Detail publikace

Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning

ALVAREZ JUSTO, J. GHIŢĂ, A. KOVÁČ, D. L. GARRETT, J. GEORGESCU, M. GONZALEZ-LLORENTE, J. TUDOR IONESCU, R. ARNE JOHANSEN, T.

Originální název

Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

Satellites are increasingly adopting onboard AI to optimize operations and increase autonomy through in-orbit inference. The use of deep learning (DL) models for segmentation in hyperspectral imagery offers advantages for remote sensing applications. In this work, we train and test 20 models for multiclass segmentation in hyperspectral imagery, selected for their potential in future space deployment. These models include 1-D and 2-D convolutional neural networks (CNNs) and the latest vision transformers (ViTs). We propose a lightweight 1D-CNN model, 1D-Justo-LiuNet, which outperforms state-of-the-art models in the hypespectral domain. 1D-Justo-LiuNet exceeds the performance of 2D-CNN UNets and outperforms Apple's lightweight vision transformers designed for mobile inference. 1D-Justo-LiuNet achieves the highest accuracy (0.93) with the smallest model size (4563 parameters) among all tested models, while maintaining fast inference. Unlike 2D-CNNs and ViTs, which encode both spectral and spatial information, 1D-Justo-LiuNet focuses solely on the rich spectral features in hyperspectral data, benefitting from the high-dimensional feature space. Our findings are validated across various satellite datasets, with the HYPSO-1 mission serving as the primary case study for sea, land, and cloud segmentation. We further confirm our conclusions through generalization tests on other hyperspectral missions, such as NASA's EO-1. Based on its superior performance and compact size, we conclude that 1D-Justo-LiuNet is highly suitable for in-orbit deployment, providing an effective solution for optimizing and automating satellite operations at edge

Klíčová slova

1D-CNNs; 2D-CNNs; deep learning (DL); remote sensing; satellite hyperspectral imagery; segmentation; vision transformers (ViTs)

Autoři

ALVAREZ JUSTO, J.; GHIŢĂ, A.; KOVÁČ, D.; L. GARRETT, J.; GEORGESCU, M.; GONZALEZ-LLORENTE, J.; TUDOR IONESCU, R.; ARNE JOHANSEN, T.

Vydáno

7. 11. 2024

Nakladatel

IEEE

Místo

New York

ISSN

2151-1535

Periodikum

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Ročník

18

Číslo

1

Stát

Spojené státy americké

Strany od

273

Strany do

293

Strany počet

21

URL

https://ieeexplore.ieee.org/document/10746584

Plný text v Digitální knihovně

http://hdl.handle.net/11012/250842

BibTex

@article{BUT193496,
  author="Jon {Alvarez Justo} and Alexandru {Ghiţă} and Daniel {Kováč} and Joseph {L. Garrett} and Mariana-Iuliana {Georgescu} and Jesus {Gonzalez-Llorente} and Radu {Tudor Ionescu} and Tor {Arne Johansen}",
  title="Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning",
  journal="IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",
  year="2024",
  volume="18",
  number="1",
  pages="273--293",
  doi="10.1109/JSTARS.2024.3487360",
  issn="2151-1535",
  url="https://ieeexplore.ieee.org/document/10746584"
}