Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks
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Data
2019-10-01
Autores
Nogueira, Keiller
Santos, Jefersson A. dos
Menini, Nathalia
Silva, Thiago S. F. [UNESP]
Morellato, Leonor Patricia C. [UNESP]
Torres, Ricardo da S.
Título da Revista
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Título de Volume
Editor
Ieee-inst Electrical Electronics Engineers Inc
Resumo
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.
Descrição
Palavras-chave
Deep learning, near surface, phenology, pixel classification, unmanned aerial vehicles
Como citar
Ieee Geoscience And Remote Sensing Letters. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 16, n. 10, p. 1665-1669, 2019.