2021/06/03 by Battula Balnarsaiah, Balnarsaiah, Battula, G. Rajitha +2
Computer Science · Engineering · #Advanced Image Fusion Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Image and Video Processing (eess.IV) #Remote-Sensing Image Classification #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2106.01896
arxiv created 2021/06/03 · openalex publication_date 2021/06/03 · arxiv updated 2021/06/04 · openalex created_date 2021/06/22 · openalex updated_date 2026/07/28
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.