Multi Sensor Image Fusion Using Stationary Wavelet Transform and Feature Extraction from Gabor Filter and GLCM Texture

Abstract

Image Fusion is a process of adding information obtained from various sensors and intelligent systems. This provides an image which containing complete information. In this process we fuse images of the same scene one is Infrared Image and other is visible image to produce an image that contains more information. In this the Infrared which are low resolution and noisy nature of image and visual image fused using Stationary wavelet transfer algorithm. In this we have used Gabor filter and GLCM to extract feature and compared the two feature extraction method using quality matrix parameters and found which method is the best method of fusion. The fusion is used in satellite image fusion which mostly used in maps also for decision-making process involved in interpreting images from multi sensor data. Images of several different targets (a military vehicle, a wood chipper, a pickup truck, and people) were used to assess how human subjects view and interpret different types of images. The premise is that combining complementing data from several sensors will result in more accurate findings for data processing difficulties. Although significant progress has been achieved in this sector, complete modeling of the human brain remains a distant objective.