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• W2949803668 abstract "This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially, we train a variant of the SegNet architecture on remote sensing data over an urban area and study different strategies for performing accurate semantic segmentation. Our contributions are the following: 1) we transfer efficiently a DFCNN from generic everyday images to remote sensing images; 2) we introduce a multi-kernel convolutional layer for fast aggregation of predictions at multiple scales; 3) we perform data fusion from heterogeneous sensors (optical and laser) using residual correction. Our framework improves state-of-the-art accuracy on the ISPRS Vaihingen 2D Semantic Labeling dataset." @default.
• W2949803668 created "2019-06-27" @default.
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• W2949803668 date "2016-09-22" @default.
• W2949803668 modified "2023-09-25" @default.
• W2949803668 title "Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks" @default.
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• W2949803668 hasPublicationYear "2016" @default.
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