Recognition of Changes in SAR Images Based on Gauss-Log Ratio and MRFFCM

TL;DR

This paper introduces a novel SAR image change detection method combining Gauss-log ratio, speckle noise reduction, and an improved MRFFCM algorithm, demonstrating superior performance over existing techniques on simulated and real data.

Contribution

It proposes a modified MRFFCM approach with a new change detection pipeline for SAR images, enhancing accuracy and robustness.

Findings

Proposed method outperforms FCM and MRFFCM in accuracy.

Shows improved error metrics and noise resilience.

Effective on both simulated and real SAR images.

Abstract

A modified version of MRFFCM (Markov Random Field Fuzzy C means) based SAR (Synthetic aperture Radar) image change detection method is proposed in this paper. It involves three steps: Difference Image (DI) generation by using Gauss-log ratio operator, speckle noise reduction by SRAD (Speckle Reducing Anisotropic Diffusion), and the detection of changed regions by using MRFFCM. The proposed method is compared with existing methods such as FCM and MRFFCM using simulated and real SAR images. The measures used for evaluation includes Overall Error (OE), Percentage Correct Classification (PCC), Kappa Coefficient (KC), Root Mean Square Error (RMSE), and Peak Signal to Noise Ratio (PSNR). The results show that the proposed method is better compared to FCM and MRFFCM based change detection method.