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Superpixel-based regional-scale grassland community classification using genetic programming with Sentinel-1 SAR and Sentinel-2 multispectral images / Zhenjiang Wu in Remote sensing, vol 13 n° 20 (October-2 2021)
[article]
Titre : Superpixel-based regional-scale grassland community classification using genetic programming with Sentinel-1 SAR and Sentinel-2 multispectral images Type de document : Article/Communication Auteurs : Zhenjiang Wu, Auteur ; Jiahua Zhang, Auteur ; Fan Deng, Auteur Année de publication : 2021 Article en page(s) : n° 4067 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] Chine
[Termes IGN] classification par algorithme génétique
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice de végétation
[Termes IGN] optimisation (mathématiques)
[Termes IGN] prairie
[Termes IGN] précision de la classification
[Termes IGN] superpixel
[Termes IGN] texture d'imageRésumé : (auteur) Grasslands are one of the most important terrestrial ecosystems on the planet and have significant economic and ecological value. Accurate and rapid discrimination of grassland communities is critical to the conservation and utilization of grassland resources. Previous studies that explored grassland communities were mainly based on field surveys or airborne hyperspectral and high-resolution imagery. Limited by workload and cost, these methods are typically suitable for small areas. Spaceborne mid-resolution RS images (e.g., Sentinel, Landsat) have been widely used for large-scale vegetation observations owing to their large swath width. However, there still keep challenges in accurately distinguishing between different grassland communities using these images because of the strong spectral similarity of different communities and the suboptimal performance of models used for classification. To address this issue, this paper proposed a superpixel-based grassland community classification method using Genetic Programming (GP)-optimized classification model with Sentinel-2 multispectral bands, their derived vegetation indices (VIs) and textural features, and Sentinel-1 Synthetic Aperture Radar (SAR) bands and the derived textural features. The proposed method was evaluated in the Siziwang grassland of China. Our results showed that the addition of VIs and textures, as well as the use of GP-optimized classification models, can significantly contribute to distinguishing grassland communities, and the proposed approach classified the seven communities in Siziwang grassland with an overall accuracy of 84.21% and a kappa coefficient of 0.81. We concluded that the classification method proposed in this paper is capable of distinguishing grassland communities with high accuracy at a regional scale. Numéro de notice : A2021-805 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13204067 Date de publication en ligne : 12/10/2021 En ligne : https://doi.org/10.3390/rs13204067 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98862
in Remote sensing > vol 13 n° 20 (October-2 2021) . - n° 4067[article]Two hidden layer neural network-based rotation forest ensemble for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 16 ([01/09/2021])
[article]
Titre : Two hidden layer neural network-based rotation forest ensemble 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 1820 - 1837 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] arbre de décision
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] ensachage
[Termes IGN] image AVIRIS
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] image ROSIS
[Termes IGN] Perceptron multicouche
[Termes IGN] précision de la classification
[Termes IGN] réseau neuronal profond
[Termes IGN] Rotation Forest classificationRésumé : (auteur) Decision tree-based Rotation Forest could generate satisfactory but lower classification accuracy for a given training sample set and image data, owing to the inherent disadvantages in decision trees, namely myopic, replication and fragmentation problem. To improve performance of Rotation Forest technique, we propose to utilize two-hidden-layered-feedforward neural network as base classifier instead of decision tree. We examine the classification performance of proposed model under two situations, namely when free network parameters are maintained the same across all ensemble components and otherwise. The proposed model, where each component is initialized with different pair of initial weights and bias, performs better than decision tree-based Rotation Forest on three different Hyperspectral sensor datasets – AVIRIS, ROSIS and Hyperion. Improvements in classification accuracy are above 2% and up to 3% depending upon dataset. Also, the proposed model achieves improvement in accuracy over Random Forest in the range 4.2–8.8%. Numéro de notice : A2021-581 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1678680 Date de publication en ligne : 21/10/2019 En ligne : https://doi.org/10.1080/10106049.2019.1678680 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98193
in Geocarto international > vol 36 n° 16 [01/09/2021] . - pp 1820 - 1837[article]Efficient image dataset classification difficulty estimation for predicting deep-learning accuracy / Florian Scheidegger in The Visual Computer, vol 37 n° 6 (June 2021)
[article]
Titre : Efficient image dataset classification difficulty estimation for predicting deep-learning accuracy Type de document : Article/Communication Auteurs : Florian Scheidegger, Auteur ; Roxana Istrate, Auteur ; Giovanni Mariani, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1593 - 1610 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] architecture de réseau
[Termes IGN] classification par nuées dynamiques
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] distance de Fréchet
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] jeu de données
[Termes IGN] précision de la classification
[Termes IGN] processeur graphiqueRésumé : (auteur) In the deep-learning community, new algorithms are published at a very fast pace. Therefore, solving an image classification problem for new datasets becomes a challenging task, as it requires to re-evaluate published algorithms and their different configurations in order to find a close to optimal classifier. To facilitate this process, before biasing our decision toward a class of neural networks or running an expensive search over the network space, we propose to estimate the classification difficulty of the dataset. Our method computes a single number that characterizes the dataset difficulty 97× faster than training state-of-the-art networks. The proposed method can be used in combination with network topology and hyper-parameter search optimizers to efficiently drive the search toward promising neural network configurations. Numéro de notice : A2021-533 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01922-5 Date de publication en ligne : 28/07/2020 En ligne : https://doi.org/10.1007/s00371-020-01922-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97993
in The Visual Computer > vol 37 n° 6 (June 2021) . - pp 1593 - 1610[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]Parsing of urban facades from 3D point clouds based on a novel multi-view domain / Wei Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)
[article]
Titre : Parsing of urban facades from 3D point clouds based on a novel multi-view domain Type de document : Article/Communication Auteurs : Wei Wang, Auteur ; Yuan Xu, Auteur ; Yingchao Ren, Auteur ; Gang Wang, Auteur Année de publication : 2021 Article en page(s) : pp 283-293 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] données localisées 3D
[Termes IGN] façade
[Termes IGN] fusion de données
[Termes IGN] milieu urbain
[Termes IGN] précision de la classification
[Termes IGN] segmentation hiérarchique
[Termes IGN] segmentation multi-échelle
[Termes IGN] semis de pointsRésumé : (Auteur) Recently, performance improvement in facade parsing from 3D point clouds has been brought about by designing more complex network structures, which cost huge computing resources and do not take full advantage of prior knowledge of facade structure. Instead, from the perspective of data distribution, we construct a new hierarchical mesh multi-view data domain based on the characteristics of facade objects to achieve fusion of deep-learning models and prior knowledge, thereby significantly improving segmentation accuracy. We comprehensively evaluate the current mainstream method on the RueMonge 2014 data set and demonstrate the superiority of our method. The mean intersection-over-union index on the facade-parsing task reached 76.41%, which is 2.75% higher than the current best result. In addition, through comparative experiments, the reasons for the performance improvement of the proposed method are further analyzed. Numéro de notice : A2021-333 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.4.283 Date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.14358/PERS.87.4.283 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97531
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 4 (April 2021) . - pp 283-293[article]Exemplaires(1)
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