Speaker
Description
Among the problems related to the use of SAR data for target classification purposes, the discrimination of floodwater beneath vegetation still represents a challenge. Double-bounce backscattering is the key process to detect flooded vegetation. When ground is covered by a smooth and very reflective water surface, the intensity of the double-bounce effect involving the surface and vertical structures such as stems, shrubs, or trunks is increased, if the penetration into the canopy is sufficient. This increase can make the backscatter from flooded vegetation significantly higher than that from non-flooded vegetation, so that the former may appear brighter in a SAR image. However, flooded vegetation does not always have a clear radar signature that can be easily detected using SAR intensity (i.e., the backscattering coefficient) because several unknowns (e.g., plant water content, structure and geometry) influence the radar response from this target.
Considering that radar polarimetry is potentially able to discriminate the double-bounce among different scattering mechanisms, it can be worthwhile to investigate the potential of quad-pol SAR data for the classification of flooded areas beneath vegetation. L-band can represent the optimal band from this point of view, thanks to the capability of L-band radiation to penetrate the canopy.
In this study, three ALOS-2 fully polarimetric images of rice fields are analyzed to investigate the added value of L-band polarimetry in detecting the increase of the intensity of the double bounce effect due to the presence of floodwater beneath vegetation. The fields are located in the Vercelli district (Northern Italy) and are periodically flooded and drained in sequence. The images were acquired in spring/summer 2015 (namely May 19th, June 16th and July 14th). The images (fine beam quad products) were multi-looked, geocoded, and calibrated. Polarimetric features were extracted taking advantage of the freely available POLSARpro software developed by the European Space Agency. First, the coherence and covariance matrices were derived. Then, the Freeman decomposition, that models the covariance matrix as the contribution of three scattering mechanisms, namely volume scattering, double-bounce scattering, and surface scattering, was used. Finally, the unsupervised Wishart classifier available through POLSARpro, was applied to classify the polarimetric images.
The classification results obtained by using L-band polarimetric data were compared to those obtained by using also C-band Radarsat polarimetric data, as well as single pol COSMO-SkyMed data. In addition, optical Landsat data were used as benchmark. The major outcomes of the classification results will be presented at the conference.
