Published

2018-01-01

A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm

Coocurrencia de región basada en una red bayesiana gradual hacia un algoritmo de recuperación de imágenes por teledetección

DOI:

https://doi.org/10.15446/esrj.v22n1.66107

Keywords:

Bayesian network, Co-occurrence region, Remote sensing image retrieval (en)
red bayesiana, región de coocurrencia, recuperación de imágenes por teledetección. (es)

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Authors

  • Rui Zeng
  • Yingyan Wang
  • Wanliang Wang

Although scholars have conducted numerous researches on content-based image retrieval and obtained great achievements, they make little progress in studying remote sensing image retrieval. Both theoretical and application systems are immature. Since remote sensing images are characterized by large data volume, broad coverage, vague themes and rich semantics, the research results on natural images and medical images cannot be directly used in remote sensing image retrieval. Even perfect content-based remote sensing image retrieval systems have many difficulties with data organization, storage and management, feature description and extraction, similarity measurement, relevance feedback, network service mode, and system structure design and implementation. This paper proposes a remote sensing image retrieval algorithm that combines co-occurrence region based Bayesian network image retrieval with average high-frequency signal strength. By Bayesian networks, it establishes correspondence relationships between images and semantics, thereby realizing semantic-based retrieval of remote sensing images. In the meantime, integrated region matching is introduced for iterative retrieval, which effectively improves the precision of semantic retrieval.

A pesar de que muchos investigadores han realizado numerosos trabajos sobre la consulta de imágenes mediante ejemplo y han obtenido grandes logros, poco se ha avanzado en la recuperación de imágenes por teledetección. Tanto la teoría como la aplicación de los sistemas son inmaduros. Ya que las imágenes por teledetección se caracterizan por un gran volumen de información, amplia cobertura, temas difusos y semántica abundante, los resultados de las investigaciones en imágenes naturales e imágenes médicas estos no pueden ser usados directamente en la recuperación de imágenes por teledetección. Incluso en una consulta perfecta de imágenes mediante ejemplo, los sistemas tienen muchas dificultades con la organización de información, almacenamiento y manejo, descripción de características y extracción, medición de similitudes, retroalimentación relevante, modo de servicio de red y diseño e implementación del sistema estructural. Este artículo propone un algoritmo de recuperación de imágenes por teledetección que combina la coocurrencia local de una red bayesiana de recuperación de imagénes con el promedio de potencia de la señal de alta frecuencia. Por las redes bayesianas, se establecen las relaciones de correspondencia entre imágenes y semántica, además de permitir la recuperación de imágenes de teledetección a través de la semántica. Mientras tanto, se desarrolló el módulo de región integrada para la recuperación repetitiva, lo que mejora efectivamente la precisión de la recuperación semántica. 

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How to Cite

APA

Zeng, R., Wang, Y. and Wang, W. (2018). A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm. Earth Sciences Research Journal, 22(1), 29–35. https://doi.org/10.15446/esrj.v22n1.66107

ACM

[1]
Zeng, R., Wang, Y. and Wang, W. 2018. A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm. Earth Sciences Research Journal. 22, 1 (Jan. 2018), 29–35. DOI:https://doi.org/10.15446/esrj.v22n1.66107.

ACS

(1)
Zeng, R.; Wang, Y.; Wang, W. A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm. Earth sci. res. j. 2018, 22, 29-35.

ABNT

ZENG, R.; WANG, Y.; WANG, W. A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm. Earth Sciences Research Journal, [S. l.], v. 22, n. 1, p. 29–35, 2018. DOI: 10.15446/esrj.v22n1.66107. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/66107. Acesso em: 19 jul. 2024.

Chicago

Zeng, Rui, Yingyan Wang, and Wanliang Wang. 2018. “A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm”. Earth Sciences Research Journal 22 (1):29-35. https://doi.org/10.15446/esrj.v22n1.66107.

Harvard

Zeng, R., Wang, Y. and Wang, W. (2018) “A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm”, Earth Sciences Research Journal, 22(1), pp. 29–35. doi: 10.15446/esrj.v22n1.66107.

IEEE

[1]
R. Zeng, Y. Wang, and W. Wang, “A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm”, Earth sci. res. j., vol. 22, no. 1, pp. 29–35, Jan. 2018.

MLA

Zeng, R., Y. Wang, and W. Wang. “A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm”. Earth Sciences Research Journal, vol. 22, no. 1, Jan. 2018, pp. 29-35, doi:10.15446/esrj.v22n1.66107.

Turabian

Zeng, Rui, Yingyan Wang, and Wanliang Wang. “A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm”. Earth Sciences Research Journal 22, no. 1 (January 1, 2018): 29–35. Accessed July 19, 2024. https://revistas.unal.edu.co/index.php/esrj/article/view/66107.

Vancouver

1.
Zeng R, Wang Y, Wang W. A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm. Earth sci. res. j. [Internet]. 2018 Jan. 1 [cited 2024 Jul. 19];22(1):29-35. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/66107

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CrossRef citations2

1. Pei Yin, Liang Zhang. (2020). Image Recommendation Algorithm Based on Deep Learning. IEEE Access, 8, p.132799. https://doi.org/10.1109/ACCESS.2020.3007353.

2. Xueting Yue, Junmin Wang, Ruiyao Wang, Zexun Geng. (2021). A technology of invariant feature extraction of Uav remote sensing image based on fuzzy fractional order function. Arabian Journal of Geosciences, 14(18) https://doi.org/10.1007/s12517-021-07860-3.

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