An Analysis of Spatial-Spectral Dependence in Hyperspectral Autoencoders

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Hyperspectral imaging is central for remote sensing, and much research has been done on analysis methods for land surveillance using space- and air-borne imaging systems. Proximal hyperspectral imaging is also widely used in plant and agriculture science. It allows the remote capturing of leaf reflectance information in order to determine and classify plant health and disease. With the high information density in hyperspectral images, it becomes increasingly important to apply sophisticated feature extraction in order to reduce image dimensionality while retaining useful information. Autoencoders are one of the primary methods for deep learning-based feature extraction in hyperspectral images. We investigate different setups of autoencoders to encode the spatial and spectral dimensions in different orders and ways. To our surprise, the best turns out to be a 3D CNN, where the spectral dimension is treated in the same way as the spatial dimensions.

TitelImage Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings
RedaktørerRikke Gade, Michael Felsberg, Joni-Kristian Kämäräinen
ISBN (Trykt)9783031314377
StatusUdgivet - 2023
Begivenhed23nd Scandinavian Conference on Image Analysis, SCIA 2023 - Lapland, Finland
Varighed: 18 apr. 202321 apr. 2023


Konference23nd Scandinavian Conference on Image Analysis, SCIA 2023
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind13886 LNCS

Bibliografisk note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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