Polsarnet: A deep fully convolutional network for polarimetric sar image classification

Adugna G. Mullissa*, Claudio Persello, Alfred Stein

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)


Deep learning has successfully improved the classification accuracy of optical remote sensing images. Recent works attempted to transfer the success of these techniques to the microwave domain to classify Polarimetric SAR data. So far, most deep learning networks separate amplitude and phase as separate input images. In this article, we present a deep fully convolutional network that uses real-valued weight kernels to perform pixel-wise classification of complex-valued images. We evaluated the performance of this network by comparing it with support vector machine, Random Forest, complex-valued convolutional neural network (CV-CNN), and a network that uses amplitude and phase information separately as real channels. The evaluation was done on a quad-polarized AIRSAR image and a dual-polarimetric multitemporal Sentinel-1 data acquired over Flevoland, the Netherlands. The proposed method achieved higher accuracy compared to all other networks with the same architecture.

Original languageEnglish
Article number8936481
Pages (from-to)5300-5309
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number12
Publication statusPublished - Dec 2019


  • Convolutional neural network (CNN)
  • deep learning
  • image classification
  • machine learning
  • polarimetric SAR (PolSAR)

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