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Precise crop classification of hyperspectral images using multi-branch feature fusion and dilation-based MLP / Haibin Wu in Remote sensing, vol 14 n° 11 (June-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 11 (June-1 2022) . - n° 2713 Titre : Precise crop classification of hyperspectral images using multi-branch feature fusion and dilation-based MLP Type de document : Article/Communication Auteurs : Haibin Wu, Auteur ; Huaming Zhou, Auteur ; Aili Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2713 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] cultures [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale [Termes IGN] Perceptron multicouche Résumé : (auteur) The precise classification of crop types using hyperspectral remote sensing imaging is an essential application in the field of agriculture, and is of significance for crop yield estimation and growth monitoring. Among the deep learning methods, Convolutional Neural Networks (CNNs) are the premier model for hyperspectral image (HSI) classification for their outstanding locally contextual modeling capability, which facilitates spatial and spectral feature extraction. Nevertheless, the existing CNNs have a fixed shape and are limited to observing restricted receptive fields, constituting a simulation difficulty for modeling long-range dependencies. To tackle this challenge, this paper proposed two novel classification frameworks which are both built from multilayer perceptrons (MLPs). Firstly, we put forward a dilation-based MLP (DMLP) model, in which the dilated convolutional layer replaced the ordinary convolution of MLP, enlarging the receptive field without losing resolution and keeping the relative spatial position of pixels unchanged. Secondly, the paper proposes multi-branch residual blocks and DMLP concerning performance feature fusion after principal component analysis (PCA), called DMLPFFN, which makes full use of the multi-level feature information of the HSI. The proposed approaches are carried out on two widely used hyperspectral datasets: Salinas and KSC; and two practical crop hyperspectral datasets: WHU-Hi-LongKou and WHU-Hi-HanChuan. Experimental results show that the proposed methods outshine several state-of-the-art methods, outperforming CNN by 6.81%, 12.45%, 4.38% and 8.84%, and outperforming ResNet by 4.48%, 7.74%, 3.53% and 6.39% on the Salinas, KSC, WHU-Hi-LongKou and WHU-Hi-HanChuan datasets, respectively. As a result of this study, it was confirmed that the proposed methods offer remarkable performances for hyperspectral precise crop classification. Numéro de notice : A2022-539 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14112713 Date de publication en ligne : 05/06/2022 En ligne : https://doi.org/10.3390/rs14112713 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101102 [article]

Efficient convolutional neural architecture search for LiDAR DSM classification / Aili Wang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 5 (May 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 5 (May 2022) . - n° 5703317 Titre : Efficient convolutional neural architecture search for LiDAR DSM classification Type de document : Article/Communication Auteurs : Aili Wang, Auteur ; Dong Xue, Auteur ; Haibin Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5703317 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] modèle de transfert radiatif [Termes IGN] modèle numérique de surface [Termes IGN] précision de la classification [Termes IGN] semis de points Résumé : (auteur) Light detection and ranging (LiDAR) data provide rich elevation information, so it plays an irreplaceable role in ground object classification. Recently, convolutional neural networks (CNNs) have shown excellent performance in LiDAR digital surface models (DSMs) classification. However, the architecture of CNN model relies heavily on manual design, so it has great limitations. In addition, different sensors capture LiDAR datasets with different properties, so the model should be designed to suit for different datasets, which further increases the workload of architecture design. Therefore, this article proposes a method of automatic design of LiDAR DSM classification model. First, attention mechanism is introduced into search space to improve the feature extraction capability of the model. Then, a gradient-based search strategy is used to obtain the optimal architecture from this search space. Second, a learning rate adjustment strategy is proposed to reduce the time spent in the search stage and evaluation stage to improve the classification accuracy of the model. Finally, a regularization scheme is introduced to enhance the robustness of the model and avoid overfitting. Experimental results on three public LiDAR datasets (Bayview Park, Recology, and Houston) obtained from different sensors show that the proposed neural architecture search method achieves the impressive classification performance compared to several state-of-the-art classification methods and improves the classification accuracy under the condition of limited training samples. Numéro de notice : A2022-408 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3171520 Date de publication en ligne : 02/05/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3171520 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100742 [article]