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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2020-11, Vol.17 (11), p.1948-1952
Main Authors: Nalepa, Jakub, Myller, Michal, Imai, Yasuteru, Honda, Ken-Ichi, Takeda, Tomomi, Antoniak, Marek
Format: Article
Language:English
Subjects:
Autoencoder
Benchmark testing
Clustering
Convolutional codes
deep learning
Feature extraction
Hyperspectral imaging
hyperspectral imaging (HSI)
Image analysis
Image classification
Image processing
Image segmentation
Imagery
Machine learning
Remote sensing
Spectral bands
Training
unsupervised segmentation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2019.2960945