Efficient compressed imaging method for a microsatellite optical camera

Imaging integrated with compression is considered the holy grail of microsatellite photography because it improves the degree of integration of the camera system, removing the compression system, high capacity storage system, and high-speed image transmission system, which consume lots of resources of the satellite platform. In this paper, we propose an efficient compressed imaging method for remote sensing photography. We consider wavelet coefficients as pixels of a block-wise megapixel sensor (BMPS). We integrate the saliency information stage into the BMPS to perform compressed sampling (CS) in order to further improve imaging performance. In the compressed sensing process, we use transformed postwavelet coefficients to calculate saliency information of images in the postwavelet domain. According to different regions having different saliency information, the corresponding sensing resources are allocated to perform CS. CS can obtain the compressed discrete sparse samples of the original signal at a much lower sample rate than the Nyquist frequency. The discrete samples signal can be reconstructed by a nonlinear recovery algorithm in the ground. Experimental results show that the proposed compressed imaging method outperforms the traditional saliency-based methods in terms of multiple assessment approaches.