Remote sensing image scene classification based on multilayer feature context encoding network

1.Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China;2.Research Center of Smart Networks and Systems, School of Information Science and Technology, Fudan University, Shanghai 200433, China

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Supported by National Natural Science Foundation of China (61971141 and 61731021)

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    Remote sensing image scene classification is one of the current hot topics in the field of remote sensing image processing. Since convolutional neural networks (CNNs) have powerful feature extraction capabilities, they have been widely applied in remote sensing image scene classification. However, the current methods have not fully considered and utilized the complementary information between different layers of CNN and the spatial context information of remote sensing images, resulting in that the corresponding classification accuracy needs to be improved. In order to address these issues, a multilayer feature context encoding (MFCE) network is proposed and utilized to solve the problem of scene classification for remote sensing images. The proposed network is composed of two parts: 1) A densely connected backbone; 2) A multiscale context encoding (MCE) module. The former is adopted to fuse the feature information of different layers of CNN, and the latter is utilized to encode and exploit the spatial context information that resides in the multilayer features. Experimental results on two large-scale remote sensing image datasets demonstrate that compared with the existing remote sensing image scene classification methods, the proposed network framework can achieve a significant gain in classification accuracy.

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LI Ruo-Yao, ZHANG Bo, WANG Bin. Remote sensing image scene classification based on multilayer feature context encoding network[J]. Journal of Infrared and Millimeter Waves,2021,40(4):530~538

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  • Received:October 09,2020
  • Revised:August 01,2021
  • Adopted:November 12,2020
  • Online: July 30,2021
  • Published: