NDLI: High performance SIFT features clustering of VHR satellite images for disaster management

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High performance SIFT features clustering of VHR satellite images for disaster management

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High performance SIFT features clustering of VHR satellite images for disaster management

Content Provider	ACM Digital Library
Author	Bhangale, Ujwala Durbha, Surya
Abstract	Disaster management applications require real time responses to handle the time critical situations. High performance computing technologies such as Graphics processing Unit (GPU) can serve this purpose, though it is not possible to provide real time responses but near real time responses can be surely helpful for these kinds of applications. This work focuses on high performance computing methodology, essential to speedup the analysis of very high resolution remote sensing data of earthquake. Agglomerative Hierarchical Clustering (AHC), one of the well known hierarchical clustering approaches is applied on robust Scale Invariant Feature Transform (SIFT) features with 128 dimensions. Agglomerative clustering with complete linkage strategy is implemented on GPU using Compute unified device architecture (CUDA). Fermi architecture based Tesla C2075 NVIDIA GPU with 448 cores is used to achieve high performance computing. It is observed that major operations of AHC such as proximity matrix calculation and minimum value extraction is providing good amount of speedup, 580X for proximity matrix calculation and 18X for minimum value extraction over a single core of Intel i7 CPU.
Starting Page	324
Ending Page	329
Page Count	6
File Format	PDF
ISBN	9781450333610
DOI	10.1145/2791405.2791460
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Sift Agglomerative hierarchical clustering High performance computing (hpc) Feature extraction Ahc Gpu Cuda
Content Type	Text
Resource Type	Article

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