Dr. Aaron Rogan Profile

Dr. Aaron Rogan

at Neva Ridge Technologies Inc

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Publications (2)

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Proceedings Article | 5 May 2011 Paper

InSAR processing using a GPGPU

Aaron Rogan, Richard Carande

Proceedings Volume 8051, 80510O (2011) https://doi.org/10.1117/12.889917

KEYWORDS: Synthetic aperture radar, Image processing, Image segmentation, Image acquisition, Interferometry, Image registration, Interferometric synthetic aperture radar, Stanford Linear Collider, Image processing algorithms and systems, Coherence (optics)

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General purpose graphical processing units or GPGPUs have emerged in recent years as the power horse behind many large scale computing efforts. For example, the recent unveiling of the world's fastest supercomputer has achieved this feat by utilizing low cost and high performance GPGPUs. Additionally, in the past year the synthetic aperture radar (SAR) community has started to utilize GPGPUs as well. The utilization of GPGPUs to date has been limited mainly to SAR image formation and in this capacity tremendous performance improvements over the same CPU based algorithms have been demonstrated. However, image formation is only one of many necessary steps towards SAR image exploitation. Image registration, filtering, interpolation and interferometric flattening are equally important steps in obtaining many of the desired output products such as coherence change detection (CCD) products and terrain adjusted interferograms. We will demonstrate that by transitioning the entire SAR image exploitation processing chain from image formation through product generation onto a GPGPU, it is possible to achieve more than an order of magnitude in performance improvements. In this paper we will review results presented at last year's SPIE conference regarding SAR image formation and present new results obtained for coherent exploitation of SAR data including CCD and interferometric SAR processing. In addition to presenting these results, we will discuss challenges associated with migration of CPU-based exploitation algorithms to the GPGPU environment, as well as to discuss possible future improvements using these powerful new devices and associated software tools.

Proceedings Article | 27 April 2010 Paper

Improving the fast back projection algorithm through massive parallelizations

Aaron Rogan, Richard Carande

Proceedings Volume 7669, 76690I (2010) https://doi.org/10.1117/12.850332

KEYWORDS: Image processing, Data processing, Synthetic aperture radar, Radar, Detection and tracking algorithms, Video surveillance, Video, Mathematics, Sensor technology, Surveillance

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With sensor technologies rapidly improving, the need to process increasingly larger data sets is becoming the main bottleneck in many real time applications associated with persistence surveillance such as VideoSAR and volumetric SAR imaging. In many instances, the image fidelity is of utmost importance which can have implications when choosing the appropriate algorithm to generate the desired data products. The performance improvements afforded by algorithms such as the fast back projection (FBP) algorithm prove attractive for such environments. Unfortunately, even though the FBP algorithm is magnitudes faster than a traditional back projection algorithm it is still incapable of meeting the strict requirements of some of the aforementioned real time applications. However, the emergence of general purpose graphical processing units (GPGPUs) in recent years have afforded many scientific fields orders of magnitudes improvement in performance for a large variety of applications. This is also the case for the FBP algorithm. By distributing the processing across 480 processing cores located on a single video card, it possible to achieve substantial performance improvements compared to the serial FBP algorithm. Considering that many PCs are capable of housing three to four video cards, it is possible to obtain more than two orders of magnitude improvement in performance with the parallel approach. This technology provides the ability to process enormous datasets in the field without the need of supercomputers that have to date been the only means of keeping pace with the incoming data.

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