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Authors: Jefersson A. dos Santos ; Otávio A. B. Penatti and Ricardo da S. Torres

Affiliation: University of Campinas – Unicamp, Brazil

ISBN: 978-989-674-029-0

Keyword(s): Image descriptors, Remote sensing image, Image classification, Image retrieval.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Feature Extraction ; Features Extraction ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image and Video Analysis ; Informatics in Control, Automation and Robotics ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Signal Processing, Sensors, Systems Modeling and Control ; Soft Computing

Abstract: Classifying Remote Sensing Images (RSI) is a hard task. There are automatic approaches whose results normally need to be revised. The identification and polygon extraction tasks usually rely on applying classification strategies that exploit visual aspects related to spectral and texture patterns identified in RSI regions. There are a lot of image descriptors proposed in the literature for content-based image retrieval purposes that may be useful for RSI classification. This paper presents a comparative study to evaluate the potential of using successful color and texture image descriptors for remote sensing retrieval and classification. Seven descriptors that encode texture information and twelve color descriptors that can be used to encode spectral information were selected. We perform experiments to evaluate the effectiveness of these descriptors, considering image retrieval and classification tasks. To evaluate descriptors in classification tasks, we also propose a methodology bas ed on KNN classifier. Experiments demonstrate that Joint Auto-Correlogram (JAC), Color Bitmap, Invariant Steerable Pyramid Decomposition (SID) and Quantized Compound Change Histogram (QCCH) yield the best results. (More)

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Paper citation in several formats:
A. dos Santos J.; A. B. Penatti O.; da S. Torres R. and (2010). EVALUATING THE POTENTIAL OF TEXTURE AND COLOR DESCRIPTORS FOR REMOTE SENSING IMAGE RETRIEVAL AND CLASSIFICATION.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 203-208. DOI: 10.5220/0002843402030208

@conference{visapp10,
author={Jefersson {A. dos Santos} and Otávio {A. B. Penatti} and Ricardo {da S. Torres}},
title={EVALUATING THE POTENTIAL OF TEXTURE AND COLOR DESCRIPTORS FOR REMOTE SENSING IMAGE RETRIEVAL AND CLASSIFICATION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={203-208},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002843402030208},
isbn={978-989-674-029-0},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - EVALUATING THE POTENTIAL OF TEXTURE AND COLOR DESCRIPTORS FOR REMOTE SENSING IMAGE RETRIEVAL AND CLASSIFICATION
SN - 978-989-674-029-0
AU - A. dos Santos, J.
AU - A. B. Penatti, O.
AU - da S. Torres, R.
PY - 2010
SP - 203
EP - 208
DO - 10.5220/0002843402030208

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