ERS-1 SAR data processing

To take full advantage of the synthetic aperature radar (SAR) to be flown on board the European Space Agency's Remote Sensing Satellite (ERS-1) (1989) and the Canadian Radarsat (1990), the implementation of a receiving station in Alaska is being studied to gather and process SAR data pertaining in particular to regions within the station's range of reception. The current SAR data processing requirement is estimated to be on the order of 5 minutes per day. The Interim Digital Sar Processor (IDP) which was under continual development through Seasat (1978) and SIR-B (1984) can process slightly more than 2 minutes of ERS-1 data per day. On the other hand, the Advanced Digital SAR Processore (ADSP), currently under development for the Shuttle Imaging Radar C (SIR-C, 1988) and the Venus Radar Mapper, (VMR, 1988), is capable of processing ERS-1 SAR data at a real time rate. To better suit the anticipated ERS-1 SAR data processing requirement, both a modified IDP and an ADSP derivative are being examined. For the modified IDP, a pipelined architecture is proposed for the mini-computer plus array processor arrangement to improve throughout. For the ADSP derivative, a simplified version is proposed to enhance ease of implementation and maintainability while maintaing real time throughput rates. These processing systems are discussed and evaluated

Pipelined digital SAR azimuth correlator using hybrid FFT-transversal filter

A synthetic aperture radar system (SAR) having a range correlator is provided with a hybrid azimuth correlator which utilizes a block-pipe-lined fast Fourier transform (FFT). The correlator has a predetermined FFT transform size with delay elements for delaying SAR range correlated data so as to embed in the Fourier transform operation a corner-turning function as the range correlated SAR data is converted from the time domain to a frequency domain. The azimuth correlator is comprised of a transversal filter to receive the SAR data in the frequency domain, a generator for range migration compensation and azimuth reference functions, and an azimuth reference multiplier for correlation of the SAR data. Following the transversal filter is a block-pipelined inverse FFT used to restore azimuth correlated data in the frequency domain to the time domain for imaging

Synthetic Aperture Radar (SAR) data processing

The available and optimal methods for generating SAR imagery for NASA applications were identified. The SAR image quality and data processing requirements associated with these applications were studied. Mathematical operations and algorithms required to process sensor data into SAR imagery were defined. The architecture of SAR image formation processors was discussed, and technology necessary to implement the SAR data processors used in both general purpose and dedicated imaging systems was addressed

An Efficient Polyphase Filter Based Resampling Method for Unifying the PRFs in SAR Data

Variable and higher pulse repetition frequencies (PRFs) are increasingly being used to meet the stricter requirements and complexities of current airborne and spaceborne synthetic aperture radar (SAR) systems associated with higher resolution and wider area products. POLYPHASE, the proposed resampling scheme, downsamples and unifies variable PRFs within a single look complex (SLC) SAR acquisition and across a repeat pass sequence of acquisitions down to an effective lower PRF. A sparsity condition of the received SAR data ensures that the uniformly resampled data approximates the spectral properties of a decimated densely sampled version of the received SAR data. While experiments conducted with both synthetically generated and real airborne SAR data show that POLYPHASE retains comparable performance to the state-of-the-art BLUI scheme in image quality, a polyphase filter-based implementation of POLYPHASE offers significant computational savings for arbitrary (not necessarily periodic) input PRF variations, thus allowing fully on-board, in-place, and real-time implementation

Methods of evaluating the effects of coding on SAR data

It is recognized that mean square error (MSE) is not a sufficient criterion for determining the acceptability of an image reconstructed from data that has been compressed and decompressed using an encoding algorithm. In the case of Synthetic Aperture Radar (SAR) data, it is also deemed to be insufficient to display the reconstructed image (and perhaps error image) alongside the original and make a (subjective) judgment as to the quality of the reconstructed data. In this paper we suggest a number of additional evaluation criteria which we feel should be included as evaluation metrics in SAR data encoding experiments. These criteria have been specifically chosen to provide a means of ensuring that the important information in the SAR data is preserved. The paper also presents the results of an investigation into the effects of coding on SAR data fidelity when the coding is applied in (1) the signal data domain, and (2) the image domain. An analysis of the results highlights the shortcomings of the MSE criterion, and shows which of the suggested additional criterion have been found to be most important

SAR data compression: Application, requirements, and designs

The feasibility of reducing data volume and data rate is evaluated for the Earth Observing System (EOS) Synthetic Aperture Radar (SAR). All elements of data stream from the sensor downlink data stream to electronic delivery of browse data products are explored. The factors influencing design of a data compression system are analyzed, including the signal data characteristics, the image quality requirements, and the throughput requirements. The conclusion is that little or no reduction can be achieved in the raw signal data using traditional data compression techniques (e.g., vector quantization, adaptive discrete cosine transform) due to the induced phase errors in the output image. However, after image formation, a number of techniques are effective for data compression

Sea Height Information from Complex SAR Data

Synthetic aperture radar data was used to construct an estimation algorithm for development of information on long waves. The evolution of chaotic dynamic systems was also explored

Knowledge-based segmentation of SAR data with learned priors

©2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/83.821747An approach for the segmentation of still and video synthetic aperture radar (SAR) images is described in this note. A priori knowledge about the objects present in the image, e.g., target, shadow, and background terrain, is introduced via Bayes' rule. Posterior probabilities obtained in this way are then anisotropically smoothed, and the image segmentation is obtained via MAP classifications of the smoothed data. When segmenting sequences of images, the smoothed posterior probabilities of past frames are used to learn the prior distributions in the succeeding frame. We show with examples from public data sets that this method provides an efficient and fast technique for addressing the segmentation of SAR data