期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 17, 期 11, 页码 1948-1952出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2019.2960945
关键词
Autoencoder; clustering; deep learning; hyperspectral imaging (HSI); unsupervised segmentation
类别
资金
- 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
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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