Paper Info
Title
Distributed SAR Image Change Detection with OpenCL-Enabled Spark
Abstract
Distributed processing framework has been widely used in remote-sensing field. Spark, as a popular distributed computing framework, has been utilized to deal with big remote sensing data. However, it is inefficient due to that the application is not only data intensive but also computation intensive. For example, in Synthetic Aperture Radar (SAR) image change detection, clustering analysis can consume a lot of computing time and memory resources dealing with big remote sensing data. Coprocessors (GPU, MIC, etc.) have a high-compute power, which is able to handle computation intensive tasks. In this paper, we proposed an OpenCL-enabled Spark framework to accelerate Kernel Fuzzy C-Mean (KFCM) algorithm for SAR image change detection. And the computation intensive operations of KFCM are transferred to coprocessors of the cluster through the proposed OpenCL-enabled Spark framework. The experimental results on real SAR image indicate that the implementation on OpenCL-enabled Spark is efficient and scalable.
Year
DOI
Venue
2017
10.1145/3129457.3129495
ETCD@ASPLOS
Field
DocType
ISBN
Kernel (linear algebra),Change detection,Spark (mathematics),Computer science,Synthetic aperture radar,Real-time computing,Coprocessor,Cluster analysis,Computation,Scalability
Conference
978-1-4503-4923-9
Citations 
PageRank 
References 
0
0.34
10
Authors
7
Name
Order
Citations
PageRank
Huming Zhu193.50
Jianing Kou200.34
Linyan Qiu300.34
Yuqi Guo421.09
Mingwei Niu521.09
Maoguo Gong62676172.02
Licheng Jiao75698475.84