Consider a wireless communication system with a radio station in an airborne synthetic aperture radar (SAR) system operating in a time-varying channel. Unpredictable packet loss occurs during transmission. Therefore

building a robust and efficient SAR image coding transmission scheme is necessary. Although traditional joint source-channel coding (JSCC) can achieve excellent and efficient transmission performance under fixed channel conditions

the predetermined redundancy of channel coding was adopted to achieve robustness. However

when the deterioration of channel condition exceeds the correction capacity of the channel codec in a time-varying channel

reconstruction performance declines at the decoder. In this work

we propose a robust SAR image coding transmission scheme over a time-varying channel using the democracy of compressive sensing (CS). A range of methods to improve the rate-distortion performance of the proposed scheme are also adopted. The reconstruction performance depends only on the number of measurements received and not on the actual measurements received; that is

every measurement is independent and nearly equal. Given the rich edge information of an SAR image

directional lifting wavelet transform (DLWT) is adopted as sparse representation to improve the representation of the edges of the SAR image. Although DLWT can attain good sparse representation for SAR images

this method cannot ensure strict sparse representation in CS; that is

representation coefficients still contain small coefficients that would interfere in the recovery of large coefficients. Thus

sparse filtering (setting small coefficients to zero) is also adopted in this study to eliminate the interference of small coefficients. Compared with the deterministic model-based CS reconstruction algorithms

the Bayesian model-based CS reconstruction algorithm is more reliable in a random signal scenario. Thus

we adopt an efficient Bayesian reconstruction algorithm called tree structured wavelet that exploits the structure dependencies of wavelet coefficients to attain high-performance image reconstruction. The proposed scheme achieves more robust SAR image coding transmission compared with the traditional JSCC scheme

CCSDS-RS

at the same rate. When the packet loss rate (PLR) reaches 0.05

the reconstruction performance of DSFB-CS is higher than that of CCSDS-RS. Therefore

CCSDS-RS is highly sensitive against packet loss and can easily lead to the cliff effect with channel deterioration. By contrast

the R-D performance in DSFB-CS decreases gracefully with channel deterioration. As the proposed scheme is based on reconstruction algorithm TSW

DSFB-CS is compared with the traditional scheme based on TSW without sparse filtering that uses DWT as sparse representation. DSFB-CS can improve the peak signal-to-noise ratio (PSNR) up to 3.9 dB. The proposed DSFB-CS achieves more robust transmission for SAR images compared with the traditional JSCC scheme

CCSDS-RS. The reconstruction performance of the proposed scheme declines slowly with the channel deterioration. Even under a time-varying channel

the robustness of DSFB-CS can still ensure that the decoder side can attain a relatively stable reconstructed image. Compared with the traditional CS scheme based on TSW

the proposed scheme exhibits significantly improved PSNR. The percentage of the reserved large coefficients in all coefficients and the bits of every measurement are both key parameters.