Listar UJI: Investigación por autoría "5db82516-08c9-43e8-9a29-1f4673ba8273"
Mostrando ítems 1-3 de 3
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Deep Learning-Based Building Footprint Extraction With Missing Annotations
Institute of Electrical and Electronics Engineers (2021-04-21)Most state-of-the-art deep learning-based methods for extraction of building footprints are aimed at designing proper convolutional neural network (CNN) architectures or loss functions able to effectively predict building ... -
Noise-Tolerant Deep Neighborhood Embedding for Remotely Sensed Images With Label Noise
IEEE (2021-02-02)Recently, many deep learning-based methods have been developed for solving remote sensing (RS) scene classification or retrieval tasks. Most of the adopted loss functions for training these models require accurate annotations. ...
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Rotation-Invariant Deep Embedding for Remote Sensing Images
Institute of Electrical and Electronics Engineers (2021-06-28)Endowing convolutional neural networks (CNNs) with the rotation-invariant capability is important for characterizing the semantic contents of remote sensing (RS) images since they do not have typical orientations. Most of ...
