Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders
| Autor: | Jakub Nalepa, Marek Antoniak, Michal Myller, Tomomi Takeda, Ken-Ichi Honda, Yasuteru Imai |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer science business.industry Deep learning Computer Vision and Pattern Recognition (cs.CV) Feature extraction 0211 other engineering and technologies Computer Science - Computer Vision and Pattern Recognition Hyperspectral imaging Pattern recognition 02 engineering and technology Spectral bands Image segmentation Geotechnical Engineering and Engineering Geology Field (computer science) Convolutional code Segmentation Artificial intelligence Electrical and Electronic Engineering Cluster analysis business 021101 geological & geomatics engineering |
| DOI: | 10.48550/arxiv.1907.08870 |
| Popis: | 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 a 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 3D convolutional autoencoders with clustering. Our multi-faceted experimental study---performed over benchmark and real-life data---revealed that our approach delivers high-quality segmentation without any prior class labels. Comment: Submitted to IEEE Geoscience and Remote Sensing Letters |
| Databáze: | OpenAIRE |
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