High resolution disaster data clustering using Graphics Processing Units

Near real time processing and clusters extraction from high-resolution satellite images of disaster affected area aids in monitoring and deployment of rescue activities. In this work the k-medoids clustering algorithm is analyzed for near real time applications. In general, due to the large size of...

Full description

Saved in:
Bibliographic Details
Main Authors: Kurte, Kuldeep R., Durbha, Surya S.
Format: Conference Proceeding
Language:English
Subjects:
CLARA
Clustering algorithms
Computer architecture
CUDA
GPU
Graphics processing units
Image resolution
Instruction sets
Partition Around Medoids
Remote sensing
Satellites
Single Instruction Multiple Data
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Near real time processing and clusters extraction from high-resolution satellite images of disaster affected area aids in monitoring and deployment of rescue activities. In this work the k-medoids clustering algorithm is analyzed for near real time applications. In general, due to the large size of satellite data, the computational time of traditional k-medoids is found to be very high. Hence in order to achieve the aim of near real time processing of such huge data we developed a parallel implementation of k-medoids (GPUPAM) and integrated with CLARA (Clustering for LARge Application), which is one of the variant of kmedoids. The implementation is performed on NVIDIA's Graphical Processing Unit (GPU). The performance improvements that were obtained is demonstrated by a GPU implementation on high resolution Haiti Earthquake QuickBird (2.4 meter resolution) data and compared with the traditional sequential implementation. The results show that the GPU implementation is found to achieve almost 96% performance improvement as compared to the sequential implementation.
ISSN:2153-6996
2153-7003
DOI:10.1109/IGARSS.2013.6723121