Ten Years of TerraSAR-X Operations

Buckreuss, Stefan; Schättler, Birgit; Fritz, Thomas; Mittermayer, Josef; Kahle, Ralph; Maurer, Edith; Böer, Johannes; Bachmann, Markus; Mrowka, Falk; Schwarz, Egbert; Breit, Helko; Steinbrecher, Ulrich

doi:10.3390/rs10060873

Cited by 25 publications

(24 citation statements)

References 25 publications

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“…2019, 11, 2733 4 of 17 backscattering coefficient (σ 0 ), and coefficient of variation (CV) are merged to reduce the linear structures of non-roads in the LFR map. In this work, the σ 0 map is obtained as P = {σ i |σ i = 10 × log 10 [argmax µ C { f (i|V(θ, w, l))}]} (1) where f (i|V(θ, w, l)) is the response of multiplicative Duda operation with the window V(θ, w, l) at site i (See Figure 2), µ C is the mean intensity value of region C in the window V which has the maximum response. Since roads have low surface roughness and low permittivity [22], the value σ 0 of roads is very low and often comparable to the SAR sensor noise level.…”

Section: Segmentation Of Road Candidatesmentioning

confidence: 99%

“…Finally, the values of non-road pixels in the LFR map are reset to zero before morphological profiles with path operators are applied, which analyze object length and shape features to determine road candidates. (1) where ( | ( , , )) f i V w l θ is the response of multiplicative Duda operation with the window θ ( , , ) V w l at site i (See Figure 2), μ C is the mean intensity value of region C in the window V which has the maximum response. Since roads have low surface roughness and low permittivity [22], the value σ 0 of roads is very low and often comparable to the SAR sensor noise level.…”

Section: Segmentation Of Road Candidatesmentioning

confidence: 99%

“…With the development of SAR technology, SAR sensors have been able to provide daily high-quality images with different modes. Taking TerraSAR-X as an example, the resolution can be of the order of 20 cm in staring spotlight mode [1]. Nevertheless, complete road detection from SAR images is still a challenging task because of the unique imaging mechanism of SAR, the diversity of road types, and the variable complex situations surrounding the road.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Road Network Extraction from High-Resolution SAR Imagery Using Direction Grouping and Curve Fitting

2019

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Roads are an important recognition target in synthetic aperture radar (SAR) image interpretation. Although a considerable number of high-quality SAR images are now available, the method of road extraction is lagging. To extract the road network with low missed and false rates, this paper proposed a road network extraction approach which includes line detection, road segmentation, road network extraction and optimization. First, the linear feature response and direction map are obtained from the SAR intensity image using the multiplicative Duda operation. Then, the backscattering coefficient and coefficient of variation are combined using a support vector machine to eliminate the linear structures of non-roads, and the binary image of road candidates is subsequently achieved by morphological profiles of path openings. Next, with the obtained direction map, a novel thinning method based on binary image decomposition and curve fitting is presented to obtain line segments of the road network. Finally, a series of measures which involve overlap, continuity, and junction optimization are proposed to construct the road network. In the experiments, the proposed method was applied to Radarsat-2 and TerraSAR-X high-resolution images. The experimental results showed that the proposed method had an excellent performance in terms of both completeness and correctness. one for local linear feature detection aiming to achieve road candidates, and one for global optimization processing, which generates regular road lines and connects the gaps to form a road network. With regard to local line detection, many edge detectors based on SAR image characteristics are presented, such as ratio of averages (ROA) operator [3], ratio of exponentially weighted averages (ROEWA) operator [4], multiplicative Duda operator [5], D1D2 operators [6,7], and so on. Recently, the method of deep fully convolutional neural networks was also introduced to detect road candidates [8]. As for the global optimization process, a commonly used framework is Markov random fields (MRFs) [2,6,9,10] which construct a graph model on road segments. In [11], a Bayesian framework which constructed a conditional random field (CRF) model was utilized to achieve road network optimization. Besides, the work by Lu et al. [12] proposed a method based on region-growing to extract the road network which extended road region step-by-step with automatically selected seeds. Similarly, Cheng et al. [13] presented an approach using a parallel particle filter to track road centerlines. Other methods are based on the genetic algorithm [14] and the snakes model [15] can also be found in the literature.In this article, we develop a road network extraction approach also based on the aforementioned two steps. On the subject of local line detection, the method of multiplicative Duda operators [5] is applied, which utilizes a group of sliding windows with different widths and directions to obtain linear feature responses. Compared with other line detectors, the multiplicative Duda operator ...

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Section: Segmentation Of Road Candidatesmentioning

confidence: 99%

Section: Segmentation Of Road Candidatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Method for Road Network Extraction from High-Resolution SAR Imagery Using Direction Grouping and Curve Fitting

2019

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“…As it turned out, not only the space segment but also the ground segment performance such as reliability and fast near real-time services contributed to the success of TerraSAR-X in maritime applications. Three papers [11][12][13] cover such operational aspects and the scientific and operational use of TerraSAR-X data.…”

Section: Contents Of This Special Issuementioning

confidence: 99%

Ten Years of TerraSAR-X—Scientific Results

2019

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“…The image width ranges from 4.6 km (Staring Spotlight) to 266 km (Wide ScanSAR). There are over 1000 possible product variations, which result from the combination of different imaging modes, polarizations and elevation angles [4].…”

Section: Introductionmentioning

confidence: 99%

Radiometric Performance of the TerraSAR-X Mission over More Than Ten Years of Operation

et al. 2018

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The TerraSAR-X mission, based on two satellites, has produced SAR data products of high quality for a number of scientific and commercial applications for more than ten years. To guarantee the stability and the reliability of these highly accurate SAR data products, both systems were first accurately calibrated during their respective commissioning phases and have been permanently monitored since then. Based on a short description of the methods applied, this paper focuses on the radiometric performance including the gain and phase properties of the transmit/receiver modules, the antenna pattern checked by evaluating scenes acquired over uniformly distributed targets and the radiometric stability derived from permanently deployed point targets. The outcome demonstrates the remarkable performance of both systems since their respective launch.

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Ten Years of TerraSAR-X Operations

Cited by 25 publications

References 25 publications

A Method for Road Network Extraction from High-Resolution SAR Imagery Using Direction Grouping and Curve Fitting

A Method for Road Network Extraction from High-Resolution SAR Imagery Using Direction Grouping and Curve Fitting

Ten Years of TerraSAR-X—Scientific Results

Radiometric Performance of the TerraSAR-X Mission over More Than Ten Years of Operation

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