This paper develops a method for making an image of an object when there are extra point-like scatterers in the environment. Once the location of these scatterers is known, they can be exploited in the imaging process.
Here the extra point scatterers are assumed to lie between the sensor and the object of interest. A single-scattering model is used for the object itself. Detailed analysis is carried out for the case of a single extra scatterer in the foreground; the extension to the case of many scatterers is expected to be similar.
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.
Conventional radars and communications systems employ waveforms with a set of bandwidth constraints for a given application. Unfortunately, for many applications, such generic waveforms do not employ the optimal use of bandwidth or energy to accomplish the mission of the user due to electromagnetic clutter and noise. We therefore suggest an alternate approach that uses a matching pursuits algorithm in conjunction with two types of detection statistics to design the optimal waveform for the application. We then will demonstrate how this approach can maximize the matched filter detection performance and band-width allocation in a radar and communications example with a high interference environment.
Radar imaging traditionally requires extremely large computational resources in order to coherently process data into an image due to the large number of data samples in high bandwidth radars. We propose a methodology to achieve high range and cross range resolution by pre-detection followed by phase integration in the wavelet domain in order to significantly reduce the bandwidth necessary and the computational load of the coherent phase processing. We will then compare this process to FFT based phase integration in terms of computational complexity and show simulated results.
Traditional wavelet edge detection and encoding schemes preserve shape features of objects effectively at a variety of spatial scales and also allow an efficient means of image and video compression. However these schemes also remove texture from imagery and thus image reproduction quality suffers. Fractal encoding techniques on the other hand generate high compression ratios but tend to introduce blocky artifacts in imagery. Thus we describe a video encoding method that combines the shape preserving capability of wavelets with the compression qualities of fractal compression for a hybrid multiresolution technique that achieves high compression, selective and accurate feature preservation, and is computationally efficient.
Traditional wavelet edge detection and encoding schemes preserve shape features of objects effectively at a variety of spatial scales and also allow an efficient means of image and video compression. However these schemes also remove texture from imagery and thus image reproduction quality suffers. Fractal encoding techniques on the other hand generate high compression ratios but tend to introduce blocky artifacts in imagery. Thus we describe an encoding method that combines the shape preserving capability of wavelets with the compression qualities of fractal compression for a hybrid multiresolution technique that achieves high compression, selective and accurate feature preservation, and is computationally efficient.
Traditional wavelet edge detection and encoding schemes preserve shape features of objects effectively at a variety of spatial scales and also allow an efficient means of image and video compression. However these schemes also remove texture from imagery and thus image reproduction quality suffers. Fractal encoding techniques on the other hand generate high compression ratios but tend to introduce blocky artifacts in imagery. Thus we describe an encoding method that combines the shape preserving capability of wavelets with the compression qualities of fractal compression for a hybrid multiresolution technique that achieves high compression, selective and accurate feature preservation, and is computationally efficient.
It is possible to build a multiresolution image encoding technique using the fractal transform. Fractal encoding methods rely on measuring least mean square difference between image blocks at two different spatial scales for most efficient block matching. If we instead, compute the centroid of the blocks to be matched before matching occurs and preserve this centroid information in the encoding process we both speed up the encoding process and have information that will allow us to interpret the shape of the objects in the encoded scenes. This ability may dramatically increase the speed at which pattern identification in large volumes of image data may be performed since less data would have to be processed for searches over large number of images.
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