scholarly journals Automatic Detection of Small Icebergs in Fast Ice Using Satellite Wide-Swath SAR Images

2019 ◽  
Vol 11 (7) ◽  
pp. 806 ◽  
Author(s):  
Ingri Soldal ◽  
Wolfgang Dierking ◽  
Anton Korosov ◽  
Armando Marino
Keyword(s):  
Spatial Resolution ◽  
Barents Sea ◽  
Computation Time ◽  
Automatic Detection ◽  
False Alarms ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Detection Scheme ◽  
Fast Ice ◽  
Wide Swath

Automatic detection of icebergs in satellite images is regarded a useful tool to provide information necessary for safety in Arctic shipping or operations over large ocean areas in near-real time. In this work, we investigated the feasibility of automatic iceberg detection in Sentinel-1 Extra Wide Swath (EWS) SAR images which follow the preferred image mode in operational ice charting. As test region, we selected the Barents Sea where the size of many icebergs is on the order of the spatial resolution of the EWS-mode. We tested a new approach for a detection scheme. It is based on a combination of a filter for enhancing the contrast between icebergs and background, subsequent blob detection, and final application of a Constant False Alarm Rate (CFAR) algorithm. The filter relies mainly on the HV-polarized intensity which often reveals a larger difference between icebergs and sea ice or open water. The blob detector identifies locations of potential icebergs and thus shortens computation time. The final detection is performed on the identified blobs using the CFAR algorithm. About 2000 icebergs captured in fast ice were visually identified in Sentinel-2 Multi Spectral Imager (MSI) data and exploited for an assessment of the detection scheme performance using confusion matrices. For our performance tests, we used four Sentinel-1 EWS images. For judging the effect of spatial resolution, we carried out an additional test with one Sentinel-1 Interferometric Wide Swath (IWS) mode image. Our results show that only 8–22 percent of the icebergs could be detected in the EWS images, and over 90 percent of all detections were false alarms. In IWS mode, the number of correctly identified icebergs increased to 38 percent. However, we obtained a larger number of false alarms in the IWS image than in the corresponding EWS image. We identified two problems for iceberg detection: 1) with the given frequency–polarization combination, not all icebergs are strong scatterers at HV-polarization, and (2) icebergs and deformation structures present on fast ice can often not be distinguished since both may reveal equally strong responses at HV-polarization.

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.

An Improved Algorithm for Ship Detection from the High-Resolution SAR Images

2014 ◽  
Vol 1044-1045 ◽  
pp. 1040-1044
Author(s):  
Qing Ping Wang ◽  
Hong Zhu ◽  
Wei Wei Wu ◽  
Chang Zhu ◽  
Nai Chang Yuan
Keyword(s):  
High Resolution ◽  
Region Of Interest ◽  
Speckle Noise ◽  
False Alarms ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Ship Detection ◽  
Whole Process ◽  
Preliminary Extraction ◽  
Improved Algorithm

An improved algorithm for ship detection from the high-resolution synthetic aperture radar (SAR) images is proposed in this paper. In this algorithm, we firstly utilize the image pre-processing step to suppress the speckle noise. Then, the ship ROIs (Region of Interest) are obtained based on MSER (Maximally Stable Extremal Region) method, which enables preliminary extraction of ship candidates. Finally, an improved CFAR (Constant False Alarm Rate) detector is designed for accurate detection with the purpose of accelerating the whole process and decreasing false alarms. The experimental results show that this method can achieve effective ship detection in high-resolution SAR images. The process of ship detection is also accelerated which is in favour of the project realization.

scholarly journals AN ADAPTIVE SHIP DETECTION ALGORITHM FOR HRWS SAR IMAGES UNDER COMPLEX BACKGROUND: APPLICATION TO SENTINEL1A DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 497-503
Author(s):  
G. He ◽  
Z. Xia ◽  
H. Chen ◽  
K. Li ◽  
Z. Zhao ◽  
...  
Keyword(s):  
High Resolution ◽  
False Alarm ◽  
False Alarm Rate ◽  
Texture Features ◽  
Detection Algorithm ◽  
Support Vector ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Ship Detection ◽  
Wide Swath

Real-time ship detection using synthetic aperture radar (SAR) plays a vital role in disaster emergency and marine security. Especially the high resolution and wide swath (HRWS) SAR images, provides the advantages of high resolution and wide swath synchronously, significantly promotes the wide area ocean surveillance performance. In this study, a novel method is developed for ship target detection by using the HRWS SAR images. Firstly, an adaptive sliding window is developed to propose the suspected ship target areas, based upon the analysis of SAR backscattering intensity images. Then, backscattering intensity and texture features extracted from the training samples of manually selected ship and non-ship slice images, are used to train a support vector machine (SVM) to classify the proposed ship slice images. The approach is verified by using the Sentinl1A data working in interferometric wide swath mode. The results demonstrate the improvement performance of the proposed method over the constant false alarm rate (CFAR) method, where the classification accuracy improved from 88.5 % to 96.4 % and the false alarm rate mitigated from 11.5 % to 3.6 % compared with CFAR respectively.

scholarly journals Towards a 20 m Global Building Map from Sentinel-1 SAR Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1833 ◽  
Author(s):  
Marco Chini ◽  
Ramona Pelich ◽  
Renaud Hostache ◽  
Patrick Matgen ◽  
Carlos Lopez-Martinez
Keyword(s):  
Arid Regions ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Satellite Mission ◽  
Sar Images ◽  
Multi Temporal ◽  
Interferometric Sar ◽  
Very High ◽  
Urban Footprint ◽  
Wide Swath

