Zhang Bingchen

Structural adaptive and optimal speckle filtering in multilook full polarimetric SAR images

Abstract A novel approach is proposed for speckle reduction in multilook full polarimetric SAR images. In contrast to others, this approach adopts an enhanced structure detection method to estimate the parameters of the polarimetric covariance matrix for the multilook polarimetric whitening filtering (MPWF) algorithm and thus a structural adaptive and optimal specklefflter is developed. To evaluate the present approach, NASA SIR-C/XSAR, L band, four-look processed polarimetric SAR data of the Tian-Mountain Forest is used for simulation. Experimental results demonstrate the effectiveness of this novel filtering algorithm in case of both speckle reduction and preservation of texture information. Comparisons with other methods are also made.

Autofocus of sparse microwave imaging radar based on phase recovery

Sparse microwave imaging radar is a newly developed concept of microwave imaging, which introduces the sparse signal processing theory to traditional microwave imaging. As a combination of sparse signal processing theory and microwave imaging, the sparse microwave imaging is attracting peoples interest for its several potential advantages including better imaging performance and lower system complexity requirement. Airborne platform is an important application platform of sparse microwave imaging radar. On the airborne platform, the effect of motion phase error must be considered. Autofocus is an important technology to deal with the phase error in radar imaging, while traditional autofocus algorithms could not be directly applied under the sparse microwave imaging framework. In this paper, we introduce the phase recovery problem in the sparse signal processing theory to sparse microwave imaging, develop a novel autofocus algorithm under the sparse microwave imaging framework based on phase recovery. The phase recovery problem is solved via a greedy algorithm. A simulation is provided to demonstrate the effectiveness of the suggested algorithm.

Polarimetric SAR tomography of forested areas based on compressive MUSIC

This paper focus on the polarimetric synthetic aperture radar (SAR) tomography for forested areas based on compressive MUSIC. In the proposed method, full polarimetric SAR echo signal reflected from the imaging area is collected, the corresponding multiple measurement vector model is established according to the parameters of polarimetric channels, the wavelet basis is adopted for representing the sparse vertical structure of the imaging area, and finally, the backscattering coefficients of the area are reconstructed by compressive MUSIC algorithm. The necessary number of tracks for SAR tomography is reduced and the severity of spurious spikes is suppressed under the same measurement accuracy. Simulation results from the PolSARpro data validate the effectiveness.