Details
| Original language | English |
|---|---|
| Pages (from-to) | 505-511 |
| Number of pages | 7 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 5 |
| Issue number | 3 |
| Publication status | Published - 3 Aug 2020 |
| Event | 2020 24th ISPRS Congress on Technical Commission III - Nice, Virtual, France Duration: 31 Aug 2020 → 2 Sept 2020 |
Abstract
Large-scale mapping of the Brazilian Savanna (Cerrado) vegetation using remote sensing images is still a challenge due to the high spatial variability and spectral similarity of the different characteristic vegetation types (physiognomies). In this paper, we report on semantic segmentation of the three major groups of physiognomies in the Cerrado biome (Grasslands, Savannas and Forests) using a fully convolutional neural network approach. The study area, which covers a Brazilian conservation unit, was divided into three regions to enable testing the approach in regions that were not used in the training phase. A WorldView-2 image was used in cross validation experiments, in which the average overall accuracy achieved with the pixel-wise classifications was 87.0%. The F-1 score values obtained with the approach for the classes Grassland, Savanna and Forest were of 0.81, 0.90 and 0.88, respectively. Visual assessment of the semantic segmentation outcomes was also performed and confirmed the quality of the results. It was observed that the confusion among classes occurs mainly in transition areas, where there are adjacent physiognomies if a scale of increasing density is considered, which agrees with previous studies on natural vegetation mapping for the Cerrado biome.
Keywords
- Biome, Cerrado, Deep Learning., Physiognomies, pixel-wise classification, Remote Sensing
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
- Environmental Science(all)
- Environmental Science (miscellaneous)
- Physics and Astronomy(all)
- Instrumentation
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In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 5, No. 3, 03.08.2020, p. 505-511.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Semantic segmentation of brazilian savanna vegetation using high spatial resolution satellite data and u-net
AU - Neves, A. K.
AU - Körting, T. S.
AU - Fonseca, L. M. G.
AU - Neto, C. D. Girolamo
AU - Wittich, D.
AU - Costa, G. A.O.P.
AU - Heipke, C.
N1 - Funding Information: Part of this study was carried out while the first author was working at IPI (Institut für Photogrammetrie und GeoInformation), Leibniz Universität Hannover, with a scholarship from the Deutscher Akademischer Austauschdienst (DAAD). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. The authors also thank the Brazilian National Research Council (CNPq) (grant Nº 140372/2017-2) and the São Paulo Foundation (FAPESP) (grant Nº 2017/24086-2) for financial support, and the DigitalGlobe Foundation for supplying the image for this work and cooperating with INPE.
PY - 2020/8/3
Y1 - 2020/8/3
N2 - Large-scale mapping of the Brazilian Savanna (Cerrado) vegetation using remote sensing images is still a challenge due to the high spatial variability and spectral similarity of the different characteristic vegetation types (physiognomies). In this paper, we report on semantic segmentation of the three major groups of physiognomies in the Cerrado biome (Grasslands, Savannas and Forests) using a fully convolutional neural network approach. The study area, which covers a Brazilian conservation unit, was divided into three regions to enable testing the approach in regions that were not used in the training phase. A WorldView-2 image was used in cross validation experiments, in which the average overall accuracy achieved with the pixel-wise classifications was 87.0%. The F-1 score values obtained with the approach for the classes Grassland, Savanna and Forest were of 0.81, 0.90 and 0.88, respectively. Visual assessment of the semantic segmentation outcomes was also performed and confirmed the quality of the results. It was observed that the confusion among classes occurs mainly in transition areas, where there are adjacent physiognomies if a scale of increasing density is considered, which agrees with previous studies on natural vegetation mapping for the Cerrado biome.
AB - Large-scale mapping of the Brazilian Savanna (Cerrado) vegetation using remote sensing images is still a challenge due to the high spatial variability and spectral similarity of the different characteristic vegetation types (physiognomies). In this paper, we report on semantic segmentation of the three major groups of physiognomies in the Cerrado biome (Grasslands, Savannas and Forests) using a fully convolutional neural network approach. The study area, which covers a Brazilian conservation unit, was divided into three regions to enable testing the approach in regions that were not used in the training phase. A WorldView-2 image was used in cross validation experiments, in which the average overall accuracy achieved with the pixel-wise classifications was 87.0%. The F-1 score values obtained with the approach for the classes Grassland, Savanna and Forest were of 0.81, 0.90 and 0.88, respectively. Visual assessment of the semantic segmentation outcomes was also performed and confirmed the quality of the results. It was observed that the confusion among classes occurs mainly in transition areas, where there are adjacent physiognomies if a scale of increasing density is considered, which agrees with previous studies on natural vegetation mapping for the Cerrado biome.
KW - Biome
KW - Cerrado
KW - Deep Learning.
KW - Physiognomies
KW - pixel-wise classification
KW - Remote Sensing
UR - http://www.scopus.com/inward/record.url?scp=85090355884&partnerID=8YFLogxK
U2 - 10.5194/isprs-Annals-V-3-2020-505-2020
DO - 10.5194/isprs-Annals-V-3-2020-505-2020
M3 - Conference article
AN - SCOPUS:85090355884
VL - 5
SP - 505
EP - 511
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
IS - 3
T2 - 2020 24th ISPRS Congress on Technical Commission III
Y2 - 31 August 2020 through 2 September 2020
ER -