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Journal Article

Reduction of Radiometric Miscalibration - Applications to Pushbroom Sensors

Authors
/persons/resource/rogass

Rogaß,  Christian
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/daniel

Spengler,  Daniel
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/mbochow

Bochow,  Mathias
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/segl

Segl,  Karl
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Lausch,  A.
External Organizations;

Doktor,  D.
External Organizations;

/persons/resource/roessner

Roessner,  Sigrid
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/behling

Behling,  Robert
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/wetz

Wetzel,  Hans-Ulrich
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/charly

Kaufmann,  Hermann
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Citation

Rogaß, C., Spengler, D., Bochow, M., Segl, K., Lausch, A., Doktor, D., Roessner, S., Behling, R., Wetzel, H.-U., Kaufmann, H. (2011): Reduction of Radiometric Miscalibration - Applications to Pushbroom Sensors. - Sensors, 11, 6, 6370-6395.
https://doi.org/10.3390/s110606370


https://gfzpublic.gfz-potsdam.de/pubman/item/item_248161
Abstract
The analysis of hyperspectral images is an important task in Remote Sensing. Foregoing radiometric calibration results in the assignment of incident electromagnetic radiation to digital numbers and reduces the striping caused by slightly different responses of the pixel detectors. However, due to uncertainties in the calibration some striping remains. This publication presents a new reduction framework that efficiently reduces linear and nonlinear miscalibrations by an image-driven, radiometric recalibration and rescaling. The proposed framework—Reduction Of Miscalibration Effects (ROME)—considering spectral and spatial probability distributions, is constrained by specific minimisation and maximisation principles and incorporates image processing techniques such as Minkowski metrics and convolution. To objectively evaluate the performance of the new approach, the technique was applied to a variety of commonly used image examples and to one simulated and miscalibrated EnMAP (Environmental Mapping and Analysis Program) scene. Other examples consist of miscalibrated AISA/Eagle VNIR (Visible and Near Infrared) and Hawk SWIR (Short Wave Infrared) scenes of rural areas of the region Fichtwald in Germany and Hyperion scenes of the Jalal-Abad district in Southern Kyrgyzstan. Recovery rates of approximately 97% for linear and approximately 94% for nonlinear miscalibrated data were achieved, clearly demonstrating the benefits of the new approach and its potential for broad applicability to miscalibrated pushbroom sensor data.