A NOVEL REMOTE SENSING IMAGE FUSION METHOD BASED ON ICA BASES
seminar project explorer Active In SP Posts: 231 Joined: Feb 2011 
11022011, 06:54 AM
A NOVEL REMOTE SENSING IMAGE FUSION METHOD BASED ON ICA BASES
Submitted by MANU C.S Third Semester M.Tech , Signal Processing A NOVEL REMOTE SENSING IMAGE FUSION METHOD BASED ON ICA BASES.pdf (Size: 1,007.12 KB / Downloads: 128) ABSTRACT Data fusion on remote sensing is one of important problems in current image processing techniques, as the remote sensing data is massive, highorder redundant and nonnegative. 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 IntensityHueSaturation(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, highorder redundant and nonnegative. 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'. Multispectral image A Multispectral 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. Multispectral 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 spacebased imaging. Multispectral 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 redgreenblue (RGB) region, and going to infra red wavelengths of 0.7 µm to 10 or more µm, classified as NIRNear InfraRed, MIRMiddle InfraRed and FIRFar 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 nonGaussian source signals. It is a special case of blindsource 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 nonGaussianity 16 The NonGaussianity family of ICA algorithms, motivated by the central limit theorem, uses kurtosis and negentropy. The MinimizationofMutual information (MMI) family of ICA algorithms uses measures like KullbackLeibler Divergence and maximumentropy. 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 vectorvalued representation of each data vector such that it gets uniquely encoded by the resulting code vector (lossfree coding), but the code components are statistically independent. 


Guest Thinking To Register 
01042013, 12:44 AM
Hello Explorer,
How is the fusion done here ? 


