Aerial image classification using texture and color-based descriptors

Daniel Cortes; Gustavo Calderón; Antonio Arista; Karina Toscano; Mariko Nakano

doi:10.1109/CNCG.2016.7985077

Aerial image classification using texture and color-based descriptors

Daniel Cortes, Gustavo Calderón, Antonio Arista, Karina Toscano, Mariko Nakano

Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Unidad Culhuacán

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

In this paper, we evaluate eleven image descriptors for urban-rural classification using aerial images. The eleven image descriptors are composed by seven texture-based descriptors and four combinations of texture and color descriptors. The classification is carried out using Support Vector Machine (SVM) with radial basis function as kernel function. The performance of these images descriptors are evaluated using accuracy, precision, sensitivity and specificity. From the evaluation, the combination of Gabor descriptor and Dominant Color descriptor provides a better performance, obtaining accuracy more than 91%.

Original language	English
Title of host publication	2016 IEEE 1er Congreso Nacional de Ciencias Geoespaciales
Subtitle of host publication	Sustainable Geospatial Technology at Service of Society, CNCG 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	9-12
Number of pages	4
ISBN (Electronic)	9781538618639
DOIs	https://doi.org/10.1109/CNCG.2016.7985077
State	Published - 19 Jul 2017
Event	1st IEEE Mexican Geospatial Science Congress, CNCG 2016 - Mexico City, Mexico Duration: 7 Dec 2016 → 9 Dec 2016

Publication series

Name	2016 IEEE 1er Congreso Nacional de Ciencias Geoespaciales: Sustainable Geospatial Technology at Service of Society, CNCG 2016 - Proceedings

Conference

Conference	1st IEEE Mexican Geospatial Science Congress, CNCG 2016
Country/Territory	Mexico
City	Mexico City
Period	7/12/16 → 9/12/16

Keywords

Image descriptor
aerial image classification
color-based descriptor
texture-based descriptor
urban-rural classification

Access to Document

10.1109/CNCG.2016.7985077

Cite this

Cortes, D., Calderón, G., Arista, A., Toscano, K., & Nakano, M. (2017). Aerial image classification using texture and color-based descriptors. In 2016 IEEE 1er Congreso Nacional de Ciencias Geoespaciales: Sustainable Geospatial Technology at Service of Society, CNCG 2016 - Proceedings (pp. 9-12). Article 7985077 (2016 IEEE 1er Congreso Nacional de Ciencias Geoespaciales: Sustainable Geospatial Technology at Service of Society, CNCG 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CNCG.2016.7985077

Cortes, Daniel ; Calderón, Gustavo ; Arista, Antonio et al. / Aerial image classification using texture and color-based descriptors. 2016 IEEE 1er Congreso Nacional de Ciencias Geoespaciales: Sustainable Geospatial Technology at Service of Society, CNCG 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 9-12 (2016 IEEE 1er Congreso Nacional de Ciencias Geoespaciales: Sustainable Geospatial Technology at Service of Society, CNCG 2016 - Proceedings).

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title = "Aerial image classification using texture and color-based descriptors",

abstract = "In this paper, we evaluate eleven image descriptors for urban-rural classification using aerial images. The eleven image descriptors are composed by seven texture-based descriptors and four combinations of texture and color descriptors. The classification is carried out using Support Vector Machine (SVM) with radial basis function as kernel function. The performance of these images descriptors are evaluated using accuracy, precision, sensitivity and specificity. From the evaluation, the combination of Gabor descriptor and Dominant Color descriptor provides a better performance, obtaining accuracy more than 91%.",

keywords = "Image descriptor, aerial image classification, color-based descriptor, texture-based descriptor, urban-rural classification",

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year = "2017",

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doi = "10.1109/CNCG.2016.7985077",

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series = "2016 IEEE 1er Congreso Nacional de Ciencias Geoespaciales: Sustainable Geospatial Technology at Service of Society, CNCG 2016 - Proceedings",

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Cortes, D, Calderón, G, Arista, A, Toscano, K & Nakano, M 2017, Aerial image classification using texture and color-based descriptors. in 2016 IEEE 1er Congreso Nacional de Ciencias Geoespaciales: Sustainable Geospatial Technology at Service of Society, CNCG 2016 - Proceedings., 7985077, 2016 IEEE 1er Congreso Nacional de Ciencias Geoespaciales: Sustainable Geospatial Technology at Service of Society, CNCG 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 9-12, 1st IEEE Mexican Geospatial Science Congress, CNCG 2016, Mexico City, Mexico, 7/12/16. https://doi.org/10.1109/CNCG.2016.7985077

Aerial image classification using texture and color-based descriptors. / Cortes, Daniel; Calderón, Gustavo; Arista, Antonio et al.
2016 IEEE 1er Congreso Nacional de Ciencias Geoespaciales: Sustainable Geospatial Technology at Service of Society, CNCG 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 9-12 7985077 (2016 IEEE 1er Congreso Nacional de Ciencias Geoespaciales: Sustainable Geospatial Technology at Service of Society, CNCG 2016 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Calderón, Gustavo

AU - Arista, Antonio

AU - Toscano, Karina

AU - Nakano, Mariko

PY - 2017/7/19

Y1 - 2017/7/19

N2 - In this paper, we evaluate eleven image descriptors for urban-rural classification using aerial images. The eleven image descriptors are composed by seven texture-based descriptors and four combinations of texture and color descriptors. The classification is carried out using Support Vector Machine (SVM) with radial basis function as kernel function. The performance of these images descriptors are evaluated using accuracy, precision, sensitivity and specificity. From the evaluation, the combination of Gabor descriptor and Dominant Color descriptor provides a better performance, obtaining accuracy more than 91%.

AB - In this paper, we evaluate eleven image descriptors for urban-rural classification using aerial images. The eleven image descriptors are composed by seven texture-based descriptors and four combinations of texture and color descriptors. The classification is carried out using Support Vector Machine (SVM) with radial basis function as kernel function. The performance of these images descriptors are evaluated using accuracy, precision, sensitivity and specificity. From the evaluation, the combination of Gabor descriptor and Dominant Color descriptor provides a better performance, obtaining accuracy more than 91%.

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KW - color-based descriptor

KW - texture-based descriptor

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PB - Institute of Electrical and Electronics Engineers Inc.

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Cortes D, Calderón G, Arista A, Toscano K , Nakano M. Aerial image classification using texture and color-based descriptors. In 2016 IEEE 1er Congreso Nacional de Ciencias Geoespaciales: Sustainable Geospatial Technology at Service of Society, CNCG 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 9-12. 7985077. (2016 IEEE 1er Congreso Nacional de Ciencias Geoespaciales: Sustainable Geospatial Technology at Service of Society, CNCG 2016 - Proceedings). doi: 10.1109/CNCG.2016.7985077

Aerial image classification using texture and color-based descriptors

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