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Complex-valued convolutional neural network and its application in polarimetric SAR image classification / Zhimian Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 12 (December 2017) . - pp 7177 - 7188 Titre : Complex-valued convolutional neural network and its application in polarimetric SAR image classification Type de document : Article/Communication Auteurs : Zhimian Zhang, Auteur ; Haipeng Wang, Auteur ; Feng Xu, Auteur ; Ya-Qiu Jin, Auteur Année de publication : 2017 Article en page(s) : pp 7177 - 7188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage dirigé [Termes IGN] données polarimétriques [Termes IGN] image radar moirée [Termes IGN] polarimétrie radar [Termes IGN] réseau neuronal convolutif Résumé : (Auteur) Following the great success of deep convolutional neural networks (CNNs) in computer vision, this paper proposes a complex-valued CNN (CV-CNN) specifically for synthetic aperture radar (SAR) image interpretation. It utilizes both amplitude and phase information of complex SAR imagery. All elements of CNN including input-output layer, convolution layer, activation function, and pooling layer are extended to the complex domain. Moreover, a complex backpropagation algorithm based on stochastic gradient descent is derived for CV-CNN training. The proposed CV-CNN is then tested on the typical polarimetric SAR image classification task which classifies each pixel into known terrain types via supervised training. Experiments with the benchmark data sets of Flevoland and Oberpfaffenhofen show that the classification error can be further reduced if employing CV-CNN instead of conventional real-valued CNN with the same degrees of freedom. The performance of CV-CNN is comparable to that of existing state-of-the-art methods in terms of overall classification accuracy. Numéro de notice : A2017-770 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2743222 En ligne : https://doi.org/10.1109/TGRS.2017.2743222 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88810 [article]