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

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

3 Citas (Scopus)

Resumen

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%.

Idioma original	Inglés
Título de la publicación alojada	2016 IEEE 1er Congreso Nacional de Ciencias Geoespaciales
Subtítulo de la publicación alojada	Sustainable Geospatial Technology at Service of Society, CNCG 2016 - Proceedings
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	9-12
Número de páginas	4
ISBN (versión digital)	9781538618639
DOI	https://doi.org/10.1109/CNCG.2016.7985077
Estado	Publicada - 19 jul. 2017
Evento	1st IEEE Mexican Geospatial Science Congress, CNCG 2016 - Mexico City, México Duración: 7 dic. 2016 → 9 dic. 2016

Serie de la publicación

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

Conferencia

Conferencia	1st IEEE Mexican Geospatial Science Congress, CNCG 2016
País/Territorio	México
Ciudad	Mexico City
Período	7/12/16 → 9/12/16

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10.1109/CNCG.2016.7985077

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Cortes, D., Calderón, G., Arista, A., Toscano, K., & Nakano, M. (2017). Aerial image classification using texture and color-based descriptors. En 2016 IEEE 1er Congreso Nacional de Ciencias Geoespaciales: Sustainable Geospatial Technology at Service of Society, CNCG 2016 - Proceedings (pp. 9-12). Artículo 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",

author = "Daniel Cortes and Gustavo Calder{\'o}n and Antonio Arista and Karina Toscano and Mariko Nakano",

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year = "2017",

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Cortes, D, Calderón, G, Arista, A, Toscano, K & Nakano, M 2017, Aerial image classification using texture and color-based descriptors. En 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, México, 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Aerial image classification using texture and color-based descriptors

AU - Cortes, Daniel

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%.

KW - Image descriptor

KW - aerial image classification

KW - color-based descriptor

KW - texture-based descriptor

KW - urban-rural classification

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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. En 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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