Gerald Baier

Gerald Baier
RIKEN | RIKEN AICS · Center for Advanced Intelligence Project

About

24
Publications
5,602
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
441
Citations

Publications

Publications (24)
Article
This work critically analyzes the problems arising from differ-modality building semantic segmentation in the remote sensing domain. With the growth of multimodality datasets, such as optical, synthetic aperture radar (SAR), light detection and ranging (LiDAR), and the scarcity of semantic knowledge, the task of learning multimodality information h...
Article
Full-text available
We synthesize both optical RGB and synthetic aperture radar (SAR) remote sensing images from land cover maps and auxiliary raster data using generative adversarial networks (GANs). In remote sensing, many types of data, such as digital elevation models (DEMs) or precipitation maps, are often not reflected in land cover maps but still influence imag...
Preprint
Full-text available
We synthesize both optical RGB and SAR remote sensing images from land cover maps and auxiliary raster data using GANs. In remote sensing many types of data, such as digital elevation models or precipitation maps, are often not reflected in land cover maps but still influence image content or structure. Including such data in the synthesis process...
Article
Full-text available
We propose a framework that estimates the inundation depth (maximum water level) and debris-flow-induced topographic deformation from remote sensing imagery by integrating deep learning and numerical simulation. A water and debris-flow simulator generates training data for various artificial disaster scenarios. We show that regression models based...
Preprint
Full-text available
We propose a framework that estimates inundation depth (maximum water level) and debris-flow-induced topographic deformation from remote sensing imagery by integrating deep learning and numerical simulation. A water and debris flow simulator generates training data for various artificial disaster scenarios. We show that regression models based on A...
Article
Outliers and speckle both corrupt time series of synthetic aperture radar (SAR) acquisitions. Owing to the coherence between SAR acquisitions, their speckle can no longer be regarded as independent. In this study, we propose an algorithm for nonlocal low-rank time series despeckling, which is robust against outliers and also specifically addresses...
Preprint
The demo codes of this paper is under: https://github.com/jiankang1991/ComCSC ----------------------------------------------------------------------------------------------------------------------------------- Interferometric phase restoration has been investigated for decades and most of the state-of-the-art methods have achieved promising perform...
Conference Paper
This study employed Sentinel-1A C-band and Sentinel-2A multispectral data combined with the decision tree ensemble algorithms to map the spatial distribution of five mangrove communities in a coastal area in North Vietnam. The results show that the rotation forests (RoFs) model achieved better overall accuracy and kappa coefficient in mapping mangr...
Article
Full-text available
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 learn...
Article
Full-text available
This article investigates the potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection in comparison to conventional TanDEM-X data, i.e. image pairs acquired in repeat-pass or bistatic mode. For this task, an unsupervised coastline detection procedure based on scale-space representations and K-medians clustering as...
Article
Full-text available
This paper presents a nonlocal InSAR filter with the goal of generating digital elevation models of higher resolution and accuracy from bistatic TanDEM-X strip map interferograms than with the processing chain used in production. The currently employed boxcar multilooking filter naturally decreases the resolution and has inherent limitations on wha...
Preprint
Full-text available
This paper presents a nonlocal InSAR filter with the goal of generating digital elevation models of higher resolution and accuracy from bistatic TanDEM-X strip map interferograms than with the processing chain used in production. The currently employed boxcar multilooking filter naturally decreases the resolution and has inherent limitations on wha...
Conference Paper
Full-text available
We investigate the feasibility of generating highly accurate digital elevation models (DEM) from TanDEM-X interferograms by using nonlocal filters for phase denoising. Some of the shortcomings of existing nonlocal filters that render them not applicable to our goal are briefly described and a new filter is proposed that alleviates these problems. T...
Conference Paper
Full-text available
This paper presents the first results generated with the TanDEM-X mission for the monitoring of the topographical changes caused by the series of earthquakes that hit central Italy between summer and autumn 2016. For the purpose, two 300 km long data takes acquired between the Tyrrhenian and the Adriatic coasts and covering the October, 30, earthqu...
Conference Paper
Full-text available
We present a nonlocal synthetic aperture radar interferometry (InSAR) filter for TanDEM-X bistatic strip map interferograms, with the goal of generating a digital elevation model (DEM) from TanDEM-X interferograms with a higher resolution and accuracy than the default product. The filter is especially tuned for DEM generation as it takes into accou...
Conference Paper
In the past few years nonlocal filters have emerged as a serious contender for denoising synthetic aperture radar (SAR) images, offering superior noise reduction and detail preservation compared to many other filters. In this manuscript we analyze how nonlocal filters, whose computational costs were so far prohibitive for large scale processing, ca...
Conference Paper
A nonlocal InSAR filter is proposed that avoids the staircasing effect, which is especially troubling for DEM generation as it leads to terrace-like artifacts for hilly terrain. The sources of the staircasing effect for InSAR filtering are presented and the proposed filter is evaluated with simulations and test data, which show that the noise reduc...
Conference Paper
This paper proposes a nonlocal filter variant that replaces the conventional static search window of nonlocal InSAR filters with an adaptive region growing based search window. The region growing approach has the allure that it preselects only similar pixels for the averaging process and that it may find a larger number of statistically homogeneous...
Article
Full-text available
The speckle is omnipresent in synthetic aperture radar (SAR) images as an intrinsic characteristic. However, it is unwanted in certain applications. Therefore, intelligent filters for speckle reduction are of great importance. It has been demonstrated in several literatures that the non-local means filter can reduce noise while preserving details....
Conference Paper
The speckle is omnipresent in synthetic aperture radar (SAR) images as an intrinsic characteristic. However, it is unwanted in certain applications. Therefore, intelligent filters for speckle reduction are of great importance. It has been demonstrated in several literatures that the non-local means filter can reduce noise while preserving details....

Network

Cited By