  Optical image data have been used by the Remote Sensing workforce to study
land use and cover since such data is easily interpretable. Synthetic Aperture
Radar (SAR) has the characteristic of obtaining images during all-day,
all-weather and provides object information that is different from visible and
infrared sensors. However, SAR images have more speckle noise and fewer
dimensions. This paper presents a method for denoising, feature extraction and
compares classifications of Optical and SAR images. The image was denoised
using K-Singular Value Decomposition (K-SVD) algorithm. A method to map the
extraordinary goal signatures to be had withinside the SAR or Optical image
using support vector machine (SVM) through offering given the enter facts to
the supervised classifier. Initially, the Gray Level Histogram (GLH) and Gray
Level Co-occurrence Matrix (GLCM) are used for feature extraction. Secondly,
the extracted feature vectors from the first step were combined using
correlation analysis to reduce the dimensionality of the feature spaces.
Thirdly, the Classification of SAR images was done in Sparse Representations
Classification (SRC). The above-mentioned classifications techniques were
developed and performance parameters are accuracy and Kappa Coefficient
calculated using MATLAB 2018a.
