Sensors and applications for drone based remote sensing

Transcription

1 Sensors and applications for drone based remote sensing Dr. Ilkka Pölönen Dr. Eija Honkavaara, NLS Dr. Heikki Saari, VTT Dr. Sakari Tuominen, Mr. Jere Kaivosoja, Luke Laboratory

2 Joint research with Finnish research institutes and companies since 2010 Frontiers of Hyperspectral Imaging from UAV s Laser Scanning from UAV s Finnish ecosystem VTT -> sensor development MLL -> UAV measurements, radiometric and geometric processing JyU -> Data analysis Luke -> Application specialists in forestry and agriculture Laboratory

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4 Hyperspectral imaging Intensity Spectrum of single spatial pixel Wavelength (nm) Laboratory

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6 Hyperspectral applications on: Agriculture Disease detection Biomass mapping Nitrogen mapping Stress mapping Forestry Tree species classification Tree diseases Forest inventory Laboratory

7 Common factors in different applications Finding abnormalities Identify detections Screening and monitoring phenomena Laboratory

8 Light-weight hyperspectral camera based on piezo actuated Fabry-Perot Interferometer (FPI) Laboratory12

9 Combining the Fabry-Perot interferometer with the mosaic RGB CMOS sensor FPI transmission Quantum efficiency of modified Bayer matrix Final Pixel Quantum efficiency + = Saari, H., et al.., Miniaturized hyperspectral imager calibration and UAV flight campaigns. Remote Sensing for Agriculture, Ecosystems, and Hydrology XV. SPIE Remote Sensing, September 2013, Dresden, Germany. Laboratory13

10 FPI optical path proto 2012 Laboratory14

11 Fabry-Perot interferometer based tuneable VIS/NIR spectral camera Proto 2012 Hyperspectral imageryin frame format: stereoscopic, spectrometric data -> 3D hyperspectral analysis Weigh <700 g Custom optics: C=10.9 mm, F- number < 3.0 CMOS detector 1024 x 648 pixels Pixel 11 μm 24 freely selectable spectral bands, more than 40 in in total Various setups are possible, includingshortwave infrared (SWIR) Commercial systems by Senop/Rikola Laboratory15

12 FGI hyperspectral, photogrammetric UAV measurement system: Summer 2014 setup Laboratory

13 Campaign examples Laboratory21

14 UAV measurement system UAV Autopilot IMU GPS Payload FPI Spectral imager RGB High spatial resolution imager GPS Irradiance sensors Ground control station Mission design and control Insitu reference measurements: irradiance, reflectance targets, sunphotometer (optional) In typical flight data cubes with spectral layers Georeferencing data Irradiance data Insitu data Laboratory22

15 1. Vihti campaign 2012 Objective: Opimizing nigtogen fertilization Wheat crops in agricultural test area in Southern Finland July 2, :39 local time Poor illumination conditions with fluctuating levels of cloudiness Flying altitude 140 m -> GSD of 14 cm Flying speed: 3.5 m/s. Five image strips and a total of 80 images Ground control points, reflectance targets Laboratory23

16 Image mosaics and sample spectra No correction Image based correction Laboratory24

17 Supervised machine learning schema for nitrogen and biomass estimation Reference material: 200 sample plots Dry mass Moisture Nitrogen content Non-linear unmixing was used for feature extraction. Features with highest correlation were selected for estimation. Estimation using KNN-based estimator. Dry biomass (kg/ha) Sample Laboratory25

18 Supervised machine learning schema for nitrogen and biomass estimation Biomass estimation (r = 0.87; R 2 = 0.75; RMSE <20% ) Nitrogen estimation (r = 0.82; R 2 = 0.66 ; RMSE <20%) Laboratory26

19 Construction on Nitrogen fertilization task Greenwood, D. J., Neeteson J. J., Draycott, A. Quantitative relationships for the dependence of growth rate of arable crops on their nitrogen content, dry weight and aerial environment. Plant and Soil 91, (1986) N NEED = æ 5.35 * Biomass ç è Nitrogen ö * ø Biomass 1000 Laboratory27

20 Task generation Resampling to 5 m grid Contouring zones Adding application rate values Laboratory28

21 Results Laboratory29

22 Individual tree classification: Example from Area with birches and spruces Yellow points: Spruces Blue points: Birches Laboratory

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25 Analysis Processing chain for UAV Case individual tree spectral analysis Data capture ALS DTM Individual tree detection Field reflectance reference Spectral characteristics of trees Forest reference plots Image preprocessing Image orientation DSM generation Radiometric block adjustment Mosaic generation Classification 41 Laboratory

26 Thank you! Laboratory