Multitemporal SAR images change detection and visualization using RABASAR and simplified GLR

TL;DR

This paper introduces a novel multitemporal SAR change detection framework combining RABASAR denoising, a simplified GLR method, and enhanced visualization techniques, improving accuracy in land surface change monitoring.

Contribution

It presents a new simplified generalized likelihood ratio method and an integrated change detection and visualization pipeline for multitemporal SAR images, enhancing change detection accuracy.

Findings

Effective detection of farmland, building, harbour, and flooding changes.

Improved change detection accuracy compared to classical methods.

Successful application to simulated and real SAR data.

Abstract

Understanding the state of changed areas requires that precise information be given about the changes. Thus, detecting different kinds of changes is important for land surface monitoring. SAR sensors are ideal to fulfil this task, because of their all-time and all-weather capabilities, with good accuracy of the acquisition geometry and without effects of atmospheric constituents for amplitude data. In this study, we propose a simplified generalized likelihood ratio ($S_{GLR}$) method assuming that corresponding temporal pixels have the same equivalent number of looks (ENL). Thanks to the denoised data provided by a ratio-based multitemporal SAR image denoising method (RABASAR), we successfully applied this similarity test approach to compute the change areas. A new change magnitude index method and an improved spectral clustering-based change classification method are also developed. In addition, we apply the simplified generalized likelihood ratio to detect the maximum change magnitude time, and the change starting and ending times. Then, we propose to use an adaptation of the REACTIV method to visualize the detection results vividly. The effectiveness of the proposed methods is demonstrated through the processing of simulated and SAR images, and the comparison with classical techniques. In particular, numerical experiments proved that the developed method has good performances in detecting farmland area changes, building area changes, harbour area changes and flooding area changes.