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 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 Study of the Effects of Target Geometry on Synthetic Aperture Radar Images using Simulation Studies

2014 ◽  
Vol XL-8 ◽  
pp. 1227-1231
Author(s):  
K. Tummala ◽  
A. K. Jha ◽  
S. Kumar
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Target Area ◽  
Simulation Algorithm ◽  
Sar Image ◽  
Sar Images ◽  
Radar Images ◽  
The Earth ◽  
Simulated Images ◽  
Aperture Radar

Synthetic aperture radar technology has revolutionized earth observation with very high resolutions of below 5m, making it possible to distinguish individual urban features like buildings and even cars on the surface of the earth. But, the difficulty in interpretation of these images has hindered their use. The geometry of target objects and their orientation with respect to the SAR sensor contribute enormously to unexpected signatures on SAR images. Geometry of objects can cause single, double or multiple reflections which, in turn, affect the brightness value on the SAR images. Occlusions, shadow and layover effects are present in the SAR images as a result of orientation of target objects with respect to the incident microwaves. Simulation of SAR images is the best and easiest way to study and understand the anomalies. This paper discusses synthetic aperture radar image simulation, with the study of effect of target geometry as the main aim. Simulation algorithm has been developed in the time domain to provide greater modularity and to increase the ease of implementation. This algorithm takes into account the sensor and target characteristics, their locations with respect to the earth, 3-dimensional model of the target, sensor velocity, and SAR parameters. two methods have been discussed to obtain position and velocity vectors of SAR sensor – the first, from the metadata of real SAR image used to verify the simulation algorithm, and the second, from satellite orbital parameters. Using these inputs, the SAR image coordinates and backscatter coefficients for each point on the target are calculated. The backscatter coefficients at target points are calculated based on the local incidence angles using Muhleman's backscatter model. The present algorithm has been successfully implemented on radarsat-2 image of San Francisco bay area. Digital elevation models (DEMs) of the area under consideration are used as the 3d models of the target area. DEMs of different resolutions have been used to simulate SAR images in order to study how the target models affect the accuracy of simulation algorithm. The simulated images have been compared with radarsat-2 images to observe the efficiency of the simulation algorithm in accurately representing the locations and extents of different objects in the target area. The simulated algorithm implemented in this paper has given satisfactory results as the simulated images accurately show the different features present in the DEM of the target area.

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 Refocusing Moving Ship Targets in SAR Images Based on Fast Minimum Entropy Phase Compensation

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1154 ◽  
Author(s):  
Xiangli Huang ◽  
Kefeng Ji ◽  
Xiangguang Leng ◽  
Ganggang Dong ◽  
Xiangwei Xing
Keyword(s):  
Synthetic Aperture Radar ◽  
Phase Error ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Minimum Entropy ◽  
Sar Images ◽  
Phase Compensation ◽  
Translation Motion ◽  
Airborne Sar ◽  
Aperture Radar

Moving ship targets appear blurred and defocused in synthetic aperture radar (SAR) images due to the translation motion during the coherent processing. Motion compensation is required for refocusing moving ship targets in SAR scenes. A novel refocusing method for moving ship is developed in this paper. The method is exploiting inverse synthetic aperture radar (ISAR) technique to refocus the ship target in SAR image. Generally, most cases of refocusing are for raw echo data, not for SAR image. Taking into account the advantages of processing in SAR image, the processing data are SAR image rather than raw echo data in this paper. The ISAR processing is based on fast minimum entropy phase compensation method, an iterative approach to obtain the phase error. The proposed method has been tested using Spaceborne TerraSAR-X, Gaofeng-3 images and airborne SAR images of maritime targets.

scholarly journals Specific Windows Search for Multi-Ship and Multi-Scale Wake Detection in SAR Images

2021 ◽  
Vol 14 (1) ◽  
pp. 25
Author(s):  
Kaiyang Ding ◽  
Junfeng Yang ◽  
Zhao Wang ◽  
Kai Ni ◽  
Xiaohao Wang ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Statistical Features ◽  
Test Results ◽  
Sar Images ◽  
Multi Scale ◽  
Wake Detection ◽  
Aperture Radar

