaj, and their adjacent cities will interconnect spatially to form a gigantic city-region. Numéro de notice : A2021-274 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101595 Date de publication en ligne : 16/02/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101595 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97357
in Computers, Environment and Urban Systems > vol 87 (May 2021) . - n° 101595[article]Mapping and quantification of the dwarf eelgrass Zostera noltii using a random forest algorithm on a SPOT 7 satellite image / Salma Benmokhtar in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)
[article]
Titre : Mapping and quantification of the dwarf eelgrass Zostera noltii using a random forest algorithm on a SPOT 7 satellite image Type de document : Article/Communication Auteurs : Salma Benmokhtar, Auteur ; Marc Robin, Auteur ; Mohamed Maanan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 313 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse
[Termes IGN] cartographie hydrographique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] fond marin
[Termes IGN] herbier marin
[Termes IGN] image SPOT 7
[Termes IGN] Maroc
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] plante aquatique d'eau salée
[Termes IGN] réflectance spectrale
[Termes IGN] typologie
[Termes IGN] Zostera noltiiRésumé : (auteur) The dwarf eelgrass Zostera noltei Hornemann (Z. noltei) is the most dominant seagrass in semi-enclosed coastal systems of the Atlantic coast of Morocco. The species is experiencing a worldwide decline and monitoring the extent of its meadows would be a useful approach to estimate the impacts of natural and anthropogenic stressors. Here, we aimed to map the Z. noltei meadows in the Merja Zerga coastal lagoon (Atlantic coast of Morocco) using remote sensing. We used a random forest algorithm combined with field data to classify a SPOT 7 satellite image. Despite the difficulties related to the non-synchronization of the satellite images with the high tide coefficient, our results revealed, with an accuracy of 95%, that dwarf eelgrass beds can be discriminated successfully from other habitats in the lagoon. The estimated area was 160.76 ha when considering mixed beds (Z. noltei-associated macroalgae). The use of SPOT 7 satellite images seems to be satisfactory for long-term monitoring of Z. noltei meadows in the Merja Zerga lagoon and for biomass estimation using an NDVI–biomass quantitative relationship. Nevertheless, using this method of biomass estimation for dwarf eelgrass meadows could be unsuccessful when it comes to areas where the NDVI is saturated due to the stacking of many layers. Numéro de notice : A2021-393 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10050313 Date de publication en ligne : 07/05/2021 En ligne : https://doi.org/10.3390/ijgi10050313 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97679
in ISPRS International journal of geo-information > vol 10 n° 5 (May 2021) . - n° 313[article]The delineation of tea gardens from high resolution digital orthoimages using mean-shift and supervised machine learning methods / Akhtar Jamil in Geocarto international, vol 36 n° 7 ([15/04/2021])
[article]
Titre : The delineation of tea gardens from high resolution digital orthoimages using mean-shift and supervised machine learning methods Type de document : Article/Communication Auteurs : Akhtar Jamil, Auteur ; Bulent Bayram, Auteur Année de publication : 2021 Article en page(s) : pp 758 - 772 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de décalage moyen
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage automatique
[Termes IGN] arbre de décision
[Termes IGN] Camellia sinensis
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] exploitation agricole
[Termes IGN] extraction de la végétation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] orthoimage
[Termes IGN] segmentation hiérarchique
[Termes IGN] TurquieRésumé : (Auteur) Rize district is an important tea production site in Turkey, which is known for high quality tea. Determining the temporal changes is very crucial from the viewpoint of agricultural management and protection of tea areas. In addition, delineation of tea gardens using photogrammetric evaluation techniques for a single orthoimage takes approximately 8 h of labour work, which is both costly and time-consuming process. To overcome these issues, a method is proposed for demarcation of tea gardens from high-resolution orthoimages. In this article, a hierarchical object-based segmentation using mean-shift (MS) and supervised machine learning (ML) methods are investigated for delineation of tea gardens. First, the MS algorithm was applied to partition the images into homogeneous segments (objects) and then from each segment, various spectral, spatial and textural features were extracted. Finally, four most widely used supervised ML classifiers, support vector machine (SVM), artificial neural network (ANN), random forest (RF), and decision trees (DTs), were selected for classification of objects into tea gardens and other types of trees. Photogrammetrically evaluated tea garden borders were taken as reference data to evaluate the performance of the proposed methods. The experiments showed that all selected supervised classifiers were effective for delineation of the tea gardens from high-resolution images. Numéro de notice : A2021-293 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1622597 Date de publication en ligne : 19/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1622597 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97349
in Geocarto international > vol 36 n° 7 [15/04/2021] . - pp 758 - 772[article]Anti-cross validation technique for constructing and boosting random subspace neural network ensembles for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 6 ([01/04/2021])
[article]
Titre : Anti-cross validation technique for constructing and boosting random subspace neural network ensembles for hyperspectral image classification Type de document : Article/Communication Auteurs : Laxmi Narayana Eeti, Auteur ; Krishna Mohan Buddhiraju, Auteur Année de publication : 2021 Article en page(s) : pp 676 - 697 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données multisources
[Termes IGN] image hyperspectrale
[Termes IGN] jeu de données
[Termes IGN] précision de la classificationRésumé : (Auteur) Achieving high classification accuracy is vital in reliable information extraction from images. Single classifiers and existing ensemble methods suffer from data dimensionality, insufficient ground truth information and lack in defining optimal feature selection. This article presents a novel idea for constructing component classifiers that boost random subspace ensemble method in improving its classification performance. It is achieved through sub-optimal training of component classifiers through interference in training process during validation error evaluation. The new approach allows to enforce different class errors among component classifiers, besides improving individual class accuracy. This article demonstrates effectiveness of the anti-cross validation approach using three classical hyperspectral Image (HSI) datasets with significant improvement in classification accuracies from 3 to 10% with the proposed approach. Numéro de notice : A2021-292 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1618926 Date de publication en ligne : 03/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1618926 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97338
in Geocarto international > vol 36 n° 6 [01/04/2021] . - pp 676 - 697[article]A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery / Ting Bai in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)
[article]
Titre : A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Ting Bai, Auteur ; Kaimin Sun, Auteur ; Wenzhuo Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 249-262 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] changement d'occupation du sol
[Termes IGN] classe d'objets
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] détection de changement
[Termes IGN] détection du bâti
[Termes IGN] image à haute résolution
[Termes IGN] milieu urbain
[Termes IGN] segmentation multi-échelleRésumé : (Auteur) A single-scale object-based change-detection classifier can distinguish only global changes in land cover, not the more granular and local changes in urban areas. To overcome this issue, a novel class-specific object-based change-detection method is proposed. This method includes three steps: class-specific scale selection, class-specific classifier selection, and land cover change detection. The first step combines multi-resolution segmentation and a random forest to select the optimal scale for each change type in land cover. The second step links multi-scale hierarchical sampling with a classifier such as random forest, support vector machine, gradient-boosting decision tree, or Adaboost; the algorithm automatically selects the optimal classifier for each change type in land cover. The final step employs the optimal classifier to detect binary changes and from-to changes for each change type in land cover. To validate the proposed method, we applied it to two high-resolution data sets in urban areas and compared the change-detection results of our proposed method with that of principal component analysis k-means, object-based change vector analysis, and support vector machine. The experimental results show that our proposed method is more accurate than the other methods. The proposed method can address the high levels of complexity found in urban areas, although it requires historical land cover maps as auxiliary data. Numéro de notice : A2021-332 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.4.249 Date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.14358/PERS.87.4.249 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97528
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 4 (April 2021) . - pp 249-262[article]Réservation
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