Explore the Abilities and Potentials of Deep Transfer Learning on High-resolution PolSAR Image Interpretations

Konferenz: EUSAR 2018 - 12th European Conference on Synthetic Aperture Radar
04.06.2018 - 07.06.2018 in Aachen, Germany

Tagungsband: EUSAR 2018

Seiten: 4Sprache: EnglischTyp: PDF

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Wu, Wenjin (Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 10094, China)
Li, Hailei (Information Engineering School, Nanchang University, Nanchang, 330000, China)

Deep learning (DL) is the hottest technology of the big data era, and the state-of-the-art performances of various nat-ural and optical image interpretation tasks have been achieved by deep convolutional neural networks (DCNNs). To explore the abilities and potentials of DL, we solve a scene classification problem through transfer learning based on different kinds of DCNNs trained with the Imagenet dataset. We would like to answer “What kind of features can a advanced DCNN learn for PolSAR images? How are they different from the ones obtained for photos or optical im-ages? Will specific filter layers learn speckle noise and are the learned features smooth?” during the experiment.