Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning

Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning

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 mul...

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Bibliographic Details
Main Authors: Jon Alvarez Justo, Alexandru Ghita, Daniel Kovac, Joseph L. Garrett, Mariana-Iuliana Georgescu, Jesus Gonzalez-Llorente, Radu Tudor Ionescu, Tor Arne Johansen
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
1D-CNNs
2D-CNNs
deep learning (DL)
remote sensing
satellite hyperspectral imagery
segmentation
Online Access:https://ieeexplore.ieee.org/document/10746584/
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https://ieeexplore.ieee.org/document/10746584/

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