Deep Convolution Neural Network Method for Remote Sensing Image Quality Level Classification

GUO Congzhou; LI Ke; LI He; TONG Xiaochong; WANG Xiwen

doi:10.13203/j.whugis20200292

Volume 47 Issue 8

Aug. 2022

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Geomatics and Information Science of Wuhan University > 2022 > 47(8): 1279-1286. > DOI: 10.13203/j.whugis20200292

GUO Congzhou, LI Ke, LI He, TONG Xiaochong, WANG Xiwen. Deep Convolution Neural Network Method for Remote Sensing Image Quality Level Classification[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1279-1286. DOI: 10.13203/j.whugis20200292

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Deep Convolution Neural Network Method for Remote Sensing Image Quality Level Classification

1.
Department of Basic, Information Engineering University, Zhengzhou 450001, China
2.
School of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China

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The National Natural Science Foundation of China 41671409

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Author Bio:
GUO Congzhou, PhD, associate professor, specializes in the theory and method of deep learning and remote sensing image processing. E-mail: czguo0618@sina.cn
Corresponding author:
LI Ke, PhD, lecturer. E-mail: like@lsec.cc.ac.cn
Received Date: June 19, 2021
Available Online: August 15, 2022
Published Date: August 04, 2022

Graphical Abstract

Abstract

Abstract

Objectives The application and development of remote sensing image requires higher and higher image quality. Different processing methods and parameters are often needed for different quality remote sensing images, which is not suitable for the intelligent demand. Through the classification of remote sensing image quality level, it can provide prior information for remote sensing image processing, evaluate the objective quality of remote sensing image, assessment the effect of sensor imaging. With the development and popularization of deep learning theory, it is possible to evaluate the quality of digital images by using deep convolution neural network.
Methods We propose a classification model of quality classification for remote sensing images based on deep convolution neural network. It is established by improving the feature extraction network and classification design.After the quality classification pretreatment, the classical deep learning method is used to detect the target, and the detection accuracy is obviously improved, which can effectively solve the problem of unbalanced quality of the training set data.
Results The experimental results show that this proposed method is better than the traditional method. The highest score value of accuracy, recall, precision and F1_score can reach 0.976, 0.972, 0.974 and 0.973 on the remote sensing image data set of Northwestern Polytechnic University.
Conclusions The classification of remote sensing image quality by convolution neural network extends the application field of deep learning. It provides a new method for the quality evaluation of remote sensing image. The classical deep learning method is used to detect the target, and the detection accuracy is obviously improved though quality classification. It provides a way to solve the problem of imbalance in remote sensing image quality.
- remote sensing image,
- quality level classification,
- objective quality evaluation,
- deep learning,
- convolution neural network

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