Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU154814" target="_blank" >RIV/00216305:26220/24:PU154814 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10746584" target="_blank" >https://ieeexplore.ieee.org/document/10746584</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/JSTARS.2024.3487360" target="_blank" >10.1109/JSTARS.2024.3487360</a>

Result language

angličtina

Original language name

Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning

Original language description

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

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/FW09020069" target="_blank" >FW09020069: On-board satellite AI-based system for effective hyperspectral data filtration and processing</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE journal of selected topics in applied earth observations and remote sensing

ISSN

1939-1404

e-ISSN

2151-1535

Volume of the periodical

18

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

21

Pages from-to

273-293

UT code for WoS article

001409622100020

EID of the result in the Scopus database

2-s2.0-85208803093