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Deconvolving and improving the spatial resolution of satellite data using the Maximum Entropy Method

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Jackett, CJ (2013) Deconvolving and improving the spatial resolution of satellite data using the Maximum Entropy Method. PhD thesis, University of Tasmania.

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Abstract

Remote sensing satellite imagery provides information about the surface of the Earth
at a range of spectral bands and spatial resolutions. This information is a valuable
resource for the management of terrestrial and marine environments. During the
capturing process, incoming light is reflected or refracted by the instrument optics
which causes a small amount of blurring. This effect is described by a mathematical
operation called convolution in which the satellite input radiance field is convolved
with the instrument Point Spread Function (PSF). This form of instrumental
distortion has the largest impact on high-contrast scenes where bright land or clouds
are adjacent to dark surfaces such as water.
This thesis investigates three mechanisms for improving the quality of recorded
satellite data. An efficient convolution method was developed to minimise boundary
effects, a deconvolution algorithm was used to remove instrumental distortion,
and a resolution enhancement algorithm was developed to improve the spatial
resolution of input images. The latter two of these problems are underdetermined
and require appropriately selected constraints in order to find unique and stable
solutions. An entropy-based method was chosen as the constraint element due to
its heavy grounding in statistical mechanics and information theory. MODerate
resolution Imaging Spectroradiometer (MODIS) Aqua images were used to quantify
the improvement of these algorithms, with a focus on coastal marine and open-ocean
environments.
Deconvolution is an algorithm-based process designed to reverse convolution
effects with a known PSF. Multiscale Entropy deconvolution was applied to MODIS
level 1A imagery to remove instrumental distortion from top-of-atmosphere radiance
counts. Removing these effects at the beginning of the satellite image processing chain reduces the propagation and amplification of errors in subsequent processing
stages. Wavelet transforms were implemented to decompose images into a range
of resolution levels that represent different spatial frequencies. This allows both
large-scale and small-scale features to be resolved simultaneously. Multiresolution
Support images were used to accurately define and target important areas within the
imagery. The combination of these techniques includes two-dimensional structural
information in the Multiscale Entropy calculation which results in accurate
deconvolution. Validation of the Multiscale Entropy deconvolution algorithm was
undertaken using in-situ measurements from the Baltic Sea and a QuickBird image
of a high-contrast Antarctic ice edge.
A novel approach to the spatial resolution enhancement of MODIS imagery
uses information about the optical PSF, along with the result of Multiscale
Entropy deconvolution. With this information, a system of linear equations
was constructed that models how high-resolution PSF convolution redistributes
information over a finite area. A new method termed Multiresolution Entropy
was developed to constrain the linear system and retrieve an optimal solution.
The algorithm successfully improved the spatial resolution of input images and
compared favourably to other interpolation-based methods. The key requirement of
this technique is to obtain high-resolution PSF measurements at the same sampling
frequency as the desired final output resolution.
The techniques developed and presented in this thesis contain a range of
important research contributions. The combination of Fast Fourier Transform
convolution with a boundary renormalisation approach produces an efficient and
accurate convolution method with minimal boundary effects. A multi-detector
convolution process accurately simulates the MODIS Aqua instrumentation and
allows for successful deconvolution. A detector saturated estimation technique
for ocean colour bands ensures the correct quantity of instrumental distortion is
removed during deconvolution. The formulation of a linear system consisting of highresolution
PSF modelling and appropriate physical constraints defines the spatial
resolution enhancement problem. The development of Multiresolution Entropy
targets high-frequency content, constrains the linear system and results in a unique and stable resolution-enhanced solution. The techniques developed throughout this
thesis provide considerable benefit to the quality of remote sensing imagery and can
substantially improve the monitoring and management of coastal zones and other
marine environments.

Item Type: Thesis (PhD)
Keywords: convolution, deconvolution, spatial resolution enhancement
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Date Deposited: 11 Feb 2014 01:03
Last Modified: 15 Sep 2017 01:06
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