Unsupervised Segmentation of Hyperspectral Images Using 3-D Convolutional Autoencoders
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.