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Adversarial Learning for Knowledge Adaptation From Multiple Remote Sensing Sources
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DOI：10.1109/LGRS.2020.3003566.png)
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摘要
En 中文
In this work, we introduce a neural architecture to unsupervised domain from multiple source domains. This architecture uses an EfficientNet as a feature extractor coupled with a set of Softmax classifiers equal to the number of source domains followed by an opportune fusion layer. To reduce the domain discrepancy between each source and target domain, we adopt a Minmax entropy approach that is based on the idea of optimizing in an adversarial manner the conditional entropy of the target samples with respect to each source classifier and minimizes it with respect to the feature extractor. As for the fusion module, we propose a weighted average fusion layer with learnable weights for aggregating the outputs of the different Softmax classifiers. Experiments on a multisource data set composed of images acquired by manned and unmanned aerial vehicles (MAVs/UAVs) over different locations are reported and discussed.
Keyword:
Feature extraction
Entropy
Prototypes
Unmanned aerial vehicles
Optimization
Remote sensing
Standards
Adversarial learning
manned and unmanned aerial vehicles (MAVs
UAVs)
Minmax entropy
multiple sources
scene classification
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16.4
论文数:
1.0W
被引数:
5.1K
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