scholarly journals Synthetic Aperture Radar Processing Approach for Simultaneous Target Detection and Image Formation

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3377 ◽  
Author(s):  
Jifang Pei ◽  
Yulin Huang ◽  
Weibo Huo ◽  
Yuxuan Miao ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Target Detection ◽  
Time Domain ◽  
Image Formation ◽  
Visual Saliency ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Saliency Maps ◽  
Sar Imaging ◽  
Sar Imagery

Finding out interested targets from synthetic aperture radar (SAR) imagery is an attractive but challenging problem in SAR application. Traditional target detection is independent on SAR imaging process, which is purposeless and unnecessary. Hence, a new SAR processing approach for simultaneous target detection and image formation is proposed in this paper. This approach is based on SAR imagery formation in time domain and human visual saliency detection. First, a series of sub-aperture SAR images with resolutions from low to high are generated by the time domain SAR imaging method. Then, those multiresolution SAR images are detected by the visual saliency processing, and the corresponding intermediate saliency maps are obtained. The saliency maps are accumulated until the result with a sufficient confidence level. After some screening operations, the target regions on the imaging scene are located, and only these regions are focused with full aperture integration. Finally, we can get the SAR imagery with high-resolution detected target regions but low-resolution clutter background. Experimental results have shown the superiority of the proposed approach for simultaneous target detection and image formation.

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

scholarly journals An Empirical Algorithm to Retrieve Significant Wave Height from Sentinel-1 Synthetic Aperture Radar Imagery Collected under Cyclonic Conditions

2018 ◽  
Vol 10 (9) ◽  
pp. 1367 ◽  
Author(s):  
Weizeng Shao ◽  
Yuyi Hu ◽  
Jingsong Yang ◽  
Ferdinando Nunziata ◽  
Jian Sun ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Wave Height ◽  
Significant Wave Height ◽  
Synthetic Aperture ◽  
Training Dataset ◽  
Sar Images ◽  
Data Set ◽  
Significant Wave ◽  
Sar Imagery ◽  
Aperture Radar

In this study, an empirical algorithm is proposed to retrieve significant wave height (SWH) from dual-polarization Sentinel-1 (S-1) synthetic aperture radar (SAR) imagery collected under cyclonic conditions. The retrieval scheme is based on the well-known CWAVE empirical function that is here updated to deal with multi-polarization S-1 SAR measurements collected using the interferometric wide (IW) and the Extra Wide-Swath (EW) imaging modes, under cyclonic conditions. First, a training dataset that consists of six S-1 SAR images collected under cyclonic conditions is exploited to both tune the retrieval function and to check the soundness of the retrievals against the co-located WAVEWATCH-III (WW3) numerical simulations. The comparison of simulation from the WW3 model and measurements from altimeter Jason-2 shows a 0.29m root mean square error (RMSE) of significant wave height (SWH). Then, a testing data-set that consists of two S-1 SAR images is exploited to provide a preliminary validation. The results, verified against both WW3 and European Centre for Medium-Range Weather Forecasts (ECMWF) data, show the soundness of the herein approach.

scholarly journals A New Synthetic Aperture Radar (SAR) Imaging Method Combining Match Filter Imaging and Image Edge Enhancement

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4133 ◽  
Author(s):  
Bing Sun ◽  
Chuying Fang ◽  
Hailun Xu ◽  
Anqi Gao
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Imaging Method ◽  
Sar Image ◽  
Sar Images ◽  
Gradient Operator ◽  
Sar Imaging ◽  
Aperture Radar

In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operator to enhance the edges of SAR images to overcome image segmentation problems. For complex-valued signals, the gradient operator was applied directly to the imaging process. The experimental results of simulated images and real images validate our proposed method. For the simulated scene, the supervised image segmentation evaluation indexes of our method have more than 1.18%, 11.2% and 11.72% improvement on probabilistic Rand index (PRI), variability index (VI), and global consistency error (GCE). The proposed imaging method will make SAR image segmentation and related applications easier.

scholarly journals A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery

2019 ◽  
Vol 11 (6) ◽  
pp. 620 ◽  
Author(s):  
Jun Wang ◽  
Tong Zheng ◽  
Peng Lei ◽  
Xiao Bai
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Detection Method ◽  
Regions Of Interest ◽  
The Other ◽  
Synthetic Aperture ◽  
Constant False Alarm Rate ◽  
Sar Imaging ◽  
Detection Stage ◽  
Sar Imagery

