Wavelet Spatio-Temporal Change Detection on multi-temporal PolSAR images

TL;DR

This paper presents WECS, an unsupervised wavelet-based method for detecting spatio-temporal changes in multi-temporal SAR and high-resolution images, demonstrated on synthetic data and real satellite image sequences.

Contribution

The paper introduces WECS, a novel wavelet energy correlation screening technique for change detection in multi-temporal SAR images, capable of identifying both sudden and gradual changes.

Findings

Effective detection of change points in synthetic data.

Successful application to real satellite image time series.

Identifies both abrupt and cumulative changes.

Abstract

We introduce WECS (Wavelet Energies Correlation Sreening), an unsupervised sparse procedure to detect spatio-temporal change points on multi-temporal SAR (POLSAR) images or even on sequences of very high resolution images. The procedure is based on wavelet approximation for the multi-temporal images, wavelet energy apportionment, and ultra-high dimensional correlation screening for the wavelet coefficients. We present two complimentary wavelet measures in order to detect sudden and/or cumulative changes, as well as for the case of stationary or non-stationary multi-temporal images. We show WECS performance on synthetic multi-temporal image data. We also apply the proposed method to a time series of 85 satellite images in the border region of Brazil and the French Guiana. The images were captured from November 08, 2015 to December 09 2017.