Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders
This addresses the problem of unsupervised segmentation for remote sensing applications, offering an incremental improvement by combining existing techniques in a novel way.
The paper tackles the challenge of training well-generalizing models for hyperspectral image segmentation due to lack of ground-truth data by proposing an end-to-end unsupervised approach using 3D convolutional autoencoders with clustering, which delivers high-quality segmentation without prior class labels.
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.