Improving the Calibration of Airborne Hyperspectral Sensors for Earth Observation Dissertation zur

133 Pages·2015·6.53 MB·English

Improving the Calibration of Airborne Hyperspectral Sensors for Earth
Observation
Dissertation
zur
Erlangung der naturwissenschaftlichen Doktorwu¨rde
(Dr. sc. nat.)
vorgelegt der
Mathematisch-naturwissenschaftlichen Fakult¨at
der
Universita¨t Zu¨rich
von
Karim Lenhard
aus
Deutschland
Promotionskomitee
Prof. Dr. Michael E. Schaepman (Vorsitz)
Prof. Dr. Ross Purves
Dr. Peter Gege (Leitung der Dissertation)
Dr. Andreas Hu¨ni
Zu¨rich, 2015

Mathematisch-naturwissenschaftliche Fakulta¨t
der Universita¨t Zu¨rich
Dissertation
Improving the calibration of airborne hyperspectral sensors for Earth
observation
Author:
Karim Lenhard
Remote Sensing Laboratories
Department of Geography
University of Zurich
Winterthurerstrasse 190
CH-8057 Zurich
Switzerland
February 2015 - All rights reserved

For Katja.

Contents
Abstract iii
Kurzfassung v
1 Introduction 1
1.1 Hyperspectral remote sensing for Earth observation . . . . . . . . . . 1
1.2 The Calibration Home Base . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Pushbroom imaging spectrometers . . . . . . . . . . . . . . . . . . . . 3
1.4 Current state of laboratory calibration . . . . . . . . . . . . . . . . . 5
1.5 Objectives and structure of this thesis . . . . . . . . . . . . . . . . . . 6
2 Calibration of imaging spectrometer data 9
2.1 Measurement uncertainties . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.1 Monte Carlo uncertainty propagation . . . . . . . . . . . . . . 10
2.1.2 Traceability of calibrations . . . . . . . . . . . . . . . . . . . . 10
2.2 Sensor model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.1 Radiometric properties . . . . . . . . . . . . . . . . . . . . . . 11
2.2.2 Imaging properties . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3 Determination of sensor model parameters . . . . . . . . . . . . . . . 16
2.3.1 Radiometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.2 Imaging properties . . . . . . . . . . . . . . . . . . . . . . . . 18
3 Characterization of NEO HySpex sensors 21
4 Improvements in calibration to HySpex VNIR 37
5 APEX Calibration Information System 75
6 Determination of uncertainty for ROSIS 89
i
ii CONTENTS
7 Summary, Conclusion and Outlook 99
7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
7.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
7.3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
Curriculum Vitae 105
Acknowledgments 111
Abstract
Hyperspectral sensors are a widely used tool in remote sensing of the Earth’s surface.
Due to the versatility of the sensors, a multitude of applications profit from or use
hyperspectral data, most prominently the assessment of the impact of climate change
on the environment.
The acceptance of hyperspectral remote sensing stems, in part, from the relatively
easy access to data. Besides several existing and planned spaceborne instruments,
airborne sensors are by now a commercial, if expensive, commodity.
The German Aerospace Center (Deutsches Zentrum fu¨r Luft- und Raumfahrt,
DLR) operates an optical laboratory for the characterization and calibration of air-
borne hyperspectral sensors, the calibration home base (CHB). This laboratory is
needed to close the gap between the extensive characterization efforts performed for
spaceborne devices, and the characterization performed by the commercial sensor’s
manufacturers. Currently, the latter often does not incorporate the possibility of
the traceability of calibration, and may not take into consideration sensor proper-
ties that require more complex or time-consuming characterisation methods. As the
airborne sensors are effectively used to develop and validate methods for spaceborne
hyperspectral remote sensing, the same level of detail in characterization needs to be
applied to the airborne devices, resulting in similar measurement uncertainties. One
objective of this thesis is therefore to assess the impact of the characterization and
calibration possible with the CHB on data from airborne hyperspectral instruments.
This is shown on a pair of NEO HySpex hyperspectral instruments operated by DLR
for the bathymetry of lake Starnberg as an exemplary hyperspectral remote sensing
data product.
The requirements for the calibration of future spaceborne hyperspectral instru-
ments include the traceability to the syst`eme international d’unit´es (SI). The second
partofthethesisthereforedealswithdifferentaspectsofpropagationofmeasurement
uncertainty and traceability. The sensors discussed here are APEX (Airborne Prism
EXperiment) and ROSIS (Reflective Optics System Imaging Spectrometer). Two
software tools are discussed: First, a database for the storage of the entire APEX
laboratory characterization data, with frontends allowing for the rapid analysis of
characterization data as well as the generation of the data needed for the calibration.
iii
iv ABSTRACT
Within the database, laboratory metadata is always linked to the actual sensor data,
which simplifies the tracing of calibration back to the laboratory data. The second
software tool is a Monte Carlo simulation of the airborne measurement process of an
inland water body. This simulation is based on a sensor model of ROSIS, but other
sensor models, such as one of the HySpex instruments, can be implemented. This
allows to propagate the uncertainties from the laboratory measurements to airborne
data acquisition. Additionally, this simulation can be used to propagate the labora-
tory calibration uncertainties to final hyperspectral remote sensing products, which
can support the development of hyperspectral methods.