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Wang et al., 2017 - Google Patents

Successive embedding and classification loss for aerial image classification

Wang et al., 2017

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Document ID
16337634418273875639
Author
Wang J
Virtue P
Yu S
Publication year
Publication venue
arXiv preprint arXiv:1712.01511

External Links

Snippet

Deep neural networks can be effective means to automatically classify aerial images but is easy to overfit to the training data. It is critical for trained neural networks to be robust to variations that exist between training and test environments. To address the overfitting …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
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    • G06K9/62—Methods or arrangements for recognition using electronic means
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    • G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
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    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
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    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
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