Transfer Learning for Segmenting Dimensionally Reduced Hyperspectral Images

Deep learning has established the state of the art in multiple fields, including hyperspectral image analysis. However, training large-capacity learners to segment such imagery requires representative training sets. Acquiring such data is human-dependent and time-consuming, especially in earth obser...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2020-07, Vol.17 (7), p.1228-1232
Main Authors: Nalepa, Jakub, Myller, Michal, Kawulok, Michal
Format: Article
Language:English
Subjects:
Benchmarks
Classification
Data
Data acquisition
Data transfer (computers)
deep learning
Earth
Feature extraction
Hyperspectral imaging
Image analysis
Image processing
Image segmentation
Imagery
Machine learning
Neural networks
segmentation
Sensors
Statistical analysis
Statistical tests
Time dependence
Training
Transfer learning
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Summary:Deep learning has established the state of the art in multiple fields, including hyperspectral image analysis. However, training large-capacity learners to segment such imagery requires representative training sets. Acquiring such data is human-dependent and time-consuming, especially in earth observation scenarios, where the hyperspectral data transfer is very costly and time-constrained. In this letter, we show how to effectively deal with a limited number and size of available hyperspectral ground-truth sets and apply transfer learning for building deep feature extractors. Also, we exploit spectral dimensionality reduction to make our technique applicable over hyperspectral data acquired using different sensors, which may capture different numbers of hyperspectral bands. The experiments, performed over several benchmarks and backed up with statistical tests, indicated that our approach allows us to effectively train well-generalizing deep convolutional neural nets even using significantly reduced data.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2019.2942832