Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification

By Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat, Pierre Alliez. IEEE Transactions on Geoscience and Remote Sensing, 2017

990 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/25e7412e12b922e6e3aab377caa85a7b51730f8f

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Citations in the corpus

Land-cover classification with high-resolution remote sensing images using transferable deep models (2018, methodology): “To obtain satisfactory land-cover classification on a RS image of interest, referred as the target image, new specific annotated samples closely related to it are often necessary for model fine-tuning [37].”
Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark (2017, background): “1 depicts a classification map over Zurich into the building/not building classes, created by using a CNN classifier trained over a semirural area in France [4].”
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series (2018, methodology): “CNNs have been widely applied to various remote sensing tasks including land cover classification of very high spatial resolution images [25,26], semantic segmentation [27], object detection [28], reconstruction of missing data [29], or pansharpening [30].”
Building Instance Classification Using Street View Images (2018, positive): “Taking advantage of geographical information from OpenStreetMap, a fully convolutional neural network is trained to achieve pixel-wise classifications in optical images on large scales (Maggiori et al., 2017).”
Learning Aerial Image Segmentation From Online Maps (2017, methodology): “We conclude that if only a limited quantity of high-quality training data is available, pretraining on free data brings even larger relative benefits, and can be recommended as general practice, which is in line with the findings reported in [50].”
Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks (2019, positive): “…learning enables to further improve the learning task where backpropagation successfully re-weights labeled data from natural image datasets, e.g. ImageNet to solve new problems in remote sensing datasets (e.g. Maggiori et al., 2017; Marmanis et al., 2016; Nogueira et al., 2017; Kang et al., 2018).”
Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use and Land Cover Classification: Outcome of the 2018 IEEE GRSS Data Fusion Contest (2019, positive): “Compared with previous FCN-based approaches [29]–[32], our”
Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks (2017, neutral): “vehicle detection [1], semantic labeling [2, 3, 4, 5] and land cover/use classification [6, 7].”
Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images (2019, background): “This architecture has been extended to remote sensing applications too [46], [50].”
High-quality hyperspectral reconstruction using a spectral prior (2017, methodology): “1 Convolutional Autoencoder Convolutional neural networks have been recently used in many tasks, such as spectral image classification [Li et al. 2017; Maggiori et al. 2016], or to extract features from images [Long et al.”
A Survey on Deep Learning for Multimodal Data Fusion (2020, background): “It has made much progress in medical image recognition (Maggiori, Tarabalka, Charpiat, & Alliez, 2017) and semantic analysis (Hu, Lu, Li, & Chen, 2014).”
Deep Learning for Geophysics: Current and Future Trends (2021, methodology): “The first introduction of DL in subjects related to geophysics focused on remote sensing in 2016 and 2017 (Chen, Jiang, et al., 2016; Chen, Wang, et al., 2016; Maggiori et al., 2017; Li et al., 2017), since remote sensing is a common technique widely used in many areas.”