Compressive sensing based image reconstruction for synthetic aperture radar using discrete cosine transform and noiselets

Tae Hee Kim, Ram M. Narayanan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Synthetic Aperture Radar (SAR) has the ability to obtain high resolution 2D imagery from distributed targets, such as landscapes. SAR image resolution is limited by the transmitted signal bandwidth and the antenna length, and also depends on the sampling rate. SAR processing is very computer-intensive since it measures and processes enormous amounts of data to obtain the target image. In this paper, we propose an alternative method using compressed sensing (CS), which is able to overcome the fast sampling and the large-scale data storage limitations. CS is also effective for reconstructing the raw image data with sparsity. For complex raw data obtained by the echo signal, compressed sensing based on ℓ1-norm optimization with discrete cosine transform and noiselet processing is adopted to reconstruct the SAR image. By using fewer SAR echo signal samples with complex data, we demonstrate improved results in the SAR image reconstruction.

Original languageEnglish (US)
Title of host publication2015 38th International Conference on Telecommunications and Signal Processing, TSP 2015
EditorsKarol Molnar, Norbert Herencsar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages582-586
Number of pages5
ISBN (Electronic)9781479984985
DOIs
StatePublished - Oct 9 2015
Event2015 38th International Conference on Telecommunications and Signal Processing, TSP 2015 - Prague, Czech Republic
Duration: Jul 9 2015Jul 11 2015

Publication series

Name2015 38th International Conference on Telecommunications and Signal Processing, TSP 2015

Other

Other2015 38th International Conference on Telecommunications and Signal Processing, TSP 2015
Country/TerritoryCzech Republic
CityPrague
Period7/9/157/11/15

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing

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