Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29565
Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks (Forthcoming/Available Online)
Author(s): Nogueira, Keiller
dos Santos, Jefersson A
Menini, Nathalia
Silva, Thiago S F
Morellato, Leonor Patricia C
da S Torres, Ricardo
Keywords: Geotechnical Engineering and Engineering Geology
Electrical and Electronic Engineering
Issue Date: 4-Apr-2019
Citation: Nogueira K, dos Santos JA, Menini N, Silva TSF, Morellato LPC & da S Torres R (2019) Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks (Forthcoming/Available Online). IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/lgrs.2019.2903194
Abstract: Plant phenology studies rely on long-term monitoring of life cycles of plants. High-resolution unmanned aerial vehicles (UAVs) and near-surface technologies have been used for plant monitoring, demanding the creation of methods capable of locating, and identifying plant species through time and space. However, this is a challenging task given the high volume of data, the constant data missing from temporal dataset, the heterogeneity of temporal profiles, the variety of plant visual patterns, and the unclear definition of individuals' boundaries in plant communities. In this letter, we propose a novel method, suitable for phenological monitoring, based on convolutional networks (ConvNets) to perform spatio-temporal vegetation pixel classification on high-resolution images. We conducted a systematic evaluation using high-resolution vegetation image datasets associated with the Brazilian Cerrado biome. Experimental results show that the proposed approach is effective, overcoming other spatio-temporal pixel-classification strategies.
DOI Link: 10.1109/lgrs.2019.2903194
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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