The increased amount of information measured by fully polarimetric SAR give additional knowledge about ground scatterers. Making the best use of the polarimetric information is crucial for target detection, amongst 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 have 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 paper is two-fold: to give a brief review of polarimetric features usually used for target detection, and to combine them optimally for vehicle detection in open field, in large natural scenes. The study's backbone is a large airborne data-set 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 field 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 X-band, the total received power is one of the best predictive parameter for vehicle detection, while the scattering mechanism characterization becomes more important at S- and L-bands.
|Journal||IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing|
|Publication status||Published - 2023|
- Radar polarimetry
- Synthetic aperture radar
- Target detection