Patch size selection for analysis of sub-meter resolution hyperspectral imagery of forests
Loading...
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Date
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
Proceedings of 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, IEEE International Geoscience and Remote Sensing Symposium proceedings
Abstract
Very high resolution remote sensing data of forests, where individual tree crowns are separable, contains structural information on tree size and density. Such information is complementary to the spectral signatures currently used in forestry applications. Advanced machine learning methods, e.g. convolutional neural networks (CNNs), offer an automated and standardized way of retrieving both spectral and structural information from imagery. A key characteristic in CNNs is patch size, which should be large enough to include dominant structural scale, yet as small as possible to avoid unnecessary averaging. Our results show that the patch should be larger than one tree, but increasing it excessively reduces retrieval accuracy. Furthermore, large patch sizes can cause loss of independence between training and validation data, leading to overestimating model performance.Description
Keywords
Other note
Citation
Mõttus, M, Molinier, M, Halme, E, Cu, T & Laaksonen, J 2021, Patch size selection for analysis of sub-meter resolution hyperspectral imagery of forests. in Proceedings of 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE International Geoscience and Remote Sensing Symposium proceedings, IEEE, IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium, 11/07/2021. https://doi.org/10.1109/IGARSS47720.2021.9554257