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