Satellite Image Processing Using SVM Classifier and ELBP-ML Features: A Review

Abstract: Satellite images in course of capturing and transmitting are frequently degraded due to image effects or uncertain conditions. These effects introduce different noise patterns such as, Additive White Gaussian Noise, Salt & Pepper Noise and Mixed Noise. Therefore, retrieved images are highly noise corrupted because the image contents are more attenuated or amplified. The selection of optimum Image Processing and filtering technique depends to have knowledge about the characteristics of degrading system and noise pattern in an image. In this paper, Machine learning model used with components Support vector machine (SVM) Classifier and Extended Local Binary Patterns (ELBP) is used for Image Processing from highly noise corrupted images. The implementation of proposed methodology is being carried out by estimating the noise patterns of wireless image through configuring System Identification with SVM Classifier. Then, these estimated noise patterns are eliminated by configuring Signal Processing with SVM algorithm. The Processing of satellite images are done on basis new proposed ELBP Processing techniques.

Keywords: ELBP-Extended Local Binary Patterns, Artificial Neural Network, Support vector machine.