Potential of Using Sentinel-1 Data to Distinguish Targets in Remote Sensing Images

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Jessie Santos Pletsch, Mikhaela Aloisia ; Korting, Thales Sehn ; de Oliveira, Willian Vieira ; Sanches, Ieda Del'Arco ; Fernandez, Victor Velazquez ; Gama, Fabio Furlan ; Sobral Escada, Maria Isabel ; Misra, S ; Gervasi, O ; Murgante, B ; Stankova, E ; Korkhov, V ; Torre, C ; Rocha, AMAC ; Taniar, D ; Apduhan, BO ; Tarantino, E

• Tipo de documento: Artigo Científico
• Fonte: COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2019, PT IV; v. 11622, p. 14-pg., 2019-01-01.

Resumo

Copernicus is the World's largest single Earth Observation (EO) programme, whose satellite constellations are planned to be launched between 2014 and 2025. Among the constellations, Sentinel-1 (S-1) is a C-band SAR able to support land cover mapping. Although optical data are commonly used for land cover monitoring, the low availability of cloud-free scenes along the year hinders the mapping process. In such a way, S-1 presents an important source of data, able of providing all-weather and day-and-night imagery of EO. In this study, we investigate the potential of using S-1 data to distinguish targets in Remote Sensing images in three different Brazilian biomes, Amazon, Cerrado, and Atlantic Forest. Based on that, we proposed a methodology to classify SAR images, which was validated considering a different area from the ones used for sampling purposes. The results showed that through S-1 data, it is possible to detect mainly water and urban area targets, with overall accuracy of 0.90, evidencing that our approach is reproducible in other regions. (AU)

• Processo FAPESP: 17/24086-2 - Gerenciamento de metadados de grandes volumes de dados de sensoriamento remoto
• Beneficiário: Thales Sehn Körting
• Modalidade de apoio: Auxílio à Pesquisa - Regular