Change Detection in SAR Images Using Hypothesis Testing and Shannon Entropy Based on the Rayleigh Distribution

This article presents a change detection algorithm based on Shannon entropy and the Rayleigh distribution for both single-look and multilook synthetic aperture radar (SAR) images. While hypothesis testing has been widely used in SAR change detection, few studies have applied this approach to both types of data. To address this, we propose a method that utilizes Shannon entropy to detect changes between two samples. The performance of the algorithm was evaluated through Monte Carlo simulations using synthetic SAR data and further validated on real-world datasets, including single-look images from the CARABAS II dataset and multilook data from the uninhabited aerial vehicle synthetic aperture radar (UAVSAR). The results demonstrate that the proposed method is effective in detecting changes. This article highlights the versatility of the approach, which is capable of handling both single-look and multilook SAR data, and reinforces the way for future research into alternative entropy measures and probability distributions in change detection tasks.