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Open AccessArticle

A Novel Relational-Based Transductive Transfer Learning Method for PolSAR Images via Time-Series Clustering

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State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
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School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
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Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1358; https://doi.org/10.3390/rs11111358
Received: 7 May 2019 / Revised: 31 May 2019 / Accepted: 4 June 2019 / Published: 6 June 2019
(This article belongs to the Special Issue Time Series Analysis Based on SAR Images)
The combination of transfer learning and remote sensing image processing technology can effectively improve the automation level of image information extraction from a remote sensing time series. However, in the processing of polarimetric synthetic aperture radar (PolSAR) time-series images, the existing transfer learning methods often cannot make full use of the time-series information of the images, relying too much on the labeled samples in the target domain. Furthermore, the speckle noise inherent in synthetic aperture radar (SAR) imagery aggravates the difficulty of the manual selection of labeled samples, so these methods have difficulty in meeting the processing requirements of large data volumes and high efficiency. In lieu of these problems and the spatio-temporal relational knowledge of objects in time-series images, this paper introduces the theory of time-series clustering and proposes a new three-phase time-series clustering algorithm. Due to the full use of the inherent characteristics of the PolSAR images, this algorithm can accurately transfer the labels of the source domain samples to those samples that have not changed in the whole time series without relying on the target domain labeled samples, so as to realize transductive sample label transfer for PolSAR time-series images. Experiments were carried out using three different sets of PolSAR time-series images and the proposed method was compared with two of the existing methods. The experimental results showed that the transfer precision of the proposed method reaches a high level with different data and different objects and it performs significantly better than the existing methods. With strong reliability and practicability, the proposed method can provide a new solution for the rapid information extraction of remote sensing image time series. View Full-Text
Keywords: transfer learning; time-series images; PolSAR; time-series clustering; transductive transfer; relational knowledge transfer transfer learning; time-series images; PolSAR; time-series clustering; transductive transfer; relational knowledge transfer
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MDPI and ACS Style

Qin, X.; Yang, J.; Li, P.; Sun, W.; Liu, W. A Novel Relational-Based Transductive Transfer Learning Method for PolSAR Images via Time-Series Clustering. Remote Sens. 2019, 11, 1358.

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