IEEE Transactions on geoscience and remote sensing / IEEE Geoscience and remote sensing society (Etats-Unis) . vol 59 n° 12Paru le : 01/12/2021 |
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Ajouter le résultat dans votre panierDiResNet: Direction-aware residual network for road extraction in VHR remote sensing images / Lei Ding in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)
[article]
Titre : DiResNet: Direction-aware residual network for road extraction in VHR remote sensing images Type de document : Article/Communication Auteurs : Lei Ding, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2021 Article en page(s) : pp 10243 - 10254 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] image à très haute résolution
[Termes IGN] segmentation d'imageRésumé : (auteur) The binary segmentation of roads in very high resolution (VHR) remote sensing images (RSIs) has always been a challenging task due to factors such as occlusions (caused by shadows, trees, buildings, etc.) and the intraclass variances of road surfaces. The wide use of convolutional neural networks (CNNs) has greatly improved the segmentation accuracy and made the task end-to-end trainable. However, there are still margins to improve in terms of the completeness and connectivity of the results. In this article, we consider the specific context of road extraction and present a direction-aware residual network (DiResNet) that includes three main contributions: 1) an asymmetric residual segmentation network with deconvolutional layers and a structural supervision to enhance the learning of road topology (DiResSeg); 2) a pixel-level supervision of local directions to enhance the embedding of linear features; and 3) a refinement network to optimize the segmentation results (DiResRef). Ablation studies on two benchmark data sets (the Massachusetts data set and the DeepGlobe data set) have confirmed the effectiveness of the presented designs. Comparative experiments with other approaches show that the proposed method has advantages in both overall accuracy and F1-score. The code is available at: https://github.com/ggsDing/DiResNet . Numéro de notice : A2021-870 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3034011 Date de publication en ligne : 16/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3034011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99128
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 12 (December 2021) . - pp 10243 - 10254[article]Flexible Gabor-based superpixel-level unsupervised LDA for hyperspectral image classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)
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Titre : Flexible Gabor-based superpixel-level unsupervised LDA for hyperspectral image classification Type de document : Article/Communication Auteurs : Sen Jia, Auteur ; Qingqing Zhao, Auteur ; Jiayue Zhuang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 10394 - 10409 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] classification non dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de Gabor
[Termes IGN] image hyperspectrale
[Termes IGN] ondelette de Gabor
[Termes IGN] segmentation d'image
[Termes IGN] superpixelRésumé : (auteur) Hyperspectral images encompass abundant information and provide unique characteristics for material classification. However, the labeling of training samples can be challenging in hyperspectral image classification. To address this problem, this study proposes a framework named flexible Gabor-based superpixel-level unsupervised linear discriminant analysis (FG- Su ULDA) to extract the most informative and discriminating features for classification. First, a number of 3-D flexible Gabor filters are rigorously designed using an asymmetric sinusoidal wave to sufficiently characterize the spatial–spectral structure in hyperspectral images. Then, an unsupervised linear discriminant analysis strategy guided by the entropy rate superpixel (ERS) segmentation algorithm, called Su ULDA, is skillfully introduced to reduce the extracted large amount of FG features. The Su ULDA method not only boosts the classification capability but also increases the peculiarity of features, with the aid of superpixel information. Finally, the achieved features are imported to the popular support vector machine classifier. The proposed FG- Su ULDA framework is applied to four real hyperspectral image data sets, and the experiments constantly prove that our FG- Su ULDA is superior to several state-of-the-art methods in both classification performance and computational efficiency, especially with scarce training samples. The codes of this work are available at http://jiasen.tech/papers/ for the sake of reproducibility. Numéro de notice : A2021-872 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3048994 Date de publication en ligne : 20/01/2021 En ligne : https://doi.org/10.1109/TGRS.2020.3048994 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99131
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 12 (December 2021) . - pp 10394 - 10409[article]Multigranularity multiclass-layer Markov random field model for semantic segmentation of remote sensing images / Chen Zheng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)
[article]
Titre : Multigranularity multiclass-layer Markov random field model for semantic segmentation of remote sensing images Type de document : Article/Communication Auteurs : Chen Zheng, Auteur ; Yun Zhang, Auteur ; Leiguang Wang, Auteur Année de publication : 2021 Article en page(s) : pp 10555 - 10574 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] champ aléatoire de Markov
[Termes IGN] granularité d'image
[Termes IGN] segmentation sémantique
[Termes IGN] texture d'imageRésumé : (auteur) Semantic segmentation is one of the most important tasks in remote sensing. However, as spatial resolution increases, distinguishing the homogeneity of each land class and the heterogeneity between different land classes are challenging. The Markov random field model (MRF) is a widely used method for semantic segmentation due to its effective spatial context description. To improve segmentation accuracy, some MRF-based methods extract more image information by constructing the probability graph with pixel or object granularity units, and some other methods interpret the image from different semantic perspectives by building multilayer semantic classes. However, these MRF-based methods fail to capture the relationship between different granularity features extracted from the image and hierarchical semantic classes that need to be interpreted. In this article, a new MRF-based method is proposed to incorporate the multigranularity information and the multilayer semantic classes together for semantic segmentation of remote sensing images. The proposed method develops a framework that builds a hybrid probability graph on both pixel and object granularities and defines a multiclass-layer label field with hierarchical semantic over the hybrid probability graph. A generative alternating granularity inference is suggested to provide the result by iteratively passing and updating information between different granularities and hierarchical semantics. The proposed method is tested on texture images, different remote sensing images obtained by the SPOT5, Gaofen-2, GeoEye, and aerial sensors, and Pavia University hyperspectral image. Experiments demonstrate that the proposed method shows a better segmentation performance than other state-of-the-art methods. Numéro de notice : A2021-873 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3033293 Date de publication en ligne : 11/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3033293 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99132
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 12 (December 2021) . - pp 10555 - 10574[article]