Mr. Yu-Jen Chen Profile

Yu-Jen Chen

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Proceedings Article | 6 September 2019 Paper

Classification of polarimetric SAR data based on subspace projection segmentation

Lena Chang, Yi-Ting Chen, Yang-Lang Chang, Yu-Jen Chen

Proceedings Volume 11132, 111320Z (2019) https://doi.org/10.1117/12.2529007

KEYWORDS: Surface plasmons, Polarimetry, Image segmentation, Speckle, Synthetic aperture radar

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The fully polarimetric SAR (PolSAR) data offers four polarimetric modes (i.e., HH, HV, VH, VV) and have shown the ability to provide better interpretation than single polarization case, which led to high classification accuracy. In this study, an efficient classification method for PolSAR data based on a subspace projection segmentation (SPS) approach is proposed to improve the classification accuracy. Instead of performing the comparison of multi-dimensional (MD) polarimetric feature vectors, the SPS first transforms the MD polarization feature vectors into one-dimensional (1D) projection lengths by projecting the feature vectors onto one reference subspace which is chosen to maximize the separation of two types of data. After the transformation, any 1D thresholding technique, such as the Otsu’s thresholding, can be applied to perform segmentation efficiently, which results in the reduction of computation complexity in segmentation. The proposed SPS can divide the data into proper homogeneous regions, that is, PolSAR data with similar polarization features being grouped together into regions. In the study, the polarimetric feature vectors are extracted from the coherent/covariance matrices obtained by the polarimetric scattering information. In addition, the referenced projection subspace is selected based on the coherent/covariance matrices of PolSAR data. Finally, the performance of the proposed SPS method is validated by simulations on PolSAR data obtained by NASA Airborne Synthetic Aperture Radar System (AIRSAR) during the PacRim II project and Advanced Land Observing Satellite (ALOS). Simulation results show that the proposed approach can reduce the computational complexity more effectively than other existing methods, and also achieve good classification accuracy.

Proceedings Article | 6 September 2019 Paper

Improved speckle filtering of polarimetric SAR by target signature preserving

Lena Chang, Ming-Hung Hung, Yang-Lang Chang, Yu-Jen Chen

Proceedings Volume 11132, 1113210 (2019) https://doi.org/10.1117/12.2529026

KEYWORDS: Image filtering, Speckle, Synthetic aperture radar

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The fully polarimetric synthetic aperture radar (PolSAR) with high resolution and quad-polarization data has shown the ability to provide better interpretation and high classification accuracy. But PolSAR image quality is critical disturbed due to speckle noise not only in the three intensities (HH, HV, VV) of PolSAR but also in the three complex correlation terms (HH-VV, VV-HV and HV-HH). Thus, reducing speckle noise level while preserving polarization scattering mechanisms and spatial resolution of PolSAR becomes a challenging task. Several filters widely used in SAR images can achieve effective speckle reduction, but can also lead to edge blurring and strong reflective scatter depressing. To mitigate these deficiencies, in the study, we proposed an improved speckle filtering approach by combining existing filters (such as Lee or Sigma filters) with edge/strong target detectors. Before performing filtering, strong targets and edges are detected and preserved by the proposed detectors. By the detection preprocessing, the improved filtering approach can not only reduce speckle noise but also preserve the target signature. The effectiveness of the proposed improved speckle filters is validated by two kinds of PolSAR data, one obtained by NASA Airborne Synthetic Aperture Radar System (AIRSAR) and the other data obtained by Advanced Land Observing Satellite(ALOS). From experimental results, the proposed improved filter provides promising results for suppressing speckle noise and preserving the potential targets.

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