The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 207–214, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-207-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 207–214, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-207-2019

  04 Jun 2019

04 Jun 2019

RASPBERRY PI 3 MULTISPECTRAL LOW-COST SENSOR FOR UAV BASED REMOTE SENSING. CASE STUDY IN SOUTH-WEST NIGER

E. Belcore1,2, M. Piras1, A. Pezzoli2, G. Massazza2, and M. Rosso1 E. Belcore et al.
  • 1Politecnico di Torino, DIATI, Department of Environment, Land and Infrastructure Engineering. Corso Duca degli Abruzzi, 24, 10129 Torino, Italia
  • 2Politecnico di Torino, DIST, Interuniversity Department of Regional and Urban Studies and Planning. Viale Pier Andrea Mattioli, 39,10125 Torino, Italia

Keywords: UAV (Unmanned Aerial Vehicles), Raspberry Pi, NoIR, camera calibration, multispectral sensor, low costing

Abstract. The technology of UAV (Unmanned Aerial Vehicles) is rapidly improving and UAV-integrated sensors have kept up with it, providing more efficient and effective solutions. One of the most sought-after characteristics of on-board sensors is the low costing associated to good quality of the collected data. This paper proposes a very low-cost multiband sensor developed on a Raspberry device and two Raspberry Pi 3 cameras that can be used in photogrammetry from drone applications. The UAV-integrated radiometric sensor and its performance were tested in in two villages of South-west Niger for the detection of temporary surface water bodies (or Ephemeral water bodies): zones of seasonal stagnant water within villages threatening the viability and people’s health. The Raspberry Pi 3 cameras employed were a regular RGB Pi camera 2 (Red, Green, Blue) and a NoIR Pi 3 camera v2 (regular RGB without IR filter) with 8MPX resolution. The cameras were geometrically calibrated and radiometrically tested before the survey in the field. The results of the photogrammetry elaborations were 4 orthophotos (a RGB and NoIRGB orthophoto for each village). The Normalized Difference Water Index (NDWI) was calculated. The index allowed the localization and the contouring of the temporary surface water bodies present in the villages. The data were checked against the data collected with a Sony (ILCE-5100). Very high correspondence between the different data was detected. Raspberry-based sensors demonstrated to be a valid tool for the data collection in critical areas.