Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units

Shui Hua Wang, Junding Sun, Preetha Phillips, Guihu Zhao, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

109 Citations (Scopus)

Abstract

Image segmentation is an important application of polarimetric synthetic aperture radar. This study aimed to create an 11-layer deep convolutional neural network for this task. The Pauli decomposition formed the RGB image and was used as the input. We created an 11-layer convolutional neural network (CNN). L-band data over the San Francisco bay area and C-band data over Flevoland area were employed as the dataset. For the San Francisco bay PSAR image, our method achieved an overall accuracy of 97.32%, which was at least 2% superior to four state-of-the-art approaches. We provided the confusion matrix over test area, and the kernel visualization. We compared the max pooling and average pooling. We validated by experiment that four convolution layers perform the best. Besides, our method gave better results than AlexNet. The GPU yields a 173× acceleration on the training samples, and a 181× acceleration on the test samples, compared to standard CPU. For the Flevoland PSAR image, our 11-layer CNN also gives better overall accuracy than five state-of-the-art approaches. The convolutional neural network is better than traditional classifiers and is effective in remote sensing image segmentation.

Original languageEnglish
Pages (from-to)631-642
Number of pages12
JournalJournal of Real-Time Image Processing
Volume15
Issue number3
DOIs
Publication statusPublished - 1 Oct 2018
Externally publishedYes

Keywords

  • AlexNet
  • Confusion matrix
  • Convolution layer
  • Convolutional neural network
  • Graphical processing unit
  • Image segmentation
  • Max pooling
  • Pauli decomposition
  • Polarimetric synthetic aperture radar
  • Rectified linear unit

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