Deep Learning for In-Orbit Cloud Segmentation and Classification in Hyperspectral Satellite Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU155003" target="_blank" >RIV/00216305:26220/24:PU155003 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/ICFSP62546.2024.10785468" target="_blank" >https://doi.org/10.1109/ICFSP62546.2024.10785468</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICFSP62546.2024.10785468" target="_blank" >10.1109/ICFSP62546.2024.10785468</a>

Result language

angličtina

Original language name

Deep Learning for In-Orbit Cloud Segmentation and Classification in Hyperspectral Satellite Data

Original language description

This article explores the latest Convolutional Neural Networks (CNNs) for cloud detection aboard hyperspectral satel-lites. The performance of the 1D CNN (1D-Justo-LiuNet) and two 2D CNNs (nnU-net and 2D-Justo-UNet-Simple) for cloud segmentation and classification is assessed. Evaluation criteria include precision and computational efficiency for in-orbit deployment. Experiments utilize NASA's EO-1 Hyperion data, with different spectral channel numbers after Principal Component Analysis. Results indicate that 1D-Justo-LiuNet achieves the highest accu-racy, outperforming 2D CNNs, while maintaining compactness with larger spectral channel sets, albeit with increased inference times. However, the performance of 1D CNN degrades with significant channel reduction. In this context, the 2D-Justo-UNet-Simple offers a good balance for in-orbit deployment, considering precision, memory, and time costs. While nnU-net is suitable for on-ground processing, deployment of lightweight 1D-Justo-LiuNet is recommended for high-precision applications. Alternatively, lightweight 2D-Justo-UNet-Simple balanced better the computational cost and precision for in-orbit deployment.

Czech name

—

Czech description

—

Type

D - Article in proceedings

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)

Publication year

2024

Confidentiality

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

Article name in the collection

2024 9th International Conference on Frontiers of Signal Processing (ICFSP)

ISBN

979-8-3503-5323-5

ISSN

—

e-ISSN

—

Number of pages

5

Pages from-to

68-72

Publisher name

IEEE

Place of publication

neuveden

Event location

Paříž

Event date

Sep 12, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—