An Image Transform Based on Temporal Decomposition

Abstract

Today, very dense synthetic aperture radar (SAR) time series are available through the framework of the European Copernicus Programme. These time series require innovative processing and preprocessing approaches including novel speckle suppression algorithms. Here we propose an image transform for hypertemporal SAR image time stacks. This proposed image transform relies on the temporal patterns only, and therefore fully preserves the spatial resolution. Specifically, we explore the potential of empirical mode decomposition (EMD), a data-driven approach to decompose the temporal signal into components of different frequencies. Based on the assumption that the highfrequency components are corresponding to speckle, these effects can be isolated and removed. We assessed the speckle filtering performance of the transform using hypertemporal Sentinel-1 data acquired over central Germany comprising 53 scenes. We investigated speckle suppression, ratio images, and edge preservation. For the latter, a novel approach was developed. Our findings suggest that EMD features speckle suppression capabilities similar to that of the Quegan filter while preserving the original image resolution.Index Terms-Multitemporal filtering, radar data, speckle, synthetic aperture radar (SAR).

Potential of Multi-Temporal ALOS-2 PALSAR-2 ScanSAR Data for Vegetation Height Estimation in Tropical Forests of Mexico

“…However, using a speckle filter based on a moving window approach strengthens the spatial autocorrelation in the reference data. Nowadays, with the increasing availability of SAR time series (e.g., Sentinel-1), novel approaches that rely on temporal patterns only can be applied to suppress speckle and preserve spatial details without spatial blurring [70]. Furthermore, since the difference in acquisition time between LiDAR and SAR data is between one to five years, significant changes (caused, e.g., by fire or deforestation) within the LiDAR transects might have occurred, which together with forest growth reduce model's predictive performance [71].…”

Wishart-Based Adaptive Temporal Filtering of Polarimetric SAR Imagery

“…As can be seen, the speckle is virtually absent from the filtered image, and land-cover boundaries and features are more easily discerned. In Figure 2, the ATSF is compared with refined Lee [2], Gamma MAP [3], and Frost [4] spatial adaptive filters, as well as with the empirical mode decomposition (EMD) temporal filter [9] and an average over the full time series. The time average is essentially the Quegan filter [21] for uncorrelated images with the same numbers of looks and equal means.…”

“…Many adaptive spatial filtering methods have been developed in the past, some of the most popular of which are described in [1][2][3][4][5][6][7][8]. In addition, temporal [9] and hybrid temporal [10] methods have been proposed for time series of SAR imagery. In this paper, we describe a straightforward adaptive temporal filter, which is essentially a by-product of a sequential change detection algorithm for times series of polarimetric SAR images [11][12][13].…”

Linking scales and disciplines: an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management

“…Further emphasis is on computational approaches taking advantage of and adding value to publicly available satellite imagery such as data from NASA's Landsat missions and ESA's Sentinel (Copernicus) programme (e.g. Cremer et al 2018;Urban et al 2018). The resulting methods are used to derive land surface parameters related to the status and dynamics of South Africa's terrestrial ecosystems (e.g.…”