Geoinformatics Unit

Gerald BAIER

  

Current Position

Gerald Baier is a postdoctoral researcher at RIKEN Center for Advanced Intelligence Project's Geoinformatics Unit.
His research interests are digital signal processing and machine learning for synthetic aperture radar interferometry and polarimetry, and high-performance computing.

Biography

2018 Sep - Present    Postdoctoral researcher, RIKEN AIP, Japan
2014 Apr - 2018 Jul    Ph.D. at the Remote Sensing Technology Institute of the German Aerospace Center (DLR) and the Technical University of Munich, Germany
2010 Sep - 2012 Nov    M.Sc. in Information and Communication Technologies, Université catholique de Louvain, Belgium and Karlsruhe Institute of Technology, Germany
2007 Oct - 2010 Aug    B.Sc. in Electrical Engineering, Karlsruhe Institute of Technology, Germany

Journal Papers

  1. G. Baier, W. He, and N. Yokoya, " Robust nonlocal low-rank SAR time series despeckling considering speckle correlation by total variation regularization ," IEEE Transactions on Geoscience and Remote Sensing (Early Access), 2020.
    Quick Abstract

    Abstract: Outliers and speckle both corrupt synthetic aperture radar (SAR) time series. Furthermore, due to the coherence between SAR acquisitions, their speckle can no longer be regarded as independent. We propose a nonlocal low-rank time series despeckling algorithm that is robust against outliers and also specifically addresses speckle correlation between acquisitions. By imposing a total variation regularization on the signal’s speckle component, its correlation between acquisition can be captured, facilitating the extraction of outliers from the unfiltered signal and correlated speckle. Robustness against outliers also addresses matching errors and inaccuracies in the nonlocal similarity search. Such errors include mismatched data in the nonlocal estimation process, which degrade denoising performance in conventional similarity-based filtering approaches. Multiple experiments on real and synthetic data assess the proposed approaches performance by comparing it to state-of-the-art methods. It provides filtering results of comparable quality but is not adversely affected by outliers.

  2. J. Kang, D. Hong, J. Liu, G. Baier, N. Yokoya, and B. Demir, " Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration ," IEEE Transactions on Neural Networks and Learning Systems, 2020.
  3. B. Adriano, J. Xia, G. Baier, N. Yokoya, S. Koshimura, " Multi-source data fusion based on ensemble learning for rapid building damage mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia ," Remote Sensing, vol. 11, no. 7, pp. 886, 2019.
    PDF    Quick Abstract

    Abstract: This work presents a detailed analysis of building damage recognition, employing multi-source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A damage classification framework is introduced and tested to categorize the building damage following the recent 2018 Sulawesi earthquake and tsunami. Three robust ensemble learning classifiers were investigated for recognizing building damage from SAR and optical remote sensing datasets and their derived features. The contribution of each feature dataset was also explored, considering different combinations of sensors as well as their temporal information. SAR scenes acquired by the ALOS-2 PALSAR-2 and Sentinel-1 sensors were used. The optical Sentinel-2 and PlanetScope sensors were also included in this study. A non-local filter in the preprocessing phase was used to enhance the SAR features. Our results demonstrated that the canonical correlation forests classifier performs better in comparison to the other classifiers. In the data fusion analysis, DEM- and SAR-derived features contributed the most in the overall damage classification. Our proposed mapping framework successfully classifies four levels of building damage (with overall accuracy > 90%, average accuracy > 67%). The proposed framework learned the damage patterns from a limited available human-interpreted building damage annotation and expands this information to map a larger affected area. This process including pre- and post-processing phases were completed in about 3 hours after acquiring all raw datasets.