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(DOQQs) and ground control points from field surveys.
The PROBE-1 scanner system has a rotating axe-head scan mirror that sequentially generated
crosstrack scan lines on both sides of nadir to form a raster image cube. Incident radiation was
dispersed onto four 32-channel detector arrays. The PROBE-1 data were calibrated to reflectance
by means of a National Institute of Standards (NIS) laboratory radiometric calibration procedure,
providing 128 channels of reflectance data from the visible through the short-wave infrared wave-
lengths (440- 2490 nm). The instrument carried an on-board lamp for recording in-flight radiometric
stability along with shutter-closed (dark current) measurements on alternate scan lines. Geometric
integrity of recorded images was improved by mounting the PROBE-1 on a three-axis, gyro-
stabilized mount, thus minimizing the effects in the imagery of changes in aircraft pitch, roll, and
yaw resulting from flight instability, turbulence, and aircraft vibration. Aircraft position was assigned
using a nondifferential global positioning system (GPS), tagging each scan line with the time,
which was cross-referenced with the time interrupts from the GPS receiver. An inertial measurement
unit added the instrument attitude data required for spatial geocorrection.
During the Pointe Mouillee overflight the PROBE-1 sensor had a 57 instantaneous field of view
(IFOV) for the required mapping of vertical and subvertical surfaces within the wetland. The typical
IFOV of 2.5 mrad along track and 2.0 mrad across track results in an optimal ground IFOV of 5
to 10 m, depending on altitude and ground speed. PROBE-1
data at Pointe Mouillee were collected
on August 29, 2001, at an altitude of 2170 m AGL, resulting in an average pixel size of 5 m
m. The data collection rate was 14 scan lines per second (i.e., pixel dwell time of 0.14 ms), and
the 6.1-km flight line resulted in total ground coverage of 13 km
. The PROBE-1 scene covering
Pointe Mouillee was then georeferenced (RMS error < 0.6 pixel) using the vendor-supplied on-
board GPS data, available DOQQs, and field-based GPS ground control points provided from
August 2001 field surveys. Georeferencing was completed using ENVI image processing software.
The single scene of PROBE-1 data covering Pointe Mouillee was initially visually examined
to remove missing or noisy bands. The resulting 104 bands of PROBE-1 data were then subjected
to a minimum noise fraction (MNF) transformation to first determine the inherent dimensionality
of the image data, segregate noise in the data, and reduce the computational requirements for
subsequent processing (Boardman and Kruse, 1994). MNF transformations were applied as mod-
ified from Green et al. (1988). The first transformation, based on an estimated noise covariance
matrix, decorrelated and rescaled the noise in the data. The second MTF step was a standard
principal components transformation of the “noise-whitened” data. Subsequently, the inherent
dimensionality of the data at Pointe Mouillee was determined by examining the final eigen values
and the associated images from the MNF transformations. The data space was then divided into
that associated with large eigen values and coherent eigen images and that associated with near-
unity eigen values and noise-dominated images. By using solely the coherent portions, the noise
was separated from the original PROBE-1 data, thus improving the spectral processing results of
image classification (RSI, 2001).
A supervised classification of the PROBE-1
Angle Mapper (SAM) algorithm. Because the PROBE-1 flights occurred 3 weeks after
scene was performed using the ENVI
sampling, there was a possibility that trampling from the field crew could have altered the physical
structure of the vegetation stands. For this reason, and due to the inherent georeferencing inaccu-
racies, spectra were collected over a 3
3-pixel area centered on the single pixel with the greatest
percentage of aerial cover and stem density within the vegetation stand (Figure 18.3 and Figure
18.4). The SAM algorithm was then used to determine the similarity between the spectra of
scene by calculating the spectral angle
between them (spectral angle threshold = 0.07 rad). SAM treats the spectra as vectors in an
and other pixels in the PROBE-1
dimensional space equal to the number of bands.
The SAM classification resulted in the detection of 18 image endmembers, each with different
areas mapped as potentially homogeneous regions of dense
. The accuracy of the 18
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