# Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders

arXiv: https://arxiv.org/abs/1907.08870 · HTML: https://mlsanity.com/paper/1907.08870/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2019.

## Built on by (most cited first, 11 of 11)

- [Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels](https://mlsanity.com/paper/2204.12296/) (2022)
- [Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images](https://mlsanity.com/paper/2203.09992/) (2022)
- [Unsupervised Discovery of 3D Hierarchical Structure with Generative Diffusion Features](https://mlsanity.com/paper/2305.00067/) (2023)
- [Generalized Unsupervised Clustering of Hyperspectral Images of Geological Targets in the Near Infrared](https://mlsanity.com/paper/2106.13315/) (2021)
- [Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks](https://mlsanity.com/paper/2111.15018/) (2021)
- [Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/2111.13920/) (2021)
- [Hyperspectral Remote Sensing Image Classification Based on Multi-scale Cross Graphic Convolution](https://mlsanity.com/paper/2106.14804/) (2021)
- [A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentation](https://mlsanity.com/paper/2203.15058/) (2022)
- [Habitat classification from satellite observations with sparse annotations](https://mlsanity.com/paper/2209.12995/) (2022)
- [Remote sensing framework for geological mapping via stacked autoencoders and clustering](https://mlsanity.com/paper/2404.02180/) (2024)
- [Scalable Context-Preserving Model-Aware Deep Clustering for Hyperspectral Images](https://mlsanity.com/paper/2506.11377/) (2025)

## Abstract (arXiv)

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.