Autoregressive Model for Multi-Pass SAR Change Detection Based on Image Stacks

TL;DR

This paper introduces an autoregressive model approach for multi-pass SAR change detection, leveraging image stacks over time to improve accuracy in identifying ground scene changes.

Contribution

It proposes using AR models on image stacks for SAR change detection, extending traditional two-image methods to utilize multiple images for better performance.

Findings

AR-based ground scene estimation is accurate

Estimated images improve change detection accuracy

Method effectively utilizes multiple SAR images over time

Abstract

Change detection is an important synthetic aperture radar (SAR) application, usually used to detect changes on the ground scene measurements in different moments in time. Traditionally, change detection algorithm (CDA) is mainly designed for two synthetic aperture radar (SAR) images retrieved at different instants. However, more images can be used to improve the algorithms performance, witch emerges as a research topic on SAR change detection. Image stack information can be treated as a data series over time and can be modeled by autoregressive (AR) models. Thus, we present some initial findings on SAR change detection based on image stack considering AR models. Applying AR model for each pixel position in the image stack, we obtained an estimated image of the ground scene which can be used as a reference image for CDA. The experimental results reveal that ground scene estimates by the AR models is accurate and can be used for change detection applications.