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. IEEE Geoscience and Remote Sensing Letters, 16 (10), pp. 1665-1669. 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.
Keywords Geotechnical Engineering and Engineering Geology; Electrical and Electronic Engineering
Nogueira, Keiller; dos Santos, Jefersson A; Menini, Nathalia; Silva, Thiago S F; Morellato, Leonor Patricia C; da S Torres, Ricardo
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Pro-Reitoria de Pesquisa da UFMG, Fundacao de Amparo a Pesquisa do Estado de Minas Gerais FAPEMIG, Sao Paulo Research Foundation FAPESP, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico CNPq Research Fellowship to JAS LPCM RST and TSFS, Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior Brasil Finance Code 001 and Cedro Textil Reserva Vellozia Parque Nacional da Serra do Cipo