A Methodical Block based Feature Level Image Fusion Technique with Wavelet Transform using Neural Network for Satellite Images
T. Prabhakara Rao1, B. Rama Rao2
1T. Prabhakara Rao, Research Scholar, Shri Venkateswara University, Gajraula (Uttar Pradesh), India.
2Dr. B. Rama Rao, Professor, Shri Venkateswara University, Gajraula (Uttar Pradesh), India.
Manuscript received on 16 August 2019 | Revised Manuscript received on 28 August 2019 | Manuscript Published on 06 September 2019 | PP: 465-474 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F10970886S19/19©BEIESP | DOI: 10.35940/ijeat.F1097.0886S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.
Keywords: Image Fusion, DWT, Neural Network, Block Based Features, Performance Measures, Discrete Wavelet Transform, Neural Network.
Scope of the Article: Image Security