This paper explores the problem of change detection in time series of heterogeneous multivariate synthetic aperture radar images. Classical change detection schemes have modelled the data as a realisation of Gaussian random vectors and have derived statistical tests under this assumption. However, when considering high-resolution images, the heterogeneous behaviour of the scatterers is not well described by a Gaussian model. In this paper, the data model is extended to Spherically Invariant Random Vectors where the heterogeneity of the images is accounted for through a deterministic texture parameter. Then three separate detection problems are considered and generalised likelihood ratio test technique is used to derive statistical tests for each problem. The constant false alarm rate property of the new statistics are studied both theoretically and through simulation. Finally, the performance of the new statistics are studied both in simulation and on real synthetic aperture radar data and compared to Gaussian-derived ones. The study yields promising results when the data are heterogeneous.

High resolution in synthetic aperture radar (SAR) leads to new physical characterizations of scatterers which are anisotropic and dispersive. These behaviors present an interesting source of diversity for target detection schemes. Unfortunately, such characteristics have been integrated and have been naturally lost in monovariate single-look SAR images. Modeling this behavior as nonstationarity, wavelet analysis has been successful in retrieving this information. However, the sharp-edge of the used wavelet functions introduces undesired high side-lobes for the strong scatterers present in the images. In this paper, a new family of parameterized wavelets, designed specifically to reduce those side lobes in the SAR image decomposition, is proposed. Target detection schemes are then explored using this spectro-angular diversity and it can be shown that in high-resolution SAR images, the non-Gaussian and robust framework leads to better results.

We introduce the WECS (Wavelet Energies Correlation Screening), an unsupervised method to detect spatio-temporal changes on multitemporal SAR images. The procedure is based on wavelet approximation for the multitemporal images, wavelet energy apportionment, and ultra-high dimensional correlation screening for the wavelet coefficients. We show WECS's performance on simulated multitemporal image data. We also evaluate the proposed method on a time series of 85 Sentinel-1 images of a forest region at the border of Brazil and French Guiana. Comparisons with well-known change detection methods found in the literature highlight the proposal's superiority in terms of change detection accuracy. Additionally, the introduced method has simple architecture and low computational cost.

The detection of wet snow by satellite imaging is currently done in an unsupervised way and lacks quantitative evaluation due to the difficulty of collecting ground truths in extreme environments. In this paper, we propose to take into account information associated with a physical model to label satellite data for the purpose of supervised learning of snow properties using Synthetic Aperture Radar (SAR) imagery. This dataset is constructed from Sentinel-1 SAR images, augmented with topographic information obtained from a Digital Elevation Model (DEM). The labelling of this data is done at the scale of the Northern Alps using the CROCUS physical snow model. Then, we trained, over thirteen combinations of labelled dataset, a wide range of machine learning models to quantitatively identify the most relevant learners for the wet snow detection task. The results demonstrate consistency among the different algorithms, with significant improvement observed when incorporating polarimetric combinations and topographic orientation data in the input of the model. The best algorithmic solution trained on this dataset is evaluated by comparing the obtained wet snow map over a validation area in the French massif of the Grandes Rousses with the existing Copernicus products, Fractional Snow Cover (FSC) and SAR Wet Snow (SWS). We also compare the temporal results obtained at one meteorological station located in the test area. The results show a better representation of wet snow during the melting period using the supervised learning approach, as well as a reduction in areas classified as wet during the winter season.