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ABSTRACT


Data fusion on remote sensing is one of important problems in current image
processing techniques, as the remote sensing data is massive, high-order
redundant and non-negative. The image fusion is an important branch of data
fusion. The image fusion can be viewed as a process that incorporates essential
information from different modality sensors into a composite image. Traditional
image fusion methods like Intensity-Hue-Saturation(IHS) transform, Principal
Component Analysis(PCA), highpass filtering(HPF) and etc. have the disadvantage
that they often lose the spectral(or high frequency) information in the process
and lead to significant color distortion in the fused image. The use of based
trained Independent Component Analysis(ICA) for image fusion is a recent much
efficient technique with increases the resolution of fused output image by a great
deal. Use of Discrete Wavelet Transform(DWT) for image fusion is found to
preserve the spectral quality in the output fused image Also, lifting scheme
based DWT is found to have a much faster computation speed. So using lifting
based DWT for mutliresolution analysis and ICA together is used for making a
more faster and efficient algorithm for remote sensing image fusion.


CHAPTER- 1
INTRODUCTION


Data fusion on remote sensing is one of important problems in
current image processing techniques, as the remote sensing data is massive,
high-order redundant and non-negative. The image fusion is an important
branch of data fusion. The image fusion can be viewed as a process that
incorporates essential information from different modality sensors into a
composite image. In remote sensing, fusion process synthesizes images with
the spectral resolution of the multispectral images and the spatial resolution
of the panchromatic image and referred to as 'Pan sharpening'.

Multi-spectral image
A Multi-spectral image is one that captures image data at specific
frequencies across the electromagnetic spectrum. The wavelengths may be
separated by filters or by the use of instruments that are sensitive to
particular wavelengths, including light from frequencies beyond the visible
light range, such as infrared. Multi-spectral imaging can allow extraction of
additional information that the human eye fails to capture with its receptors
for red, green and blue. It was originally developed for space-based imaging.
Multi-spectral images are the main type of images acquired by Remote
sensing (RS) radiometers. Dividing the spectrum into many bands, multi-
spectral is the opposite of panchromatic which records only the total
intensity of radiation falling on each pixel. Usually satellites have 3 to 7 or
more radiometers (Landsat has 7). Each one acquires one digital image (in
remote sensing, called a scene) in a small band of visible spectra, ranging
0.7 µm to 0.4 µm, called red-green-blue (RGB) region, and going to infra-
red wavelengths of 0.7 µm to 10 or more µm, classified as NIR-Near
InfraRed, MIR-Middle InfraRed and FIR-Far InfraRed or Thermal.

CHAPTER- 2
DISCRETE WAVELET TRANSFORM


A Discrete Wavelet Transform (DWT) is any wavelet transform for
which the wavelets are discretely sampled. As with other wavelet transforms,
a key advantage it has over Fourier transforms is temporal resolution: it
captures both frequency and location information (location in time). The first
DWT was invented by the Hungarian mathematician, Alfred Haar. For an
input represented by a list of 2n
numbers, the Haar wavelet transform may be
considered to simply pair up input values, storing the difference and passing
the sum.

CHAPTER- 3
INDEPENDENT COMPONENT ANALYSIS

Independent component analysis (ICA) is a computational method
for separating a multivariate signal into additive subcomponents supposing
the mutual statistical independence of the non-Gaussian source signals. It is
a special case of blind-source separation. When the independence
assumption is correct, blind ICA separation of a mixed signal gives very
good results. It is also used for signals that are not supposed to be generated
by a mixing for analysis purposes. A simple application of ICA is the
“cocktail party problem”, where the underlying speech signals are separated
from a sample data consisting of people talking simultaneously in a room.
Usually the problem is simplified by assuming no time delays or echoes. An
important note to consider is that if N sources are present, at least N
observations (e.g. microphones) are needed to get the original signals. This
constitutes the square (J = D, where D is the input dimension of the data and
J is the dimension of the model). Other cases of underdetermined (J < D)
and overdetermined (J > D) have been investigated.

Defining component Independence

ICA finds the independent components (aka factors, latent variables or
sources) by maximizing the statistical independence of the estimated components.
We may choose one of many ways to define independence, and this choice governs
the form of the ICA algorithms. The two broadest definitions of independence for
ICA are
1) Minimization of Mutual Information
2) Maximization of non-Gaussianity 16

The Non-Gaussianity family of ICA algorithms, motivated by the central limit
theorem, uses kurtosis and negentropy. The Minimization-of-Mutual information
(MMI) family of ICA algorithms uses measures like Kullback-Leibler Divergence
and maximum-entropy.
Typical algorithms for ICA use centering, whitening (usually with the eigenvalue
decomposition), and dimensionality reduction as preprocessing steps in order to
simplify and reduce the complexity of the problem for the actual iterative
algorithm. Whitening and dimension reduction can be achieved with principal
component analysis or singular value decomposition. Whitening ensures that all
dimensions are treated equally a priori before the algorithm is run. Algorithms for
ICA include infomax, FastICA, and JADE, but there are many others also.
In general, ICA cannot identify the actual number of source signals, a uniquely
correct ordering of the source signals, nor the proper scaling (including sign) of the
source signals.
ICA is important to blind signal separation and has many practical applications. It
is closely related to (or even a special case of) the search for a factorial code of the
data, i.e., a new vector-valued representation of each data vector such that it gets
uniquely encoded by the resulting code vector (loss-free coding), but the code
components are statistically independent.