Multitemporal Level- $1\beta$ Products: Definitions, Interpretation, and Applications

Abstract

In this paper, we present a new framework for the fusion, representation, and analysis of multitemporal SAR data. It leads to the definition of a new class of products representing an intermediate level between the classic Level-1 and Level-2 products. The proposed Level-1β products are particularly oriented toward non-expert users. In fact, their principal characteristics are the interpretability and the suitability to be processed with standard algorithms. The main innovation of the paper is the design of a suitable RGB representation of data aiming to enhance the information content of time-series. The physical rationale of the products is presented through examples in which we show their robustness with respect to sensor, acquisition mode and geographic area. A discussion about the suitability of the proposed products with Sentinel-1 imagery is also provided, showing the full compatibility with data acquired by the new ESA sensor. Finally, we propose two applications based on the usage of Kohonen's self-organizing maps dealing with classification problems.

RGB SAR products: methods and applications

“…In Figure 3, the general scheme of the proposed framework (we called MAP3) is depicted (see Amitrano et al, [2015] and Amitrano et al [2016] for details). It is composed by three blocks of activities: pre-processing chain, adaptive processing chain and representation/ analysis chain.…”

“…However, it is possible to combine these images in a unique RGB frame taking into account of some temporal indicators, such as the variance, the mean backscattering, the mean interferometric coherence, and the normalized saturation index [Amitrano et al, 2016]. In such way, a classification-oriented product can be obtained.…”

“…The result of this processing is depicted in Figure 6. We will refer to it as Level-1β product [Amitrano et al, 2016]. The red band is reserved to the time series variance.…”

“…This image is more easily understandable, at least for people having a technical expertise with SAR imagery. It is quite clear that one of the most annoying problems handling SAR data is the grayscale displaying, having humans the habit to deal with color images [Jacobson et al, 2007], [Amitrano et al, 2016]. This scenario is further complicated by the scarce availability of commercial/open source software suites implementing ad hoc algorithms for data analysis.…”

Earth Environmental Monitoring Using Multi-Temporal Synthetic Aperture Radar: A Critical Review of Selected Applications

“…Despite their scarce diffusion in the end-user community, SAR data can enable several applications, especially in near-real time, or give fundamental contribution through data assimilation in geophysical/hydrological/weather models and/or integration with multisource information. Due to the variety of the field, a special focus will be dedicated to some applications related to biosphere and hydrosphere monitoring, while many others dealing, as an example, with the urban environment [10][11][12], classification [13][14][15] or the estimation of small [16][17][18] and large [19][20][21] displacements will be only eventually mentioned.…”