Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W3, 19-25, 2017
https://doi.org/10.5194/isprs-archives-XLII-3-W3-19-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
19 Oct 2017
ESTIMATING EXTERIOR ORIENTATION PARAMETERS OF HYPERSPECTRAL BANDS BASED ON POLYNOMIAL MODELS
A. Berveglieri1, A. M. G. Tommaselli1, and E. Honkavaara2 1UNESP, São Paulo State University, Dept. of Cartography, 19060-900 Presidente Prudente – São Paulo, Brazil
2Finnish Geospatial Research Institute FGI, Kirkkonummi, Finland
Keywords: FPI camera, Image orientation, Photogrammetry, Polynomial model, UAV Abstract. Hyperspectral camera operating in sequential acquisition mode produces spectral bands that are not recorded at the same instant, thus having different exterior orientation parameters (EOPs) for each band. The study presents experiments on bundle adjustment with time-dependent polynomial models for band orientation of hyperspectral cubes sequentially collected. The technique was applied to a Rikola camera model. The purpose was to investigate the behaviour of the estimated polynomial parameters and the feasibility of using a minimum of bands to estimate EOPs. Simulated and real data were produced for the analysis of parameters and accuracy in ground points. The tests considered conventional bundle adjustment and the polynomial models. The results showed that both techniques were comparable, indicating that the time-dependent polynomial model can be used to estimate the EOPs of all spectral bands, without requiring a bundle adjustment of each band. The accuracy of the block adjustment was analysed based on the discrepancy obtained from checkpoints. The root mean square error (RMSE) indicated an accuracy of 1 GSD in planimetry and 1.5 GSD in altimetry, when using a minimum of four bands per cube.
Conference paper (PDF, 1192 KB)


Citation: Berveglieri, A., Tommaselli, A. M. G., and Honkavaara, E.: ESTIMATING EXTERIOR ORIENTATION PARAMETERS OF HYPERSPECTRAL BANDS BASED ON POLYNOMIAL MODELS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W3, 19-25, https://doi.org/10.5194/isprs-archives-XLII-3-W3-19-2017, 2017.

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