Abstract:

The conventional remote sensing system is faced with some intractable problems, such as high speed sampling and mass data storage, owing to the requirement of high resolution. The synthetic aperture radar and the optical sparse remote sensing systems are designed based on compressive sensing, they break through the limitation of Shannon-Nyquist theorem and realize a equivalent or even better signal recovery based on much fewer measurements. Firstly, the sparse representation matrix is designed according to different characteristics of the measured targets and scenes. Secondly, by minimizing the cross-correlation, the sensing matrix is selected which corresponds to the sparse representation matrix. Finally, a recovery algorithm suitable to large-scale problems is investigated. The feasibility of the designed sparse remote sensing systems is validated by the numerical experiments based on radar echo generated by a professional electromagnetic scattering software and the optical image of complex scenes.