Parallel and Distributed Algorithms for High-Speed Image Processing

Stevenson, Robert Louis; Lumsdaine, Andrew; Squires, Jeffery M.; McNally, Micheal P.

doi:10.21236/ada377689

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…to take advantage of the packing of pixels in parallelization. A lot of research was done using various strategies to parallelize the sequential algorithms (Stevenson, et al, 2000) (Jones, et al, 2003) (Akoguz, et al, 2013) (Bhojne, et al, 2013) (Plaza & Chang, 2007). Those various implementations require image processing domain knowledge from scientists and computer knowledge from computer engineers, which creates a gap between image processing and high performance (Seinstra, et al, 2002).…”

Section: Parallel Computingmentioning

confidence: 99%

“…Hence, solutions were sought from the high performance computing technologies, among which parallel computing and distributed computing are outstanding. The choice of parallel computing is rooted from the parallel computation nature in most image processing algorithms (Stevenson, et al, 2000) (Merigot & Petrosino, 2008). Distributed computing from another perspective could simultaneously execute computation from different tasks and hence increase the overall data throughput in a limited time (WikiPedia, 2015e) (Deng, et al, 2014a).…”

Section: Introductionmentioning

confidence: 99%

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High Performance Computing for DSM Extraction From Zy-3 Tri-Stereo Imagery

Huang

Pan

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:ZY-3 has been acquiring high quality imagery since its launch in 2012 and its tri-stereo (three-view or three-line-array) imagery has become one of the top choices for extracting DSM (Digital Surface Model) products in China over the past few years. The ZY-3 tristereo sensors offer users the ability to capture imagery over large regions including an entire territory of a country, such as China, resulting in a large volume of ZY-3 tri-stereo scenes which require timely (e.g., near real time) processing, something that is not currently possible using traditional photogrammetry workstations. This paper presents a high performance computing solution which can efficiently and automatically extract DSM products from ZY-3 tri-stereo imagery. The high performance computing solution leverages certain parallel computing technologies to accelerate computation within an individual scene and then deploys a distributed computing technology to increase the overall data throughput in a robust and efficient manner. By taking advantage of the inherent efficiencies within the high performance computing environment, the DSM extraction process can exploit all combinations offered from a set of tri-stereo images (forward-backword, forward-nadir and backword-nadir). The DSM results merged from all of the potential combinations can minimize blunders (e.g., incorrect matches) and also offer the ability to remove potential occlusions which may exist in a single stereo pair, resulting in improved accuracy and quality versus those that are not merged. Accelerated performance is inherent within each of the individual steps of the DSM extraction workflow, including the collection of ground control points and tie points, image bundle adjustment, the creation of epipolar images, and computing elevations. Preliminary experiments over a large area in China have proven that the high performance computing system can generate high quality and accurate DSM products in a rapid manner.

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Section: Parallel Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High Performance Computing for DSM Extraction From Zy-3 Tri-Stereo Imagery

Huang

Pan

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…to take advantage of the packing of pixels in parallelization. Many researches were done using various strategies to parallelize the sequential algorithms [23] [29] [30] [31] [26]. Those various implementations require image processing domain knowledge from scientists and computer knowledge from computer engineers, which creates a gap between image processing and high performance [32].…”

Section: Parallel Computingmentioning

confidence: 99%

“…Considering the parallel computation natural in most image processing algorithms [23] [24], the first strategy towards building a high performance system is to make the key and time consuming algorithms parallel. The algorithms for GCP collection and orthorectification are identified as two key algorithms during the process of turning Level 1 or 2 products into Level 4 products.…”

Section: Introductionmentioning

confidence: 99%

Building high-performance system for processing a daily large volume of Chinese satellites imagery

et al. 2014

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The number of Earth observation satellites from China increases dramatically recently and those satellites are acquiring a large volume of imagery daily. As the main portal of image processing and distribution from those Chinese satellites, the China Centre for Resources Satellite Data and Application (CRESDA) has been working with PCI Geomatics during the last three years to solve two issues in this regard: processing the large volume of data (about 1,500 scenes or 1 TB per day) in a timely manner and generating geometrically accurate orthorectified products. After three-year research and development, a high performance system has been built and successfully delivered. The high performance system has a service oriented architecture and can be deployed to a cluster of computers that may be configured with high end computing power. The high performance is gained through, first, making image processing algorithms into parallel computing by using high performance graphic processing unit (GPU) cards and multiple cores from multiple CPUs, and, second, distributing processing tasks to a cluster of computing nodes. While achieving up to thirty (and even more) times faster in performance compared with the traditional practice, a particular methodology was developed to improve the geometric accuracy of images acquired from Chinese satellites (including HJ-1 A/B, ZY-1-02C, ZY-3, GF-1, etc.). The methodology consists of fully automatic collection of dense ground control points (GCP) from various resources and then application of those points to improve the photogrammetric model of the images. The delivered system is up running at CRESDA for pre-operational production and has been and is generating good return on investment by eliminating a great amount of manual labor and increasing more than ten times of data throughput daily with fewer operators. Future work, such as development of more performance-optimized algorithms, robust image matching methods and application workflows, is identified to improve the system in the coming years.

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