Experimental Validation of Conifer and Broad-Leaf Tree Classification Using High Resolution PolSAR Data above X-Band

Synthetic Aperture Radar (SAR) is an established remote sensing technique that can robustly provide high-resolution imagery of the Earth’s surface. However, current space-borne SAR systems are limited, as a matter of principle, in achieving high azimuth resolution and a large swath width at the same time. Digital beamforming (DBF) has been identified as a key technology for resolving this limitation and provides various other advantages, such as an improved signal-to-noise ratio (SNR) or the adaptive suppression of radio interference (RFI). Airborne SAR sensors with digital beamforming capabilities are essential tools to research and validate this important technology for later implementation on a satellite. Currently, the Microwaves and Radar Institute of the German Aerospace Center (DLR) is developing a new advanced high-resolution airborne SAR system with digital beamforming capabilities, the so-called DBFSAR, which is planned to supplement its operational F-SAR system in near future. It is operating at X-band and features 12 simultaneous receive and 4 sequential transmit channels with 1.8 GHz bandwidth each, flexible DBF antenna setups and is equipped with a high-precision navigation and positioning unit. This paper aims to present the DBFSAR sensor development, including its radar front-end, its digital back-end, the foreseen DBF antenna configuration and the intended calibration strategy. To analyse the status, performance, and calibration quality of the DBFSAR system, this paper also includes some first in-flight results in interferometric and multi-channel marine configurations. They demonstrate the excellent performance of the DBFSAR system during its first flight campaigns.

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Studying the Applicability of X-Band SAR Data to the Network-Scale Mapping of Pavement Roughness on US Roads

Remote Sensing ◽

10.3390/rs12091507 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1507 ◽

Cited By ~ 2

Author(s):

Franz J. Meyer ◽

Olaniyi A. Ajadi ◽

Edward J. Hoppe

Keyword(s):

High Resolution ◽

Remote Sensing Data ◽

Ride Quality ◽

International Roughness Index ◽

Pavement Roughness ◽

X Band ◽

The Us ◽

Network Scale ◽

Sar Data ◽

Satellite Radar

The traveling public judges the quality of a road mostly by its roughness and/or ride quality. Hence, mapping, monitoring, and maintaining adequate pavement smoothness is of high importance to State Departments of Transportation in the US. Current methods rely mostly on in situ measurements and are, therefore, time consuming and costly when applied at the network scale. This paper studies the applicability of satellite radar remote sensing data, specifically, high-resolution Synthetic Aperture Radar (SAR) data acquired at X-band, to the network-wide mapping of pavement roughness of roads in the US. Based on a comparison of high-resolution X-band Cosmo-SkyMed images with road roughness data in the form of International Roughness Index (IRI) measurements, we found that X-band radar brightness generally increases when pavement roughness worsens. Based on these findings, we developed and inverted a model to distinguish well maintained road segments from segments in need of repair. Over test sites in Augusta County, VA, we found that our classification scheme reaches an overall accuracy of 92.6%. This study illustrates the capacity of X-band SAR for pavement roughness mapping and suggests that incorporating SAR into DOT operations could be beneficial.

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Network-scale pavement roughness mapping using spaceborne high-resolution X-band SAR data

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2017.8127267 ◽

2017 ◽

Author(s):

Franz J Meyer ◽

Olaniyi A Ajadi ◽

Edward Hoppe

Keyword(s):

High Resolution ◽

Pavement Roughness ◽

X Band ◽

Network Scale ◽

Sar Data

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Simulation and experimental validation of high-resolution test objects for evaluating a next-generation digital breast tomosynthesis prototype

Medical Imaging 2019: Physics of Medical Imaging ◽

10.1117/12.2511304 ◽

2019 ◽

Cited By ~ 2

Author(s):

Trevor L. Vent ◽

Bruno Barufaldi ◽

Andrew D. A. Maidment

Keyword(s):

