Published

2020-10-12

Maximum Likelihood Classification of Soil Remote Sensing Image Based on Deep Learning

Clasificación de verosimilitud máxima de imágenes en teledetección del suelo con base en aprendizaje profundo automático

DOI:

https://doi.org/10.15446/esrj.v24n3.89750

Keywords:

Deep learning, Soil remote sensing image, Maximum likelihood estimation, Classification method (en)
Aprendizaje profundo, Imagen de teledetección del suelo, Estimación de máxima verosimilitud, Método de clasificación. (es)

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Authors

  • Shujun Liang Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou 450002, China
  • Jing Cheng Engineering Training Center, Zhengzhou University of Light Industry, Zhengzhou 450002, China
  • Jianwei Zhang Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou 450002, China

Soil remote sensing image classification is the most difficult in the National Soil Census work. Current soil remote sensing image classification methods based on deep learning and maximum likelihood estimation are challenging to meet the actual needs. Therefore, this paper combines deep learning with maximum likelihood estimation and proposes a maximum likelihood classification method for soil remote sensing images based on deep learning. The method is divided into four parts. Firstly, the pretreatment of soil remote sensing image is carried out, including three processes: image gray, image denoising, and image correction; secondly, the target of soil remote sensing image is detected by deep learning algorithm; thirdly, the maximum likelihood algorithm is used to classify soil remote sensing image; finally, the classification performance is tested by an example. The results show that this method can effectively segment the remote sensing image of soil, and the segmentation accuracy is high, which proves the effectiveness and superiority of the method.

La clasificación de imágenes de detección remota de suelos es la más difícil en el trabajo del Censo Nacional de Suelos en China. Los métodos vigentes de clasificación de imágenes de teledetección del suelo basados en el aprendizaje profundo y la estimación de máxima probabilidad no satisfacen las necesidades actuales. Por lo tanto, este documento combina el aprendizaje profundo con la estimación de máxima verosimilitud y propone un método de clasificación para estas imágenes de teledetección. En primer lugar, se lleva a cabo el preprocesamiento de la imagen de teledetección del suelo, lo que incluye tres procesos: imagen gris, eliminación de ruido y corrección de imagen; en segundo lugar, el objetivo de la imagen del suelo se detecta mediante un algoritmo de aprendizaje profundo; tercero, el algoritmo de máxima verosimilitud se usa para clasificar la imagen de detección remota del suelo; y, finalmente, el rendimiento de la clasificación se prueba con un ejemplo. Los resultados muestran que este método puede segmentar efectivamente la imagen de detección remota del suelo, y la precisión de la segmentación es alta, lo que demuestra la efectividad y superioridad del método.

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How to Cite

APA

Liang, S., Cheng, J. and Zhang, J. (2020). Maximum Likelihood Classification of Soil Remote Sensing Image Based on Deep Learning. Earth Sciences Research Journal, 24(3), 357–365. https://doi.org/10.15446/esrj.v24n3.89750

ACM

[1]
Liang, S., Cheng, J. and Zhang, J. 2020. Maximum Likelihood Classification of Soil Remote Sensing Image Based on Deep Learning. Earth Sciences Research Journal. 24, 3 (Oct. 2020), 357–365. DOI:https://doi.org/10.15446/esrj.v24n3.89750.

ACS

(1)
Liang, S.; Cheng, J.; Zhang, J. Maximum Likelihood Classification of Soil Remote Sensing Image Based on Deep Learning. Earth sci. res. j. 2020, 24, 357-365.

ABNT

LIANG, S.; CHENG, J.; ZHANG, J. Maximum Likelihood Classification of Soil Remote Sensing Image Based on Deep Learning. Earth Sciences Research Journal, [S. l.], v. 24, n. 3, p. 357–365, 2020. DOI: 10.15446/esrj.v24n3.89750. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/89750. Acesso em: 9 jul. 2024.

Chicago

Liang, Shujun, Jing Cheng, and Jianwei Zhang. 2020. “Maximum Likelihood Classification of Soil Remote Sensing Image Based on Deep Learning”. Earth Sciences Research Journal 24 (3):357-65. https://doi.org/10.15446/esrj.v24n3.89750.

Harvard

Liang, S., Cheng, J. and Zhang, J. (2020) “Maximum Likelihood Classification of Soil Remote Sensing Image Based on Deep Learning”, Earth Sciences Research Journal, 24(3), pp. 357–365. doi: 10.15446/esrj.v24n3.89750.

IEEE

[1]
S. Liang, J. Cheng, and J. Zhang, “Maximum Likelihood Classification of Soil Remote Sensing Image Based on Deep Learning”, Earth sci. res. j., vol. 24, no. 3, pp. 357–365, Oct. 2020.

MLA

Liang, S., J. Cheng, and J. Zhang. “Maximum Likelihood Classification of Soil Remote Sensing Image Based on Deep Learning”. Earth Sciences Research Journal, vol. 24, no. 3, Oct. 2020, pp. 357-65, doi:10.15446/esrj.v24n3.89750.

Turabian

Liang, Shujun, Jing Cheng, and Jianwei Zhang. “Maximum Likelihood Classification of Soil Remote Sensing Image Based on Deep Learning”. Earth Sciences Research Journal 24, no. 3 (October 12, 2020): 357–365. Accessed July 9, 2024. https://revistas.unal.edu.co/index.php/esrj/article/view/89750.

Vancouver

1.
Liang S, Cheng J, Zhang J. Maximum Likelihood Classification of Soil Remote Sensing Image Based on Deep Learning. Earth sci. res. j. [Internet]. 2020 Oct. 12 [cited 2024 Jul. 9];24(3):357-65. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/89750

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