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Title: Unsupervised Feature Learning for High-Resolution Satellite Image Classification

Abstract

The rich data provided by high-resolution satellite imagery allow us to directly model geospatial neighborhoods by understanding their spatial and structural patterns. In this paper we explore an unsupervised feature learning approach to model geospatial neighborhoods for classification purposes. While pixel and object based classification approaches are widely used for satellite image analysis, often these approaches exploit the high-fidelity image data in a limited way. In this paper we extract low-level features to characterize the local neighborhood patterns. We exploit the unlabeled feature measurements in a novel way to learn a set of basis functions to derive new features. The derived sparse feature representation obtained by encoding the measured features in terms of the learned basis function set yields superior classification performance. We applied our technique on two challenging image datasets: ORNL dataset representing one-meter spatial resolution satellite imagery representing five land-use categories and, UCMERCED dataset consisting of 21 different categories representing sub-meter resolution overhead imagery. Our results are highly promising and, in the case of UCMERCED dataset we outperform the best results obtained for this dataset. We show that our feature extraction and learning methods are highly effective in developing a detection system that can be used to automaticallymore » scan large-scale high-resolution satellite imagery for detecting large-facility.« less

Authors:
 [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
Work for Others (WFO)
OSTI Identifier:
1073654
DOE Contract Number:
DE-AC05-00OR22725
Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Tranactions on Geoscience and Remote Sensing; Journal Issue: 99
Country of Publication:
United States
Language:
English
Subject:
Basis function; feature learning; sparse coding; classification; neighborhood mapping; satellite image

Citation Formats

Cheriyadat, Anil M. Unsupervised Feature Learning for High-Resolution Satellite Image Classification. United States: N. p., 2013. Web.
Cheriyadat, Anil M. Unsupervised Feature Learning for High-Resolution Satellite Image Classification. United States.
Cheriyadat, Anil M. Tue . "Unsupervised Feature Learning for High-Resolution Satellite Image Classification". United States. doi:.
@article{osti_1073654,
title = {Unsupervised Feature Learning for High-Resolution Satellite Image Classification},
author = {Cheriyadat, Anil M},
abstractNote = {The rich data provided by high-resolution satellite imagery allow us to directly model geospatial neighborhoods by understanding their spatial and structural patterns. In this paper we explore an unsupervised feature learning approach to model geospatial neighborhoods for classification purposes. While pixel and object based classification approaches are widely used for satellite image analysis, often these approaches exploit the high-fidelity image data in a limited way. In this paper we extract low-level features to characterize the local neighborhood patterns. We exploit the unlabeled feature measurements in a novel way to learn a set of basis functions to derive new features. The derived sparse feature representation obtained by encoding the measured features in terms of the learned basis function set yields superior classification performance. We applied our technique on two challenging image datasets: ORNL dataset representing one-meter spatial resolution satellite imagery representing five land-use categories and, UCMERCED dataset consisting of 21 different categories representing sub-meter resolution overhead imagery. Our results are highly promising and, in the case of UCMERCED dataset we outperform the best results obtained for this dataset. We show that our feature extraction and learning methods are highly effective in developing a detection system that can be used to automatically scan large-scale high-resolution satellite imagery for detecting large-facility.},
doi = {},
journal = {IEEE Tranactions on Geoscience and Remote Sensing},
number = 99,
volume = ,
place = {United States},
year = {Tue Jan 01 00:00:00 EST 2013},
month = {Tue Jan 01 00:00:00 EST 2013}
}
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