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A Semantic Segmentation Approach Based on DeepLab Network in High-Resolution Remote Sensing Images

  • Hangtao Hu
  • Shuo Cai
  • Wei WangEmail author
  • Peng Zhang
  • Zhiyong Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11903)

Abstract

Recently, more and more applications for high-resolution remote sensing image intelligent processing are required. Therefore, the semantic segmentation based on deep learning has successfully attracted people’s attention. In this paper, the improved Deeplabv3 network is used in the application of image semantic segmentation. The problem of segmenting objects of multiple scales of high-resolution remote sensing image is handled, and the Chinese GaoFen NO. 2(GF-2) remote sensing image is taken as the main research object. Firstly, the original image is pre-processed. Next, use data augmentation and expansion for the pre-processed training image to avoid over-fitting. Finally, it is studied the adaptability and accuracy of the model of high-resolution remote sensing images, while is found the appropriate parameters to improve the precise of the result models compared. And explore the effectiveness of the model in the case of a fewer samples. This model is demonstrated that could be achieved the well classification result.

Keywords

Remote sensing image classification Deep learning Semantic segmentation 

Notes

Acknowledgment

This work was supported by the National Nature Science Foundation, P.R. China 61702052 and 61070040 and also supported by Hunan Provincial Education Department under grant 18A137 and 17C043.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hangtao Hu
    • 1
    • 2
  • Shuo Cai
    • 1
  • Wei Wang
    • 1
    • 2
    Email author
  • Peng Zhang
    • 2
  • Zhiyong Li
    • 2
  1. 1.Changsha University of Science and Technology, School of Computer and Communication EngineeringChangshaChina
  2. 2.Hunan Shenfan Technology Co., Ltd.ChangshaChina

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