Polarimetric SAR Features for Vehicle Detection in Natural Surroundings

The increased amount of information measured by fully polarimetric synthetic aperture radar (SAR) give additional knowledge about ground scatterers. Making the best use of the polarimetric information is crucial for target detection, among other applications. Several representations of the data, such as polarimetric decompositions, have been proposed to summarize the information into polarimetric features. The relation between these features with physical properties of the scatterers has been studied in depth. The different approaches to target detection proposed make use of different polarimetric features and different properties of the targets. The goal of this article is twofold: 1) to give a brief review of polarimetric features usually used for target detection, and 2) to combine them optimally for vehicle detection in open fields, in large natural scenes. The study's backbone is a large airborne dataset in
X
-,
S
-, and
L
-bands, in which several flights following different flight tracks were performed around a controlled area with a dozen vehicles. At first, a univariate study is performed to evaluate the contrast provided by individual polarimetric features between vehicles and different types of natural covers. Then, optimal subsets of polarimetric features for distinguishing vehicles in open fields from natural cover are determined using random forest classifiers. The multivariate approach yielded better detection results for all wavelengths but brought more significant improvement as the wavelength increases. At the
X
-band, the total received power is one of the best predictive parameters for vehicle detection while the scattering mechanism characterization becomes more important at
S
- and
L
-bands.