DocumentCode :
1882409
Title :
Satellite imagery retrieval: Features & metrics evaluation
Author :
Gebril, Mohamed ; Homaifar, Abdollah ; Buaba, Ruben ; Kihn, Eric
Author_Institution :
NOAA-ISET Center, North Carolina A&T State Univ., Greensboro, NC, USA
fYear :
2012
fDate :
3-10 March 2012
Firstpage :
1
Lastpage :
6
Abstract :
In this paper, an evaluation technique based on several image feature attributes along with image classifications is investigated. Furthermore, a semi-supervised technique based on support vector machine (SVM) for image classification and a Locality Sensitive Hashing (LSH) based searching algorithm to search for similarity of satellite imagery is presented. Given a query image, the goal is to retrieve matching images in the database based on the shape features extracted from satellite imagery data. The experimental results demonstrate superior results based on shape features which provide a better classification accuracy using both support vector machine and the semi-supervised hashing search methods.
Keywords :
cryptography; feature extraction; image classification; image matching; image retrieval; shape recognition; support vector machines; LSH based searching algorithm; SVM; classification accuracy; image classification; image feature attribute; image matching; locality sensitive hashing; metrics evaluation; query image; satellite imagery data; satellite imagery retrieval; satellite imagery similarity; semisupervised hashing search method; shape feature extraction; support vector machine; Correlation; Feature extraction; Satellites; Shape; Support vector machine classification; Training; Image classification; Image retrieval; Shape feature vector; Similarity measure;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Aerospace Conference, 2012 IEEE
Conference_Location :
Big Sky, MT
ISSN :
1095-323X
Print_ISBN :
978-1-4577-0556-4
Type :
conf
DOI :
10.1109/AERO.2012.6187167
Filename :
6187167
Link To Document :
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