Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31506
Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: A Change-Driven Image Foveation Approach for Tracking Plant Phenology
Author(s): Silva, Ewerton
Torres, Ricardo da S
Alberton, Bruna
Morellato, Leonor Patricia C
Silva, Thiago S F
Keywords: foveal model
image foveation
hilbert curve
plant phenology tracking
space-variant image
Issue Date: May-2020
Citation: Silva E, Torres RdS, Alberton B, Morellato LPC & Silva TSF (2020) A Change-Driven Image Foveation Approach for Tracking Plant Phenology. Remote Sensing, 12 (9), Art. No.: 14. https://doi.org/10.3390/rs12091409
Abstract: One of the challenges in remote phenology studies lies in how to efficiently manage large volumes of data obtained as long-term sequences of high-resolution images. A promising approach is known as image foveation, which is able to reduce the computational resources used (i.e., memory storage) in several applications. In this paper, we propose an image foveation approach towards plant phenology tracking where relevant changes within an image time series guide the creation of foveal models used to resample unseen images. By doing so, images are taken to a space-variant domain where regions vary in resolution according to their contextual relevance for the application. We performed our validation on a dataset of vegetation image sequences previously used in plant phenology studies.
DOI Link: 10.3390/rs12091409
Rights: This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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