You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

Proceedings of the Symposium on Global and Environmental Monitoring

K. Staenz and D.G. Goodenough
Canada Centre for Remote Sensing
2464 Sheffield Road, Ottawa, Ontario, Canada K1A 0Y7
This paper describes the ISDA (Imaging
Spectrometer Data Analyzer) preprocessing steps
necessary to bring the imaging spectrometer data
acquired with the Programmable Multispectral
Imager (PMI) over an agricultural test site near
Zurich, Switzerland to an acceptable data quality
level for information extraction purposes. Such
preprocessing steps include data quality assess
ment, correction of data in the spectral, as well
as in the spatial domains, the proper radiometric
alignment of the five PMI cameras, and relative
and absolute normalization. Special emphasis was
directed towards the viewing angle effects result
ing from the large PMI scan angle range of 72.5
degrees. The removal of this effect is very
important for information extraction purposes with
respect to spectroscopic analysis and classifica
tion of airborne imaging data. Preliminary work
in the classification of imaging spectrometer data
of agricultural targets (e.g. wheat, corn,
potatoes, etc.) is presented. The two most likely
methodologies for classification of such data,
feature selection followed by classification and
'full' spectrum classification, will be discussed
with special emphasis on data reduction using the
band-moment analysis approach.
Key Words: Imaging spectrometer, data quality
assessment, radiometric camera
alignment, data normalization, data
reduction, band-moment analysis,
feature selection, classification.
Application of remote sensing technologies to
agriculture has resulted in numerous investiga
tions of the spectral behaviour of agricultural
objects (Buechel et al., 1989; Horler et al.,
1984; Brown et al., 1980). Most of the remotely
sensed data acquisition to date has been conducted
within well-defined, non-contiguous bands. There
have also been efforts made to collect spectral
data with spectroradiometers on the ground in
order to generate a full spectrum of the
agricultural object. Unfortunately, such ground-
based measurements usually do not provide
sufficient spatial coverage under similar
illumination conditions. In order to combine the
spatial as well as the spectral resolution and,
therefore, to increase the potential of remote
sensing substantially, a new generation of sensor
systems, such as the airborne imaging spectrometer
(Vane and Goetz, 1988), has been developed.
These instruments, such as NASA's AVIRIS (Airborne
Visible/Infrared Imaging Spectrometer) or
Moniteq ' s PMI ( Programmable Multispectral Imager),
acquire data simultaneously in many narrow
spectral bands (up to 288), usually covering the
VNIR/SWIR (Visible and Near Infra-Red/ShortWave
Infra-Red) region of the electromagnetic spectrum.
The volume and complexity of imaging spectrometer
data, however, require new approaches in image
processing, especially in data handling, prepro
cessing, and information extraction. These areas
are addressed in the Imaging Spectrometer Data
Analyzer (ISDA), a software package developed at
the Canada Centre for Remote Sensing for an
efficient analysis of imaging spectrometer data
(Staenz and Goodenough, 1989).
Discussed in this present study are the results
of an analysis involving an agricultural data set
with emphasis on the different ISDA preprocessing
steps required to bring the imaging data to an
acceptable quality. These steps are extremely
important for information extraction purposes and
the utilization of the full potential of imaging
spectrometer data. Preliminary results of a
classification approach are also presented.
The data set was acquired with the PMI sensor over
an agricultural test area, approximately 30 km NW
of Zurich, Switzerland on July 28, 1986 by the
German Aerospace Research Establishment (DLR).
The PMI sensor characteristics are summarized in
Table 1. This instrument works in a rake mode
providing a total of 40 profiles over the wave
length range from 430 to 805 nm in 288 contiguous,
2.6 nm wide, spectral bands sampled at 1.3 nm
intervals (Hollinger and Gray, 1987). The over
flight was conducted under nearly cloud-free
conditions (horizontal visual range approximately
15 km) from an altitude of 500 m resulting in a
swath width of approximately 730 m by a scan angle
of 72.5 degrees as well as a ground resolution at
nadir of 3.3 m across and 10.4 m along track. The
aircraft heading is approximately 33.0 degrees
azimuth and the solar illumination geometry is
given by a zenith angle of 29.2 degrees and an
azimuth of 194.3 degrees.
Major crop types within the flat test site include
wheat, barley, corn, and potatoes. Ground
reference information, such as plant height and
density, age (planting data), degree of fertiliza
tion, etc., was collected for these different
object types. In addition, black and white, as
well as false colour, aerial photography was used
in support of the ground data collection, for
preprocessing of the imaging spectrometer data and
for information extraction.
Problems were encountered during the process of
analyzing the 16-bit PMI data set with respect to
the data quality of the sensor, calibration and
the aircraft attitude variations (Borstad et al.,
1985). In order to maximize the accuracy of
information extraction, it is important to address
these problems. Figure 1 summarizes the