سابِق|تال

Classification of high-resolution remote sensing images based on enhanced DeepLab algorithm and adaptive loss function

  • role： First author第一作者
  • Affiliation：

    National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：xuzeyu@aircas.ac.cn
  • Introduction：许泽宇，1995年生，男，硕士研究生，研究方向为遥感信息计算。E-mail： xuzeyu@aircas.ac.cn
XU Zeyu12,  
  • role： Corresponding author通信作者
  • Affiliation：

    National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：shenzf@radi.ac.cn
  • Introduction：沈占锋，1977年生，男，研究员，研究方向为遥感信息计算。E-mail： shenzf@radi.ac.cn
SHEN Zhanfeng14*,  
  • Affiliation：

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

LI Yang23,  
  • Affiliation：

    National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

ZHAO Lifang14,  
  • Affiliation：

    National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

KE Yingming14,  
  • Affiliation：

    National Disaster Reduction Center of China, Beijing 100124, China

LI Lingling5,  
  • Affiliation：

    National Disaster Reduction Center of China, Beijing 100124, China

WEN Qi5

ملخص

High-resolution remote sensing images are complex and difficult to classify. Deep learning methods can extract more and deeper information of the features, which is suitable for the classification of high-resolution remote sensing images. This paper studies the high-precision classification of impervious ground, buildings, low vegetation, trees, vehicles, and other features in high-resolution images. An E-DeepLab network model is proposed by combining the characteristics of remote sensing multiground feature classification based on DeepLab v3+ network model. The main improvements are as follows: (1) Improving the combination of encoder and decoder modules and using a simple and effective addition connection method. (2) Reducing the single upsampling multiple, increasing the upsampling layer, and improving the tightness of the connection between the encoder and the decoder. (3) Using the improved adaptive weight loss function to automatically adjust the weight of losses. In accordance with the characteristics of the data, a multichannel training method combining digital surface model and normalized difference vegetation index data is proposed. Using the data from the two regions to conduct experiments, the overall extraction accuracy in Potsdam region reaches 93.2%, the extraction accuracy in buildings reaches 97.8%, the overall extraction accuracy in Vaihingen region reaches 90.7%, and the accuracy of building extraction reaches 96.3%. The accuracy of the two regions is significantly better than the original DeepLab v3+ model and other related models. The visual effect of the classification results is extremely close to the standard map by comparing the classification map and the standard marker map. Results show that the E-DeepLab network has good application value in the feature extraction and classification of high-precision remote sensing images.

مفهوم

remote sensing;high-resolution images;deep learning;E-DeepLab;adaptive weight loss function

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