SAR-PC: Edge Detection in SAR Images via an Advanced Phase Congruency Model

Xiang, Yuming; Wang, Feng; Wan, Li‐Jun; You, Hongjian

doi:10.3390/rs9030209

Cited by 40 publications

(29 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, in these applications, the phase congruency model is directly applied to the raw SAR image or the logarithm of the SAR image, which also suffers from speckle noise [34]. Inspired by the ratio-based edge detectors that provide the property of constant false alarm rate, we introduce the ratio-based method [35] and the monogenic signal [36] into the phase congruency to solve the aforementioned problem while maintaining a small computational cost.…”

Section: Methodsmentioning

confidence: 99%

An Automatic and Novel SAR Image Registration Algorithm: A Case Study of the Chinese GF-3 Satellite

Xiang

Wang

You

2018

Sensors

Self Cite

View full text Add to dashboard Cite

The Chinese GF-3 satellite launched in August 2016 is a Synthetic Aperture Radar (SAR) satellite that has the largest number of imaging modes in the world. It achieves a free switch in the spotlight, stripmap, scanSAR, wave, global observation and other imaging modes. In order to further utilize GF-3 SAR images, an automatic and fast image registration procedure needs to be done. In this paper, we propose a novel image registration technique for GF-3 images of different imaging modes. The proposed algorithm consists of two stages: coarse registration and fine registration. In the first stage, we combine an adaptive sampling method with the SAR-SIFT algorithm to efficiently eliminate obvious translation, rotation and scale differences between the reference and sensed images. In the second stage, uniformly-distributed control points are extracted, then the fast normalized cross-correlation of an improved phase congruency model is utilized as a new similarity metric to match the reference image and the coarse-registered image in a local search region. Moreover, a selection strategy is used to remove outliers. Experimental results on several GF-3 SAR images of different imaging modes show that the proposed algorithm gives a robust, efficient and precise registration performance, compared with other state-of-the-art algorithms for SAR image registration.

show abstract

Section: Methodsmentioning

confidence: 99%

An Automatic and Novel SAR Image Registration Algorithm: A Case Study of the Chinese GF-3 Satellite

Xiang

Wang

You

2018

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a consequence, no information of the original SAR image is lost, and the resolution is not degraded (Wegner et al, 2010). In the detection process, linear features are first extracted by combining the SAR phase congruency detector (Xiang et al, 2017) and the hysteresis thresholding method. However, the threshols are strongly affected by the template size and image scene, which needs to be manually set, while it is difficult to define a proper threshold to distinguish good linear features from bad ones.…”

Section: Methodsmentioning

confidence: 99%

Automatic Coregistration for Multiview Sar Images in Urban Areas

Xiang

Kang

Wang

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Due to the high resolution property and the side-looking mechanism of SAR sensors, complex buildings structures make the registration of SAR images in urban areas becomes very hard. In order to solve the problem, an automatic and robust coregistration approach for multiview high resolution SAR images is proposed in the paper, which consists of three main modules. First, both the reference image and the sensed image are segmented into two parts, urban areas and nonurban areas. Urban areas caused by double or multiple scattering in a SAR image have a tendency to show higher local mean and local variance values compared with general homogeneous regions due to the complex structural information. Based on this criterion, building areas are extracted. After obtaining the target regions, L-shape structures are detected using the SAR phase congruency model and Hough transform. The double bounce scatterings formed by wall and ground are shown as strong L- or T-shapes, which are usually taken as the most reliable indicator for building detection. According to the assumption that buildings are rectangular and flat models, planimetric buildings are delineated using the L-shapes, then the reconstructed target areas are obtained. For the orignal areas and the reconstructed target areas, the SAR-SIFT matching algorithm is implemented. Finally, correct corresponding points are extracted by the fast sample consensus (FSC) and the transformation model is also derived. The experimental results on a pair of multiview TerraSAR images with 1-m resolution show that the proposed approach gives a robust and precise registration performance, compared with the orignal SAR-SIFT method.

show abstract

“…Synthetic aperture radar (SAR) imagery is widely utilized in remote sensing because of its features of all-day, all weather and high-resolution [1]. Additionally, digital elevation model (DEM) generation using high-resolution SAR images has become an important application.…”

Section: Introductionmentioning

confidence: 99%

DEM Generation With a Scale Factor Using Multi-Aspect SAR Imagery Applying Radargrammetry

et al. 2020

View full text Add to dashboard Cite

Digital elevation model (DEM) generation using multi-aspect synthetic aperture radar (SAR) imagery applying radargrammetry has become a hotspot. The traditional radargrammetric method is to solve the rigorous radar projection equations to obtain the three dimensional coordinates of targets. In this paper, we propose a new DEM generation method based on the offset between multi-aspect images formed on ground plane. The ground object will be projected to different positions from different viewing aspect angles if the height of object is not equal to the height of imaging plane. The linear relationship between the offset of imaging positions and height of the object is derived and scale factor is obtained finally. Height information can be retrieved from offset of imaging positions directly through the DEM extraction model presented in this paper. Thus the solution to nonlinear equations point by point can be avoided. Real C band airborne circular SAR images is used to verify the proposed approach. When extracted DEM applied in multi-aspect imaging process, superimposition of multi-aspect images will no longer be defocusing and can achieve finer observation of the scanned scene.

show abstract

SAR-PC: Edge Detection in SAR Images via an Advanced Phase Congruency Model

Cited by 40 publications

References 28 publications

An Automatic and Novel SAR Image Registration Algorithm: A Case Study of the Chinese GF-3 Satellite

An Automatic and Novel SAR Image Registration Algorithm: A Case Study of the Chinese GF-3 Satellite

Automatic Coregistration for Multiview Sar Images in Urban Areas

DEM Generation With a Scale Factor Using Multi-Aspect SAR Imagery Applying Radargrammetry

Contact Info

Product

Resources

About