Reduction of Uncorrelated Striping Noise—Applications for Hyperspectral Pushbroom Acquisitions

Abstract

Hyperspectral images are of increasing importance in remote sensing applications. Imaging spectrometers provide semi-continuous spectra that can be used for physics based surface cover material identification and quantification. Preceding radiometric calibrations serve as a basis for the transformation of measured signals into physics based units such as radiance. Pushbroom sensors collect incident radiation by at least one detector array utilizing the photoelectric effect. Temporal variations of the detector characteristics that differ with foregoing radiometric calibration cause visually perceptible along-track stripes in the at-sensor radiance data that aggravate succeeding image-based analyses. Especially, variations of the thermally induced dark current dominate and have to be reduced. In this work, a new approach is presented that efficiently reduces dark current related stripe noise. It integrates an across-effect gradient minimization principle. The performance has been evaluated using artificially degraded whiskbroom (reference) and real pushbroom OPEN ACCESS Remote Sens. 2014, 6 11083 acquisitions from EO-1 Hyperion and AISA DUAL that are significantly covered by stripe noise. A set of quality indicators has been used for the accuracy assessment. They clearly show that the new approach outperforms a limited set of tested state-of-the-art approaches and achieves a very high accuracy related to ground-truth for selected tests. It may substitute recent algorithms in the Reduction of Miscalibration Effects (ROME) framework that is broadly used to reduce radiometric miscalibrations of pushbroom data takes.

Detection and Mapping of Black Rock Coatings Using Hyperion Images: Sudbury, Ontario, Canada

“…The Hyperion sensor, on board the orbiting EO-1 (Earth Observing One) satellite, collects data with a 30 m pixel size in 242 bands in the visible, near-infrared, and shortwave infrared ranges [60,61]. Though Hyperion images are characterized by relatively high levels of noise [59,[62][63][64][65][66][67][68], they represent important alternatives to datasets generated by orbiting multispectral systems. In numerous case studies, Hyperion images have proven useful in the mapping of lithological and mineralogical classes [63,64,67,[69][70][71][72][73][74][75][76].…”

Improving Sensor Fusion: A Parametric Method for the Geometric Coalignment of Airborne Hyperspectral and Lidar Data

“…The offset (that includes the thermally induced dark current) is determined automatically, by closing a shutter before (VNIR) or after (SWIR) every flight line [27] measuring the dark signal. The gain coefficient is determined in laboratory measuring the sensor's response of a radiance standard that is illuminated by a known artificial light source [31]. The result of the radiometric correction is a separate hyperspectral data cube for VNIR and SWIR including at-sensor-radiance values.…”

EnGeoMAP 2.0—Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission

“…This approach includes the reduction of miscalibration effects noticeable as striping artifacts in the data [18] and an atmospheric correction routine, which has been designed for …”

“…This approach includes the reduction of miscalibration effects noticeable as striping artifacts in the data [18] and an atmospheric correction routine, which has been designed for the EnMAP Toolbox [29], which is based on the theoretical framework of ATCOR [30]. The Hyperion data used in this study is listed in Table A1 of Appendix A. EnMAP data was simulated from the hyperspectral airborne reflectance mosaics of HyMap using the EnMAP End to End simulation tool [15].…”