XXII ISPRS Congress 2012: Technical Commission VII

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Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/1: PHYSICAL MODELLING AND SIGNATURES IN REMOTE SENSING]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: REFLECTANCE CALIBRATION SCHEME FOR AIRBORNE FRAME CAMERA IMAGES U. Beisl

Document type:: Multivolume work

Structure type:: Chapter

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia sensor and in concentric circles of constant view azimuth angle for frame sensors. In addition, those circles have to be split up in segments for different relative azimuth angle. Due to missing sampling redundancy physical models cannot be used and are impractical for the large variety of surface types in a typical aerial image. So it is favourable to use linear semi- empirical kernel models like those developed by (Wanner et al., 1995). The inversion of linear models is a least squares regression and results in a simple matrix inversion while models with non-linear parameters would require calculation-intensive adaptive inversion algorithms. For the correction step which requires many forward calculations, simple kernel functions are preferable. As suggested in (Beisl et al., 2004) even a simple 3-parameter Walthall model (Walthall et al., 1985) without distinguishing between different ground types (“global correction”) shows good results (eqn 11). There is an extended version including a varying sun zenith angle (Nilson and Kuusk, 1989) (eqn 12). p(8,, 9) 2 a6? - b0, cos pc (11) 0(8,0,,9) 2 a0?8? - b(8? 9?) - c0, cos p d (12) where p = reflectance 6; = incident illumination zenith angle 0, = reflection view zenith angle 9 — relative azimuth angle a, b, c, d = free parameters Since the Walthall model does not include a hot spot term a simple empirical elliptical kernel function (eqn 13) is added to eqn (11) and (12) which is inspired by the hot spot distance function of the Li-kernels from the AMBRALS model (Wanner et al., 1995). D = Jtan? 6, + tan’ 6, - 2 tan 6, tan O, cos @ (13) In case of frame sensors and for reasonably short line scan images the incident illumination zenith angle is constant for a single frame, so there is no need to consider this angle in the BRDF correction and eqn (11) can be used. However, to cover larger areas, images are acquired in blocks with large overlap (60 % - 80 %) for stereo measurements. In order to make use of the redundancy and to ensure the proper radiometric matching of consecutive images a sliding window technique can be used by sampling the current image together with the previous and the following image and invert this set of samples to give the modelling function for the middle image. Depending on the block size, neighbouring flight lines may have considerable time offsets due to the flight planning schedule and therefore require considering the sun zenith angle as modelling variable. (Chandelier et al., 2009) and (Hernandez-Lopez et al., 2011) suggest sampling on a regular grid of so-called radiometric tie points, followed by an adjustment process and call the procedure "radiometric aerotriangulation". A first implementation will contain an NDVI-based land mask algorithm that prevents water areas from being sampled. This is because the water BRDF is of a specular reflectance type which is contrary to the land BRDF which is of a hot-spot type. 2.3. BRDF correction: Since the reflection process is a linear function of irradiance, a multiplicative correction by the ratio of the model values at the final geometry to the model values at the original geometry is used. P.(0,.9)= p(0,)*(R(6.,0)/R(0,,)) ^ a where p, p. = observed and corrected reflectance R(0, o) = modelled reflectance 0,= view zenith angle 6. = correction view zenith angle @ = relative azimuth angle 3. CONCLUSIONS AND OUTLOOK This paper has given an overview of practical methods to correct for radiometric distortions in photogrammetric images caused by environmental effects. The idea is to include as much physical information as is available into those corrections in order to give a true copy of the reality as if it were seen from the ground. This information includes absolute radiometric sensor calibration, solar position, and haze information. As a future step, measured ground spectra can be used to perform an in-flight calibration to improve the absolute radiometric calibration for remote sensing purposes (i.e adjust the calibration factors such that the measured spectra match with the spectra of the corresponding atmospherically corrected and reflectance calibrated pixels). Furthermore a class specific BRDF correction should be implemented to better adapt to the specific surface properties. Therefore a proper classification has to be made with a special treatment of the class boundaries. The atmospheric correction could be improved with the correction of the local adjacency effect to enhance the contrast in the image and also include to correction of topographic effects by varying terrain height, surface tilt and change in diffuse illumination by the percentage of visible sky. Also a shadow correction would be a favourable, but challenging add- on. However, the guideline for the implementation of any new feature must be the operational and efficient processing, and that no new artefacts are introduced. 4. REFERENCES Beisl, U., and Woodhouse, N., 2004. Correction of atmospheric and bidirectional effects in multispectral ADS40 images for mapping purposes. In: Int. Arch. Photogramm. Remote Sens., Istanbul, Turkey, Vol. XXXV, Part B7, 5 pp. Beisl, U., 2006. Absolute spectroradiometric calibration of the ADS40 Sensor. In: Int. Arch. Photogramm. Remote Sens., Paris, France, Vol. XXXVI, part 1, 5 pp. Beisl, U., Telaar, J, and Schônermark, M.v., 2008. Atmospheric correction, reflectance calibration and BRDF correction for ADS40 image data. In: Int. Arch. Photogramm. Remote Sens., Beijing, China, Vol. XXXVII, part B7, pp. 7-12. Beisl, U., and Adiguezel, M., 2010. Validation of the reflectance calibration of the ADS40 airborne sensor using

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