A Change-Driven Image Foveation Approach for Tracking Plant Phenology
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Data
2020-05-01
Autores
Silva, Ewerton
Torres, Ricardo S.
Alberton, Bruna [UNESP]
Morellato, Leonor Patricia C. [UNESP]
Silva, Thiago S. F.
Título da Revista
ISSN da Revista
Título de Volume
Editor
Mdpi
Resumo
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.
Descrição
Palavras-chave
foveal model, image foveation, hilbert curve, plant phenology tracking, space-variant image
Como citar
Remote Sensing. Basel: Mdpi, v. 12, n. 9, 14 p., 2020.