DocumentCode :
1893865
Title :
Satellite image retrieval using semi-supervised learning
Author :
Gebril, Mohamed ; Homaifar, Abdollah ; Buaba, Ruben ; Kihn, Eric
Author_Institution :
NOAA-ISET Center Autonomous Control, North Carolina A&T State Univ., Greensboro, NC, USA
fYear :
2011
fDate :
24-29 July 2011
Firstpage :
2935
Lastpage :
2938
Abstract :
In this paper, 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 :
artificial satellites; feature extraction; file organisation; geophysical image processing; image classification; image retrieval; learning (artificial intelligence); query formulation; support vector machines; database; image classification; locality sensitive hashing; matching image retrieval; query image; satellite image retrieval; semisupervised hashing search method; semisupervised learning; shape feature extraction; similarity searching; support vector machine; Accuracy; Feature extraction; Measurement; Satellites; Shape; Support vector machines; Training; Image classification; Image retrieval; Shape feature vector; Similarity measure;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International
Conference_Location :
Vancouver, BC
ISSN :
2153-6996
Print_ISBN :
978-1-4577-1003-2
Type :
conf
DOI :
10.1109/IGARSS.2011.6049830
Filename :
6049830
Link To Document :
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