Traditional ship identification systems have difficulty in identifying illegal or broken ships, but the wakes generated by ships can be used as a major feature for identification. However, multi-ship and multi-scale wake detection is also a big challenge. This paper combines the geometric and pixel characteristics of ships and their wakes in Synthetic Aperture Radar (SAR) images and proposes a method for multi-ship and multi-scale wake detection. This method first detects the highlight pixel area in the image and then generates specific windows around the centroid, thereby detecting wakes of different sizes in different areas. In addition, all wake components can be located completely based on wake clustering, the statistical features of wake axis pixels can be used to determine the visible length of the wake. Test results on the Gaofen-3 SAR image show the special potential of the method for wake detection.

scholarly journals Removing Speckle Noise in Synthetic Aperture Radar Images using Combined Intensity Coherence Vector & Hybrid Filtering

2021 ◽  
Vol 58 (1) ◽  
pp. 4289-4295
Author(s):  
Dr. D. Suresh Et al.
Keyword(s):  
Synthetic Aperture Radar ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Signal To Noise ◽  
Sar Images ◽  
Radar Images ◽  
Hybrid Filtering ◽  
Edge Based ◽  
Aperture Radar

Noise will be unavoidable in image securing practice and denoising is a fundamental advance to recoup the image quality. The image of Synthetic Aperture Radar (SAR) is intrinsically misrepresented in dot noise that happens because of coherent nature of the dispersing wonders. Denoising SAR images target eliminating dot while safeguarding image highlights, for example, surface, edges, and point targets. The blend of nonlocal gathering and changed area filtering has coordinated the cutting edge denoising methods. Notwithstanding, this methodology makes an intense suspicion that image fix itself gives a brilliant guess on the genuine boundary, which prompts predisposition issue transcendently under genuine dot noise. Another impediment is that the for the most part utilized fix pre-determination techniques can't productively avoid the exceptions and harm the edges. The SAR image is infused with spot noise, and afterward edge based marker controlled watershed division is applied to recognize the homogeneous locales in SAR image. For every locale, the local pixels are distinguished by utilizing Intensity Coherence Vector (ICV) and are denoised autonomously by utilizing a half and half filtering, which contains the improved forms of ice, middle and mean channel. The exploratory outcomes show that the proposed strategy outflanks different techniques, for example, fix based filtering, non-nearby methods, wavelets and old style dot channels in wording higher wavelets signal-to-noise and edge conservation proportions relatively.

EMPIRIC AND DYNAMIC DETECTION OF THE SEA BOTTOM TOPOGRAPHY FROM SYNTHETIC APERTURE RADAR IMAGE

2009 ◽  
Vol 01 (02) ◽  
pp. 243-263 ◽  
Author(s):  
YELI YUAN ◽  
MEIBIN JIN ◽  
PINGJIAN SONG ◽  
JIE ZHANG
Keyword(s):  
Synthetic Aperture Radar ◽  
Surface Waves ◽  
Bottom Topography ◽  
Wave Spectrum ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Sar Images ◽  
Sea Bottom ◽  
Dynamic Detection ◽  
Aperture Radar

In this study, we develop empiric and dynamic methods to detect the underlying sea bottom topography from synthetic aperture radar (SAR) images. The ocean bottom features can be seen from SAR images due to the modulation of the surface waves by the nonuniform currents, which, by motion equations, should be a function of depth. We first derive the SAR image equation at a given time from Valenzuela's formula and the expression of micro-scale wave spectrum. By integrating the SAR image equation and the governing equations for ocean currents, we establish the "forward and inverse problems" for dynamic detection of topography. To utilize the SAR images, we separate the current modulation scale from the surface waves by a two-dimensional empirical mode decomposition method based on the Delaunay triangulation with the most protruding principle and the Berstein–Bezier fitting and interpolation with the most optimum principle. Examples of bottom topography detection from SAR images are presented.

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