<title>Error-resilient intelligent compression of SAR imagery</title>

Abstract

In the present work, we propose a system for error-resilient coding of synthetic aperture radar imagery, whereby regions of interest and background information are coded independently of each other. A multiresolution, constant-false-alarm-rate (CFAR) detection scheme is utilized to discriminate between target regions and natural clutter. Based upon the detected target regions, we apply less compression to targets, and more compression to background data. This methodology preserves relevant features of targets for further analysis, and preserves the background only to the extent of providing contextual information. The proposed system is designed specifically for transmission of the compressed bit stream over noisy wireless channels. The coder uses a robust channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the image coding based upon the channel characteristics. A phase scrambling stage is also incorporated to further increase the coding performance, and to improve the robustness to nonstationary signals and channels. The resulting system dramatically reduces the bandwidth/storage requirements of the digital SAR imagery, while preserving the target-specific utility of the imagery, and enabling its transmission over noisy wireless channels without the use of error correction/concealment techniques.