Thu Trang Le

Adaptive Multitemporal SAR Image Filtering Based on the Change Detection Matrix

This letter presents an adaptive filtering approach of synthetic aperture radar (SAR) image times series based on the analysis of the temporal evolution. First, change detection matrices (CDMs) containing information on changed and unchanged pixels are constructed for each spatial position over the time series by implementing coefficient of variation (CV) cross tests. Afterward, the CDM provides for each pixel in each image an adaptive spatiotemporal neighborhood, which is used to derive the filtered value. The proposed approach is illustrated on a time series of 25 ascending TerraSAR-X images acquired from November 6, 2009 to September 25, 2011 over the Chamonix-Mont-Blanc test-site, which includes different kinds of change, such as parking occupation, glacier surface evolution, etc.

Wavelet Operators and Multiplicative Observation Models—Application to SAR Image Time-Series Analysis

This paper first provides statistical properties of wavelet operators when the observation model can be seen as the product of a deterministic piecewise regular function (signal) and a stationary random field (noise). This multiplicative observation model is analyzed in two standard frameworks by considering either: 1) a direct wavelet transform of the model; or 2) a log-transform of the model prior to wavelet decomposition. The paper shows that, in Framework 1, wavelet coefficients of the time series are affected by intricate correlation structures which blur signal singularities. Framework 2 is shown to be associated with a multiplicative (or geometric) wavelet transform, and the multiplicative interactions between wavelets and the model highlight both sparsity of signal changes near singularities (dominant coefficients) and decorrelation of speckle wavelet coefficients. This paper then derives that, for time series of synthetic aperture radar data, geometric wavelets represent a more intuitive and relevant framework for the analysis of smooth earth fields observed in the presence of speckle. From this analysis, this paper proposes a fast-and-concise geometric-wavelet-based method for joint change detection and regularization of synthetic aperture radar image time series. In this method, geometric wavelet details are first computed with respect to the temporal axis in order to derive generalized-ratio change images from the time series. The changes are then enhanced, and speckle is attenuated by using spatial block sigmoid shrinkage. Finally, a regularized time series is reconstructed from the sigmoid shrunken change images. Some applications highlight relevancy of the method for the analysis of SENTINEL-1A and TerraSAR-X image time series over Chamonix Mont Blanc.

Change analysis of dual polarimetric Sentinel-1 SAR image time series using stationary wavelet transform and change detection matrix

This paper provides initial change detection results on a time series of 11 dual polarimetric IW level-1 single look complex (SLC) Sentinel-1 SAR images acquired in descending pass over Chamonix Mont-Blanc, France. The changed and unchanged information in the time series is identified in the change detection matrix (CDM) constructed by similarity cross tests between wavelet extracted features issued from stationary wavelet transform (SWT). The analysis from multiscale decomposition allows the separation of multidate pixel changes in low frequencies from changes in high frequencies. The index of change dynamics derived from CDM enables to analyze the temporal evolution of the observed area.

Adaptive multitemporal filtering of polarimetric SAR images

This paper proposes an approach for temporal adaptive filtering of Polarimetric Synthetic Aperture Radar (PolSAR) image time series by integrating a change detection technique. The filtering strategy is based on the detection of changed and unchanged areas derived by applying an appropriate similarity test. A time series including 7 descending fine-quad polarization RADARSAT2 images acquired from January 29, 2009 to Jun 22, 2009 over Chamonix-MontBlanc test-site which includes different kinds of change is used to validate the proposed method.

Change analysis using multitemporal Sentinel-1 SAR images

This paper presents a method for analyzing SAR image time series and provides initial change detection results on a time series of 11 descending Interferometric Wide Swath (IW) Level-1 Single Look Complex (SLC) Sentinel-1 SAR images over Chamonix-Mont-Blanc, France. This method is based on the Change Detection Matrix (CDM) which identifies the presence of changes in the time series. It provides a useful information to gather homogeneous samples for spatio-temporal speckle filtering and to obtain a map of change dynamics in order to reveal the temporal evolution.

Vector and matrix LP norms in polarimetric radar filtering

The paper addresses multi-channel complex image filtering. It provides regularization cost functions associated to non-conventional vector and matrix iv norms for promoting geometry properties. The approach is shown to be efficient for filtering PolSAR images.