GPU-based acceleration of interval type-2 fuzzy c-means clustering for satellite imagery land-cover classification

When processing with large data such as satellite images, the computing speed is the problem need to be resolved. This paper introduces a method to improve the computational efficiency of the interval type-2 fuzzy c-means clustering(IT2-FCM) based on GPU platform and applied to land-cover classifica...

Full description

Saved in:
Bibliographic Details
Main Authors: Long Thanh Ngo, Dinh Sinh Mai, Mau Uyen Nguyen
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Clustering algorithms
Fuzzy logic
Fuzzy sets
Graphics processing units
high performance computing
interval type-2 fuzzy c-means clustering
Partitioning algorithms
Satellites
type-2 fuzzy sets
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:When processing with large data such as satellite images, the computing speed is the problem need to be resolved. This paper introduces a method to improve the computational efficiency of the interval type-2 fuzzy c-means clustering(IT2-FCM) based on GPU platform and applied to land-cover classification from multi-spectral satellite image. GPU-based calculations are high performance solution and free up the CPU. The experimental results show that the performance of the GPU is many times faster than CPU.
ISSN:2164-7143
2164-7151
DOI:10.1109/ISDA.2012.6416674