Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 17, Issue 11, Pages 1948-1952Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2019.2960945
Keywords
Autoencoder; clustering; deep learning; hyperspectral imaging (HSI); unsupervised segmentation
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Funding
- European Space Agency (HYPERNET)
- Polish National Centre for Research and Development [POIR.01.01.01-00-0356/17]
- Silesian University of Technology Funds under The Rector's Habilitation Grant [02/020/RGH19/0185]
- Ministry of Economy, Trade and Industry, Japan
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Hyperspectral image analysis has become an important topic widely researched by the remote sensing community. Classification and segmentation of such imagery help understand the underlying materials within a scanned scene since hyperspectral images convey detailed information captured in a number of spectral bands. Although deep learning has established the state-of-the-art in the field, it still remains challenging to train well-generalizing models due to the lack of ground-truth data. In this letter, we tackle this problem and propose an end-to-end approach to segment hyperspectral images in a fully unsupervised way. We introduce a new deep architecture which couples 3-D convolutional autoencoders with clustering. Our multifaceted experimental study-performed over the benchmark and real-life data-revealed that our approach delivers highquality segmentation without any prior class labels.
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