Systems Engineering and Electronics ›› 2018, Vol. 40 ›› Issue (3): 682-691.doi: 10.3969/j.issn.1001-506X.2018.03.30

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General deep transfer features based high resolution remote scene classification

LUO Chang, WANG Jie, WANG Shiqiang, SHI Tong, REN Weihua   

  1. Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China
  • Online:2018-02-26 Published:2018-02-26

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Abstract:

Transferring deep convolutional neural network (DCNN) for high resolution remote scene classification is discussed. A promising architecture is proposed to enhance the generalization power of pre-trained DCNN for high resolution remote scene classification. Firstly, a linear principle component analysis network is designed to synthesize spatial information of high resolution remote sensing images. This design shortens the spatial distance between the target and source datasets for DCNN. Then pre-trained DCNN extract more general global features from the synthesized image. Experimental results of the two independent remote sensing datasets demonstrate that compared with state-of-the-art results, the proposed framework improves the accuracy of the remote scene classification without changing parameters in DCNN.

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