Reduction of Radiometric Miscalibration—Applications to Pushbroom Sensors

Sensors

Christian Rogaß¹  Daniel Spengler¹  Mathias Bochow¹  Karl Segl¹  Angela Lausch²  Daniel Doktor²  Sigrid Roessner¹  Robert Behling¹  Hans-Ulrich Wetzel¹ 

[1]Section 1.4 Remote Sensing, Helmholtz Centre Potsdam–GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany

[2] E-Mails:

[3]Department of Landscape Ecology, Helmholtz Centre for Environmental Research–UFZ, Permoserstr.15, 04318 Leipzig, Germany

[4] E-Mails:

关键词: radiometric;    correction;    miscalibration;    stripes;    nonlinearity;    hyperspectral;    AISA;    Hyperion;    EnMAP;    MoLaWa;    PROGRESS;   

DOI  :  10.3390/s110606370

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.