Acta Geodaetica et Cartographica Sinica ›› 2022, Vol. 51 ›› Issue (4): 475-487.doi: 10.11947/j.AGCS.2022.20220027

• The 90th Anniversary of Tongji University Surveying and Mapping Discipline • Previous Articles     Next Articles

The design of deep learning framework and model for intelligent remote sensing

GONG Jianya1, ZHANG Mi1, HU Xiangyun1, ZHANG Zhan2, LI Yansheng1, Jiang Liangcun1   

  1. 1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;
    2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China
  • Received:2022-01-14 Revised:2022-03-10 Published:2022-04-24
  • Supported by:
    Major Program of the National Natural Science Foundation of China (No. 92038301); The National Natural Science Foundation of China (No. 41901265)

Abstract: The rapid development of remote sensing technology has achieved massive remote sensing images, and the deep-learning-based remote sensing image interpretation has shown certain advantages in image feature extraction and representation. However, the intelligent processing framework and information service capabilities are relatively lagging. Open-source deep learning frameworks and models cannot yet meet the requirements of intelligent remote sensing processing. Based on the analysis of existing intelligent frameworks and models, we design a dedicated deep learning framework and model with remote sensing characteristics for the problems of large remote sensing image size, large-scale changes, and multiple data channels. The focus is on the construction of a dedicated framework that takes into account remote sensing data characteristics and the preliminary experimental results on remote sensing image classification. The design of this remote sensing image interpretation framework will provide strong support for the construction of a dedicated deep learning framework and models that integrate the temporal, spatial, and spectral features of remote sensing data.

Key words: remote sensing intelligent interpretation, deep learning, dedicated framework and model, remote sensing feature

CLC Number: