Neural network image fusion with PCA preprocessing

Arthur C. Depoian; Lorenzo E. Jaques; Dong Xie; Colleen P. Bailey; Parthasarathy Guturu

doi:10.1117/12.2588039

12 April 2021 Neural network image fusion with PCA preprocessing

Arthur C. Depoian, Lorenzo E. Jaques, Dong Xie, Colleen P. Bailey, Parthasarathy Guturu

Proceedings Volume 11730, Big Data III: Learning, Analytics, and Applications; 117300K (2021) https://doi.org/10.1117/12.2588039
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

The fusion of multispectral sensor data techniques for sets containing complementary information about the subject of observation leads to the visualization of data into a form more easily interpreted by both humans and algorithms. Many applications of feature-level fusion seek to combine edges and textures, across the bandwidth of the sensory spectrum. Visualization techniques can be skewed by the introduction of corruption and redundancies induced by harmonics. A majority of image fusion techniques rely on intensity hue saturation (IHS) transforms, principal component analysis (PCA), and Gram Schmidt. PCA’s ability to remove the redundancy from a set of correlated data while preserving the variance and its resistance to color distortion lends itself to this application. PCA also has a lower spectral distortion as compared to IHS and has been found to create superior image fusion. The application of neural network control techniques has been shown to more accurately recreate results similar to those found by human inference. Over the years, increased computation power has given rise to the spread of neural networks into roles previously carried out by humans. Select advanced image processing techniques have benefited greatly from their implementation. We propose a novel method of utilizing PCA in conjunction with a neural network to achieve a higher quality of image fusion. Implementation of an autoencoder neural network to fuse this information creates a higher level of data visualization when compared to traditional weighted fusion techniques.

Conference Presentation

Citation Download Citation

Arthur C. Depoian, Lorenzo E. Jaques, Dong Xie, Colleen P. Bailey, and Parthasarathy Guturu "Neural network image fusion with PCA preprocessing", Proc. SPIE 11730, Big Data III: Learning, Analytics, and Applications, 117300K (12 April 2021); https://doi.org/10.1117/12.2588039

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available