An Analysis of Spatial-Spectral Dependence in Hyperspectral Autoencoders

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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.

Original languageEnglish
Title of host publicationImage Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings
EditorsRikke Gade, Michael Felsberg, Joni-Kristian Kämäräinen
Publication date2023
ISBN (Print)9783031314377
Publication statusPublished - 2023
Event23nd Scandinavian Conference on Image Analysis, SCIA 2023 - Lapland, Finland
Duration: 18 Apr 202321 Apr 2023


Conference23nd Scandinavian Conference on Image Analysis, SCIA 2023
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13886 LNCS

Bibliographical note

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

ID: 357282323