# Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders

## TL;DR

This paper presents an unsupervised deep learning method using 3D convolutional autoencoders combined with clustering to segment hyperspectral images effectively without requiring labeled data.

## Contribution

The authors introduce a novel unsupervised deep architecture that couples 3D convolutional autoencoders with clustering for hyperspectral image segmentation.

## Key findings

- Achieves high-quality segmentation without prior class labels
- Performs well on benchmark and real-life datasets
- Outperforms traditional methods in unsupervised segmentation

## 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 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.