Denoising and Optical and SAR Image Classifications Based on Feature Extraction and Sparse Representation

TL;DR

This paper introduces a method combining denoising, feature extraction, and classification techniques for Optical and SAR images, utilizing K-SVD for noise reduction and SVM and SRC for classification, with performance evaluated via accuracy and Kappa coefficient.

Contribution

It presents a novel integrated approach for denoising and classifying Optical and SAR images using sparse representation and feature reduction techniques.

Findings

K-SVD effectively denoised SAR and Optical images.

Feature extraction with GLH and GLCM improved classification.

Support vector machine and sparse representation achieved high accuracy.

Abstract

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.