郭从洲, 李可, 李贺, 童晓冲, 王习文. 遥感图像质量等级分类的深度卷积神经网络方法[J]. 武汉大学学报 ( 信息科学版), 2022, 47(8): 1279-1286. DOI: 10.13203/j.whugis20200292
引用本文: 郭从洲, 李可, 李贺, 童晓冲, 王习文. 遥感图像质量等级分类的深度卷积神经网络方法[J]. 武汉大学学报 ( 信息科学版), 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
Citation: 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

遥感图像质量等级分类的深度卷积神经网络方法

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

  • 摘要: 遥感图像应用发展对图像质量的要求越来越高,不同质量的遥感图像往往需要不同的处理方法和参数。通过遥感图像质量等级分类研究,不仅能够为遥感图像的处理提供先验信息,还能够对遥感图像的客观质量评价和传感器的成像效果进行评估。为了克服现有的遥感图像质量等级分类方法计算参数获取困难、等级数量少的缺点,利用深度学习方法的分类机能,通过改进特征提取网络和等级分类设计,建立了一种基于深度卷积神经网络的遥感图像质量等级分类模型。通过质量等级分类预处理后,利用经典的深度学习方法进行目标检测实验。结果表明,所提方法在西北工业大学遥感图像数据集上质量等级分类的准确率、召回率、精确率和F1最高能达到0.976、0.972、0.974和0.973, 优于传统算法。利用卷积神经网络实现遥感图像质量等级分类,既拓展了深度学习的应用领域,又为遥感图像质量评估提供了一个新方法。

     

    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.

     

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