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Deep Network Ensembles for Aerial Scene Classification
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Densely Connected Convolutional Networks
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Remote Sensing Image Scene Classification: Benchmark and State of the Art
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Fusing Local and Global Features for High-Resolution Scene Classification
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Aerial Scene Classification via Multilevel Fusion Based on Deep Convolutional Neural Networks
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Scene Recognition From Optical Remote Sensing Images Using Mid-Level Deep Feature Mining
inferredcites →https://doi.org/10.1109/tgrs.2017.2685945openalex-snapshot
AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification
inferredcites →https://doi.org/10.1109/tgrs.2017.2700322openalex-snapshot
Deep Feature Fusion for VHR Remote Sensing Scene Classification
inferredcites →https://doi.org/10.1109/tgrs.2017.2702596openalex-snapshot
Remote Sensing Scene Classification by Unsupervised Representation Learning
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When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs
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Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery
inferredcites →https://doi.org/10.48550/arxiv.1704.00109openalex-snapshot
Snapshot Ensembles: Train 1, get M for free
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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