The ghost phenomenon in synthetic aperture radar (SAR) imaging is primarily caused by azimuth or range ambiguities, which cause difficulties in SAR target detection application. To mitigate this influence, we propose a ship target detection method in spaceborne SAR imagery, using a hierarchical convolutional neural network (H-CNN). Based on the nature of ghost replicas and typical target classes, a two-stage CNN model is built to detect ship targets against sea clutter and the ghost. First, regions of interest (ROIs) were extracted from a large imaged scene during the coarse-detection stage. Unwanted ghost replicas represented major residual interference sources in ROIs, therefore, the other CNN process was executed during the fine-detection stage. Finally, comparative experiments and analyses, using Sentinel-1 SAR data and various assessment criteria, were conducted to validate H-CNN. Our results showed that the proposed method can outperform the conventional constant false-alarm rate technique and CNN-based models.

scholarly journals The Dependence of Sea SAR Image Distribution Parameters on Surface Wave Characteristics

2018 ◽  
Vol 10 (11) ◽  
pp. 1843 ◽  
Author(s):  
Jian Sun ◽  
Xin Wang ◽  
Xinzhe Yuan ◽  
Qingjun Zhang ◽  
Changlong Guan ◽  
...  
Keyword(s):  
Surface Wave ◽  
Synthetic Aperture Radar ◽  
Target Detection ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Statistical Distributions ◽  
Sea State ◽  
Sar Images ◽  
Distribution Parameters ◽  
Aperture Radar

Modeling the statistical distribution of synthetic aperture radar (SAR) images is essential for sea target detection, which is an important aspect of marine SAR applications. The main goal of this study is to determine the effects of sea states and surface wave texture characteristics on the statistical distributions of sea SAR images. A statistical analysis of the Envisat Advanced Synthetic Aperture Radar (ASAR) wave mode images (imagettes), covering a variety of sea states and wave conditions, was carried out to investigate the suitability of the statistical distributions often used in the literature for sea states parameters. The results revealed the variation in the distribution parameters in terms of their azimuthal cutoff wavelength (ACW) and the peak-to-background ratio (PBR) of the SAR image intensity spectra. The shape parameters of Gamma and Weibull distribution are sensitive and monotonously decreasing with respect to PBR, while the scale parameter is sensitive to ACW. The K distribution was shown to perform well, with both high and stable accuracy. The results of this paper provide a parameterized scheme for sea state classifications and can potentially be used for choosing the most suitable distribution model according to sea state when performing sea target detection.

scholarly journals Improved Relative Range Equation in Squint SAR for Moving Targets Refocusing

CONVERTER ◽  
2021 ◽  
pp. 212-220
Author(s):  
Pu Cheng, ZhentaoYu, Jie Chen
Keyword(s):  
Synthetic Aperture Radar ◽  
Target Detection ◽  
Relative Motion ◽  
Relative Velocity ◽  
Synthetic Aperture ◽  
Moving Target ◽  
Moving Targets ◽  
Sar Imaging ◽  
Motion Parameters ◽  
Range Equation

Moving target detection is difficult for synthetic aperture radar (SAR). As SAR is designed for imaging of stationary ground scene, the moving targets would be blurred and displaced in conventional SAR imaging. To increase the signal clutter ratio, the moving targets should be refocused while detecting. Based on relative range equation, one can refocus and detect the moving targets simultaneously by searching the relative velocity. This method has been derived and applied for side looking SAR. In this paper, we extend the relative range equation to squint mode. The procedures of the refocusing method are also illustrated. By introducing a parameter of relative squint angle, the imaging position of the moving target is derived. The refocusing method is validated by simulations. The moving target can be optimally refocused, and the refocused position can be parametrized by the relative motion parameters.

scholarly journals Synthetic aperture radar detection of the snowline on Commonwealth and Howard Glaciers, Taylor Valley, Antarctica

2002 ◽  
Vol 34 ◽  
pp. 177-183 ◽  
Author(s):  
Patrick Bardel ◽  
Andrew G. Fountain ◽  
Dorothy K. Hall ◽  
Ron Kwok
Keyword(s):  
Synthetic Aperture Radar ◽  
Optical Depth ◽  
Synthetic Aperture ◽  
Polar Regions ◽  
Sar Images ◽  
Reflection And Refraction ◽  
Taylor Valley ◽  
Sar Imagery ◽  
Radar Wave ◽  
Aperture Radar

AbstractSynthetic aperture radar (SAR) images of Taylor Valley, Antarctica, were acquired in January 1999 in coordination with ground-based measurements to assess SAR detection of the snowline on dry polar glaciers. We expected significant penetration of the radar wave resulting in an offset of the SAR-detected snowline relative to the true snowline. Results indicated no detectable displacement of the SAR snowline. Snow depths of 15 cm over ice can be detected on the imagery. We hypothesize that the optical depth of thin snowpacks is enhanced by reflection and refraction of the radar beam by internal snow layers. The enhanced optical depth increases the volume scattering, and thereby enhances backscatter sufficiently to be detected by the SAR. Consequently, SAR imagery may be used directly to image the position of transient snowlines in dry polar regions.

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