Unsupervised Segmentation of Hyperspectral Images Using 3-D Convolutional Autoencoders
Jakub Nalepa, Michał Myller, Yasuteru Imai, Kenichi Honda, Tomomi Takeda, Marek Antoniak
Abstract
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 high-quality segmentation without any prior class labels.