An Efficient Block based Feature Level Image Fusion Technique using Wavelet Transform and Neural Network

We presented 3D fusion technique based on wavelet transform for analyzing 3D dataset of gravity and magnetic inversion intuitively and comprehensively. The technique expands the conventional 2D image fusion technique based on wavelet transform to 3D case, including using 3D wavelet decomposition and reconstruction to replace 2D ones and reforming the fusion rules of high and low frequency components in 3D field. The disciplines of some crucial parameters related to the 3D fusion technique were provided, so that bring some convenient to use this techinique. The synthetic data test showed that the 3D fusion technique is effetive and reliable.

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Performance of Spatial Frequency Discrete Wavelet Transform Based Remote Sensing Image Fusion Algorithm

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6402.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 7968-7978

Keyword(s):

Remote Sensing ◽

Wavelet Transform ◽

Spatial Frequency ◽

Image Fusion ◽

Discrete Wavelet Transform ◽

Discrete Wavelet ◽

Fusion Algorithm ◽

Fusion Technique ◽

Image Fusion Technique ◽

Better Than

The high sensor cost for producing images with superior spectral and spatial qualities in remote sensing application have led to the development of image fusion algorithms. Image fusion technique combines a Panchromatic image and a Multispectral image with an aim to produce images with excellent spatial and spectral qualities. One of the major factors that affect the performance of any image fusion algorithm is the capability of the algorithm in extracting the spatial and spectral data from the respective images and how effective the so extracted information is blended together. One of the recently developed spectral domain algorithm to perform image fusion in remote sensing applications is Spatial Frequency Discrete Wavelet Transform abbreviated as SFDWT. The excellence of SFDWT image fusion algorithm is already proven better than the prevailing algorithms based on Discrete Wavelet Transform. This paper is coined with an eye to realize the performance of SFDWT based image fusion algorithm with respect to IHS-DWT, which being an enhanced form of a typical DWT based image fusion algorithm. The performance of SFDWT and IHS-DWT based image fusion algorithms will be evaluated by applying both techniques in the fusion of urban images received from Pléiades sensors with 1:4 resolution ratio using qualitative and quantitative image quality assessment methods. The consequence of varying the decomposition level on the quality of the images produced using SFDWT image fusion technique and three variants of IHS-DWT techniques based on substitution, averaging and maximum selection will be also evaluated. From the experimental analysis done using MATLAB simulation, it will be vivid that images obtained using image fusion algorithm based on SFDWT are much better than that obtained using IHS-DWT technique with excellent spatial and spectral qualities

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A Methodical Block based Feature Level Image Fusion Technique with Wavelet Transform using Neural Network for Satellite Images

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1097.0886s19 ◽

2019 ◽

Vol 8 (6S) ◽

pp. 465-474

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Image Fusion ◽

Back Propagation ◽

Image Data ◽

Back Propagation Neural Network ◽

Discrete Wavelet ◽

Learning Capability ◽

Feed Forward Back Propagation ◽

Block Based

This paper describe about the feature extraction or detection machine learning application which one is wavelet transform integrated with neural network. It has obtained an effective block based feature level with wavelet transform using neural network (BFWN) model for image fusion. In the projected BFWN model, the discrete wavelet transform (DWT) and neural network (NN) are considered for fusing IRS-1D images using LISS- III scanner about the location different areas in India. Also Quick Bird image data and Landsat 7 image data are used to carry out on the proposed BFWN method. The characteristics like contrast visibility, energy of gradient, spatial frequency, variance and edge information are under study. A Feed forward back propagation neural network is trained and tested for categorization since the learning capability of neural network makes it feasible to customize the image fusion process. The trained neural network is used to fuse the two source images. The proposed BFWN model is distinguish, with DWT alone to assess the quality of the fused image. The results obviously show that the proposed BFWN model is a capable and feasible algorithm for image fusion

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