This study introduces a technique for automatically mapping built-up areas using synthetic aperture radar (SAR) backscattering intensity and interferometric multi-temporal coherence generated from Sentinel-1 data in the framework of the Copernicus program. The underlying hypothesis is that, in SAR images, built-up areas exhibit very high backscattering values that are coherent in time. Several particular characteristics of the Sentinel-1 satellite mission are put to good use, such as its high revisit time, the availability of dual-polarized data, and its small orbital tube. The newly developed algorithm is based on an adaptive parametric thresholding that first identifies pixels with high backscattering values in both VV and VH polarimetric channels. The interferometric SAR coherence is then used to reduce false alarms. These are caused by land cover classes (other than buildings) that are characterized by high backscattering values that are not coherent in time (e.g., certain types of vegetated areas). The algorithm was tested on Sentinel-1 Interferometric Wide Swath data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa. The resulting building maps were compared with the Global Urban Footprint (GUF) derived from the TerraSAR-X mission data and, on average, a 92% agreement was obtained.

scholarly journals Extended GLRT Detection of Moving Targets for Multichannel SAR Based on Generalized Steering Vector

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1478
Author(s):  
Chong Song ◽  
Bingnan Wang ◽  
Maosheng Xiang ◽  
Wei Li
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Moving Target ◽  
Ratio Test ◽  
Constant False Alarm Rate ◽  
Moving Targets ◽  
Sar Images ◽  
Steering Vector ◽  
Current Cell ◽  
Data Vector

A generalized likelihood ratio test (GLRT) with the constant false alarm rate (CFAR) property was recently developed for adaptive detection of moving targets in focusing synthetic aperture radar (SAR) images. However, in the multichannel SAR-ground moving-target indication (SAR-GMTI) system, image defocus is inevitable, which will remarkably degrade the performance of the GLRT detector, especially for the lower radar cross-section (RCS) and slower radial velocity moving targets. To address this issue, based on the generalized steering vector (GSV), an extended GLRT detector is proposed and its performance is evaluated by the optimum likelihood ratio test (LRT) in the Neyman-Pearson (NP) criterion. The joint data vector formulated by the current cell and its adjacent cells is used to obtain the GSV, and then the extended GLRT is derived, which coherently integrates signal and accomplishes moving-target detection and parameter estimation. Theoretical analysis and simulated SAR data demonstrate the effectiveness and robustness of the proposed detector in the defocusing SAR images.

scholarly journals A Novel Orthogonal Waveform Separation Scheme for Airborne MIMO-SAR Systems

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3580 ◽  
Author(s):  
Jie Wang ◽  
Ke-Hong Zhu ◽  
Li-Na Wang ◽  
Xing-Dong Liang ◽  
Long-Yong Chen
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Synthetic Aperture Radar ◽  
3D Imaging ◽  
Synthetic Aperture ◽  
Separation Scheme ◽  
Sar Images ◽  
Wide Swath ◽  
Aperture Radar

In recent years, multi-input multi-output (MIMO) synthetic aperture radar (SAR) systems, which can promote the performance of 3D imaging, high-resolution wide-swath remote sensing, and multi-baseline interferometry, have received considerable attention. Several papers on MIMO-SAR have been published, but the research of such systems is seriously limited. This is mainly because the superposed echoes of the multiple transmitted orthogonal waveforms cannot be separated perfectly. The imperfect separation will introduce ambiguous energy and degrade SAR images dramatically. In this paper, a novel orthogonal waveform separation scheme based on echo-compression is proposed for airborne MIMO-SAR systems. Specifically, apart from the simultaneous transmissions, the transmitters are required to radiate several times alone in a synthetic aperture to sense their private inner-aperture channels. Since the channel responses at the neighboring azimuth positions are relevant, the energy of the solely radiated orthogonal waveforms in the superposed echoes will be concentrated. To this end, the echoes of the multiple transmitted orthogonal waveforms can be separated by cancelling the peaks. In addition, the cleaned echoes, along with original superposed one, can be used to reconstruct the unambiguous echoes. The proposed scheme is validated by simulations.

Optimal Sensor Placement for Damage Detection in Complex Structures

2009 ◽  
Author(s):  
Sunilkumar Soni ◽  
Santanu Das ◽  
Aditi Chattopadhyay
Keyword(s):  
False Alarm ◽  
Damage Detection ◽  
False Alarm Rate ◽  
Structural Component ◽  
Detection Rate ◽  
Sensor Placement ◽  
False Alarms ◽  
Optimal Sensor Placement ◽  
Detection Scheme ◽  
Sensing Radius

An optimal sensor placement methodology is proposed based on detection theory framework to maximize the detection rate and minimize the false alarm rate. Minimizing the false alarm rate for a given detection rate plays an important role in improving the efficiency of a Structural Health Monitoring (SHM) system as it reduces the number of false alarms. The placement technique is such that the sensor features are as directly correlated and as sensitive to damage as possible. The technique accounts for a number of factors, like actuation frequency and strength, minimum damage size, damage detection scheme, material damping, signal to noise ratio (SNR) and sensing radius. These factors are not independent and affect each other. Optimal sensor placement is done in two steps. First, a sensing radius, which can capture any detectable change caused by a perturbation and above a certain threshold, is calculated. This threshold value is based on Neyman-Pearson detector that maximizes the detection rate for a fixed false alarm rate. To avoid sensor redundancy, a criterion to minimize sensing region overlaps of neighboring sensors is defined. Based on the sensing region and the minimum overlap concept, number of sensors needed on a structural component is calculated. In the second step, a damage distribution pattern, known as probability of failure distribute, is calculated for a structural component using finite element analysis. This failure distribution helps in selecting the most sensitive sensors, thereby removing those making remote contributions to the overall detection scheme.

Sign in / Sign up

Export Citation Format

Share Document