Dual-branch deep convolution neural network for polarimetric SAR image classification

Fei Gao, Teng Huang, Jun Wang, Jinping Sun, Amir Hussain, Erfu Yang

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)
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Deep convolution neural network (CNN), which has prominent advantages in feature learning, can learn and extract features from data automatically. Existing polarimetric synthetic aperture radar (PolSAR) image classification methods based on CNN only consider the polarization information of the image, instead of incorporating the image’s spatial information. In this paper, a novel method based on dual-branch deep convolution neural network (Dual-CNN) is proposed to realize the classification of PolSAR images. The proposed method is built on two deep CNNs: one is used to extract the polarization features from the 6-channel real matrix (6Ch) which is derived from complex coherency matrix. The other is utilized to extract the spatial features of Pauli RGB image. These extracted features are first combined into a fully connected layer sharing the polarization and spatial property. Then the Softmax classifier is employed to classify these features. The experiments are conducted on the AIRSAR data of Flevoland and the results show that the classification accuracy on 14 types of land cover is up to 98.56%. Such results are promising in comparison with other the state of the art methods.
Original languageEnglish
Article number447
Number of pages18
JournalApplied Sciences
Issue number5
Early online date27 Apr 2017
Publication statusPublished - 31 May 2017


  • deep convolution neural network
  • dual-branch convolution neural network
  • land cover classification
  • polarimetric SAR images


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