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.

Forest type classification using spaceborne remote sensing is a challenge. Low-frequency Synthetic Aperture Radar (SAR) signals (i.e., P-band, ∼0.69 m wavelength) are needed to penetrate a thick vegetation layer. However, this measurement alone does not guarantee a good performance in forest classification tasks. SAR tomography, a technique employing multiple acquisitions over the same areas to form a three-dimensional image, has been demonstrated to improve SAR’s capability in many applications. Our study shows the potential value of SAR tomography acquisitions to improve forest classification. By using P-band tomographic SAR data from the German Aerospace Center F-SAR sensor during the AfriSAR campaign in February 2016, the vertical profiles of five different forest types at a tropical forest site in Mondah, Gabon (South Africa) were analyzed and exploited for the classification task. We demonstrated that the high sensitivity of SAR tomography to forest vertical structure enables the improvement of classification performance by up to 33%. Interestingly, by using the standard Random Forest technique, we found that the ground (i.e., at 5–10 m) and volume layers (i.e., 20–40 m) play an important role in identifying the forest type. Together, these results suggested the promise of the TomoSAR technique for mapping forest types with high accuracy in tropical areas and could provide strong support for the next Earth Explorer BIOMASS spaceborne mission which will collect P-band tomographic SAR data.

IEEEThis paper presents an approach for Interferometric Synthetic Aperture Radar (InSAR) coherence change analysis. This approach is based on the Change Detection Matrix (CDM) which allows the identification of the presence of changes on the ground. Coherence images are first computed from InSAR image time series (ITS) between each two adjacent dates. The CDM approach is then applied to study the obtained coherence time series with patch-wise analysis. The preliminary results on damage mapping related to the tsunami induced by the eruption of Krakatoa volcano, Sunda Strait, Indonesia on December 22, 2018 using Sentinel-1 InSAR ITS have shown the relevancy of the proposed approach.

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.