High Resolution ◽

Experimental Validation ◽

Digital Breast Tomosynthesis ◽

Next Generation ◽

Breast Tomosynthesis ◽

Test Objects

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Brief Communication: Rapid mapping of landslide events: the 3 December 2013 Montescaglioso landslide, Italy

Natural Hazards and Earth System Science ◽

10.5194/nhess-14-1835-2014 ◽

2014 ◽

Vol 14 (7) ◽

pp. 1835-1841 ◽

Cited By ~ 43

Author(s):

A. Manconi ◽

F. Casu ◽

F. Ardizzone ◽

M. Bonano ◽

M. Cardinali ◽

...

Keyword(s):

High Resolution ◽

Synthetic Aperture Radar ◽

Southern Italy ◽

Surface Deformation ◽

Synthetic Aperture ◽

Sar Images ◽

Main Road ◽

Rapid Mapping ◽

X Band ◽

Sar Data

Abstract. We present an approach to measure 3-D surface deformations caused by large, rapid-moving landslides using the amplitude information of high-resolution, X-band synthetic aperture radar (SAR) images. We exploit SAR data captured by the COSMO-SkyMed satellites to measure the deformation produced by the 3 December 2013 Montescaglioso landslide, southern Italy. The deformation produced by the deep-seated landslide exceeded 10 m and caused the disruption of a main road, a few homes and commercial buildings. The results open up the possibility of obtaining 3-D surface deformation maps shortly after the occurrence of large, rapid-moving landslides using high-resolution SAR data.

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Identifying Building Damage Patterns in the 2016 Meinong, Taiwan Earthquake Using Post-Event Dual-Polarimetric ALOS-2/PALSAR-2 Imagery

Journal of Disaster Research ◽

10.20965/jdr.2018.p0291 ◽

2018 ◽

Vol 13 (2) ◽

pp. 291-302 ◽

Cited By ~ 1

Author(s):

Yanbing Bai ◽

◽

Bruno Adriano ◽

Erick Mas ◽

Shunichi Koshimura

Keyword(s):

Statistical Analysis ◽

High Resolution ◽

Synthetic Aperture Radar ◽

Building Damage ◽

Synthetic Aperture ◽

Color Features ◽

X Band ◽

Radar Imagery ◽

Synthetic Aperture Radar Imagery ◽

Aperture Radar

The 2016 magnitude 6.4 Meinong earthquake caused catastrophic damage to peoples lives and properties in Taiwan. Synthetic Aperture Radar remote sensing is a useful tool to rapidly grasp the near real-time building damage to areas affected by the earthquake. Previous studies employed X-band single polarized high-resolution synthetic aperture radar imagery to identify building damage. However, suitable X-band single polarized high-resolution synthetic aperture radar imagery is not always accessible. Therefore, this research applied L-band dual-polarimetric ALOS-2/PALSAR-2 data to analyze the radar scattering characteristics of three types of affected buildings in the 2016 Meinong earthquake. The results show that collapsed buildings are characterized by a weak double-bounce scattering due to a reduced dihedral structure, while the characteristics of slightly damaged buildings are similar to those of undamaged buildings. Furthermore, the discrimination ability of a series of polarimetric, texture, and color features derived from the dual-polarimetric SAR data for three types of buildings affected by the earthquake are quantified based on a statistical analysis using the pixels in the combined areas of layover, shadow, and building footprint of each building. The results of the statistical analysis show that the spaceborne dual-polarimetric ALOS-2/PALSAR-2 images have good potential to distinguish between slightly damaged buildings and collapsed and tilted buildings. However, it is still difficult to distinguish between collapsed and tilted buildings. In addition, the results of the statistical analysis show that the mean value and variance value of the Gray-Level Co-Occurrence Matrix of the span image are two suitable features by which the categories of building damage can be distinguished. The polarimetric and color features demonstrated poorer performance in terms of distinguishing between damage categories than the texture features.

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