Peer-Reviewed Articles
1025 Image-Based Atmospheric Corrections-Revisited and Improved
Pat S. Chavez, Jr.
Abstract
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To generate acceptable radiometric correction results, a model is required
that typically uses in-situ atmospheric measurements and radiative transfer
code (RTC) to correct for atmospheric effects. The optimum radiometric correction
procedure is one based solely on the digital image and requiring no in-situ
field measurements during the satellite overflight. The dark-object subtraction
(DOS) method, a strictly image-based technique, is an attempt to achieve
this ideal procedure. However, the accuracy is not acceptable for many applications,
mostly because it corrects only for the additive scattering effect and not
for the multiplicative transmittance effect. This paper presents an entirely
image- based procedure that expands on the DOS model by including a simple
multiplicative correction for the effect of atmospheric transmittance.
1037 Restoration of Corrupted Optical Fuyo-1 (JERS-1) Data Using Frequency
Domain Techniques
C.R. de Souza Filho, S.A. Drury, A.M. Denniss, R.W.T.
Carlton, and D.A. Rothery
Abstract
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The image data collected by Fuyo-1's sensors covering the visible and the short
wave infrared (SWIR) are affected by severe noise problems. The important
narrow SWIR channels show the worst defects. Fourier analysis is used to
characterize these artifacts in the frequency domain. A scene-dependent Fourier
operator that is able to eliminate the major noise components is described.
This involves the construction of a binary mask derived from the difference
between the Fourier spectra of two channels containing noise signals at similar
frequencies and amplitude. This mask is used to modulate frequency domain
images, so removing all noise components while preserving real image data
with minimum loss and distortion in the spatial domain. Fuyo-1 brightness
saturation problems can also be minimized by applying a Gaussian contrast
stretch to the Fourier spectra prior to image inversion.
1049 Registration Techniques for Multisensor Remotely Sensed Images
Leila M.G. Fonseca and B.S. Manjunath
Abstract
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With the increase in the number of images collected every day from different
sensors, automated registration of multisensor/multispectral images has become
a very important issue. Given the density of the data, it is unlikely that
a single registration scheme will work satisfactorily for all different applications.
A possible solution is to integrate multiple registration algorithms into
a rule-based artificial intelligence system so that appropriate methods for
any given set of multisensor data can be automatically selected. The first
step in the development of such an expert system for remote sensing application
would be to obtain a better understanding and characterization of the various
existing techniques for image registration. This is the main objective of
this paper as we present a comparative study of some recent image registration
methods.
1057 The Use of Multiresolution Analysis and Wavelets Transform for Merging
SPOT Panchromatic and Multispectral Image Data
Bruno Garguet-Duport, Jacky Girel, Jean-Marc Chassery, and Guy
Patou
Abstract
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Several techniques have been developed to merge SPOT 10-m resolution panchromatic
data (SPOT P) with simultaneously acquired 20-m resolution multispectral
data (SPOT XS). A method allowing the use of 10-m resolution XS data while
conserving the spectral properties of the original 20-m data is presented.
This method uses a multiresolution analysis procedure based upon the wavelets
transform; it is applied to remotely sensed SPOT P and SPOT XS images of
the river junction Arc-Isere (France). This new method is compared with the
IHS method and P+XS method. The wavelets method is the one which least distorts
the spectral characteristics of the data. The distortions are minimal and
difficult to detect.
1067 Multiresolution Wavelet Decomposition Image Merger of Landsat Thematic
Mapper and SPOT Panchromatic Data
David A. Yocky
Abstract
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Spatially registered Landsat Thematic Mapper (TM) and SPOT panchromatic images
were merged by combining multiresolution wavelet decomposition components
from each, and then reconstructing a merged image using the inverse wavelet
transform. Three wavelet merging techniques were compared to the intensity-
hue-saturation merging technique. The comparison results show the wavelet
merger providing greater flexibility and the potential for higher accuracy
for combining and preserving spectral-spatial information for remotely sensed
data and their applications.
1075 Multispectral Imagery Band Sharpening Study
Jim Vrabel
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The fusion of multisensor and multiresolution satellite imagery is an effective
means of exploiting the complimentary nature of various image types. With
band sharpening (a type of imagery fusion), higher spatial resolution panchromatic
data is fused with lower resolution multispectral imagery (MSI). This function
creates a product with the spectral characteristics of the MSI and a spatial
resolution approaching that of the panchromatic image (effective ground-
sample distance, or GSD). MSI of 10 m to 30 m was sharpened with 5- to 15-m
imagery (sharpening factors of 2:1 to 6:1) using four algorithms. The research
goals were to determine the validity of the concept of 'effective GSD'; determine
the relative utility of band sharpening by different factors; and compare
the relative effectiveness of different band sharpening algorithms.
1085 An Empirical Investigation of Image Resampling Effects Upon the Spectral
and Textural Super-vised Classification of a High Spatial Resolution Multispectral
Image
O. Dikshit and D.P. Roy
Abstract
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Spectral and textural maximum-likelihood classifications are performed upon
unresampled and upon bilinear and cubic convolution resampled versions of
the image. The texture classifications use spectral training data and additional
texture training data calculated using the grey-level difference histogram
algorithm. The resampling algorithms increase the overall classification
accuracies in a statistically significant manner which vary between individual
classes. These results are explained by consideration of the interaction
between the local smoothing properties of the resampling algorithms and the
grey-level structure of the image. The results indicate that spectral and
textural classification procedures may be applied to images, after they have
been resampled, without a reduction in the classification accuracy, and that
textural classification procedures should use class training statistics collected
from the resampled image.
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