A tutorial on synthetic aperture radar

Moreira, Alberto; Prats-Iraola, Pau; Younis, Marwan; Krieger, Gerhard; Hajnsek, Irena; Papathanassiou, Kostas

doi:10.1109/mgrs.2013.2248301

Cited by 1,878 publications

(1,047 citation statements)

References 162 publications

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“…Synthetic Aperture Radar (SAR) provides two-dimensional images independent from weather, daylight and cloud coverage conditions and has various applications, such as mapping, urban planning, disaster prevention [1], etc. Among these applications, terrain classification is one of the extremely active research interests.…”

Section: Sar Images Classificationmentioning

confidence: 99%

Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field

Fan

Feng

et al. 2017

Remote Sensing

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This paper presents a hierarchical classification approach for Synthetic Aperture Radar (SAR) images. The Conditional Random Field (CRF) and Bayesian Network (BN) are employed to incorporate prior knowledge into this approach for facilitating SAR image classification.(1) A multilayer region pyramid is constructed based on multiscale oversegmentation, and then, CRF is used to model the spatial relationships among those extracted regions within each layer of the region pyramid; the boundary prior knowledge is exploited and integrated into the CRF model as a strengthened constraint to improve classification performance near the boundaries. (2) Multilayer BN is applied to establish the causal connections between adjacent layers of the constructed region pyramid, where the classification probabilities of those sub-regions in the lower layer, conditioned on their parents' regions in the upper layer, are used as adjacent links. More contextual information is taken into account in this framework, which is a benefit to the performance improvement. Several experiments are conducted on real ESAR and TerraSAR data, and the results show that the proposed method achieves better classification accuracy.

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Section: Sar Images Classificationmentioning

confidence: 99%

Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field

Fan

Feng

et al. 2017

Remote Sensing

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“…InSAR technique using coherence information for various applications is well defined in [1] and [9]. With this technique, [12] introduces the method that compares the coherence between acquired images from slightly different positions and time.…”

Section: B Coastline Extraction Using Sar Imagesmentioning

confidence: 99%

“…SAR interferometry (InSAR) is a well-established remote sensing technique that enables the highly accurate measurement of important geophysical parameters such as surface topography, ground deformation and subsidence as well as glacier movements [1]. This technique used that radar images contain the phase information in its own pixel.…”

Section: B Sar Data Acquisitionmentioning

confidence: 99%

Semi-automatic coastline extraction method using Synthetic Aperture Radar images

Yang

Lee

Kim

et al. 2014

16th International Conference on Advanced Communication Technology

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Abstract-Coastline is the boundary that discriminates the land and sea area. Originally, to utilize the coastline information, optical images from air-borne or space-borne systems are used. Due to manual interpretation from optical images, conventional coastline extraction method has several error. This paper proposes semi-automatic coastline extraction method using Synthetic Aperture Radar (SAR) images to reduce these errors. SAR is a platform on-board active sensor which can observe Earth day and night regardless of the weather condition. SAR provides high-resolution images for variety of applications in remote sensing field such as climate change research, surveillance imaging, geoscience, and etc. Using the phase information in SAR image data, we detect and measure the coherence between land and sea area, finally get the coastline information.

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“…Compared with optical and infrared sensors, SAR is not affected by lighting, climate and other conditions, the imaging patterns are rich, SAR can satisfy the different requirements of ships detection [1]. Target monitoring based on SAR has developed into a common detection technology in remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

A New Method Based on Two-Stage Detection Mechanism for Detecting Ships in High-Resolution SAR Images

Xiong

et al. 2017

MATEC Web Conf.

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Abstract. Ship detection in synthetic aperture radar (SAR) remote sensing images, being a fundamental but challenging problem in the field of satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. Aiming at the requirements of ship detection in high-resolution SAR images, the accuracy, the intelligent level, a better real-time operation and processing efficiency, The characteristics of ocean background and ship target in high-resolution SAR images were analyzed, we put forward a ship detection algorithm in high-resolution SAR images. The algorithm consists of two detection stages: The first step designs a pre-training classifier based on improved spectral residual visual model to obtain the visual salient regions containing ship targets quickly, then achieve the purpose of probably detection of ships. In the second stage, considering the Bayesian theory of binary hypothesis detection, a local maximum posterior probability (MAP) classifier is designed for the classification of pixels. After the parameter estimation and judgment criterion, the classification of pixels are carried out in the target areas to achieve the classification of two types of pixels in the salient regions. In the paper, several types of satellite image data, such as TerraSAR-X (TS-X), Radarsat-2, are used to evaluate the performance of detection methods. Comparing with classical CFAR detection algorithms, experimental results show that the algorithm can achieve a better effect of suppressing false alarms, which caused by the speckle noise and ocean clutter background inhomogeneity. At the same time, the detection speed is increased by 25% to 45%.

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A tutorial on synthetic aperture radar

Cited by 1,878 publications

References 162 publications

Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field

Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field

Semi-automatic coastline extraction method using Synthetic Aperture Radar images

A New Method Based on Two-Stage Detection Mechanism for Detecting Ships in High-Resolution SAR Images

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