scholarly journals Integration of Fine Model-Based Decomposition and Guard Filter for Ship Detection in PolSAR Images

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4295
Author(s):  
Dongsheng Liu ◽  
Ling Han
Keyword(s):  
Figure Of Merit ◽  
Spatial Information ◽  
Synthetic Aperture ◽  
Widespread Application ◽  
Polarimetric Synthetic Aperture Radar ◽  
Ship Detection ◽  
Qualitative And Quantitative ◽  
Decomposition Scheme ◽  
Model Based ◽  
Maritime Surveillance

Ship detection with polarimetric synthetic aperture radar (PolSAR) has gained extensive attention due to its widespread application in maritime surveillance. Nevertheless, designing identifiable features to realize accurate ship detection is still challenging. For this purpose, a fine eight-component model-based decomposition scheme is first presented by incorporating four advanced physical scattering models, thus accurately describing the dominant and local structure scattering of ships. Through analyzing the exclusive scattering mechanisms of ships, a discriminative ship detection feature is then constructed from the derived contributions of eight kinds of scattering components. Combined with a spatial information-based guard filter, the efficacy of the feature is further amplified and thus a ship detector is proposed which fulfills the final ship detection. Several qualitative and quantitative experiments are conducted on real PolSAR data and the results demonstrate that the proposed method reaches the highest figure-of-merit (FoM) factor of 0.96, which outperforms the comparative methods in ship detection.

scholarly journals Seven-Component Model-Based Decomposition for PolSAR Data with Sophisticated Scattering Models

2019 ◽  
Vol 11 (23) ◽  
pp. 2802 ◽  
Author(s):  
Hui Fan ◽  
Sinong Quan ◽  
Dahai Dai ◽  
Xuesong Wang ◽  
Shunping Xiao
Keyword(s):  
Urban Areas ◽  
State Of The Art ◽  
Component Model ◽  
Cross Polarization ◽  
Polarimetric Synthetic Aperture Radar ◽  
Decomposition Scheme ◽  
Model Based ◽  
Component Decomposition ◽  
Oriented Dipole ◽  
Quarter Wave

Due to incomprehensive and inaccurate scattering modeling, the state-of-the-art polarimetric synthetic aperture radar (PolSAR) model-based target decompositions are incapable of effectively depicting the scattering mechanism of obliquely oriented urban areas. In this paper, a seven-component model-based decomposition scheme is proposed by constructing several sophisticated scattering models. First, an eigenvalue-based obliquely-oriented dihedral scattering model is presented to reasonably distribute the co-polarization and cross-polarization scattering powers in obliquely oriented urban areas, thus accurately characterizing the urban scattering. Second, the ±45° oriented dipole and ±45° quarter-wave reflector scattering models are incorporated for the purpose of accounting for the real and imaginary components of the T 13 element in the coherency matrix so as to fully utilize polarimetric information. Finally, according to their mathematical forms, several strategies for model parameter solutions are designed, and the seven-component decomposition is fulfilled. Experimental results conducted on different PolSAR data demonstrate that the proposed method considerably improves the PolSAR scattering interpretation in a more physical manner compared to other existing model-based decomposition, which can be applied for urban area detection, classification, and other urban planning applications.

scholarly journals Enhanced Feature Extraction for Ship Detection from Multi-Resolution and Multi-Scene Synthetic Aperture Radar (SAR) Images

2019 ◽  
Vol 11 (22) ◽  
pp. 2694 ◽  
Author(s):  
Fei Gao ◽  
Wei Shi ◽  
Jun Wang ◽  
Erfu Yang ◽  
Huiyu Zhou
Keyword(s):  
Feature Extraction ◽  
Synthetic Aperture Radar ◽  
Spatial Information ◽  
Reduction Process ◽  
Feature Representation ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Ship Detection ◽  
Comparison Results ◽  
Aperture Radar

Independent of daylight and weather conditions, synthetic aperture radar (SAR) images have been widely used for ship monitoring. The traditional methods for SAR ship detection are highly dependent on the statistical models of sea clutter or some predefined thresholds, and generally require a multi-step operation, which results in time-consuming and less robust ship detection. Recently, deep learning algorithms have found wide applications in ship detection from SAR images. However, due to the multi-resolution imaging mode and complex background, it is hard for the network to extract representative SAR target features, which limits the ship detection performance. In order to enhance the feature extraction ability of the network, three improvement techniques have been developed. Firstly, multi-level sparse optimization of SAR image is carried out to handle clutters and sidelobes so as to enhance the discrimination of the features of SAR images. Secondly, we hereby propose a novel split convolution block (SCB) to enhance the feature representation of small targets, which divides the SAR images into smaller sub-images as the input of the network. Finally, a spatial attention block (SAB) is embedded in the feature pyramid network (FPN) to reduce the loss of spatial information, during the dimensionality reduction process. In this paper, experiments on the multi-resolution SAR images of GaoFen-3 and Sentinel-1 under complex backgrounds are carried out and the results verify the effectiveness of SCB and SAB. The comparison results also show that the proposed method is superior to several state-of-the-art object detection algorithms.

scholarly journals Unsupervised Classification Method for Polarimetric Synthetic Aperture Radar Imagery Based on Yamaguchi Four-Component Decomposition Model

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Sheng Sun ◽  
Renfeng Liu ◽  
Wen Wen
Keyword(s):  
Unsupervised Classification ◽  
Component Model ◽  
Synthetic Aperture ◽  
Polarimetric Synthetic Aperture Radar ◽  
Qualitative And Quantitative ◽  
Four Component Model ◽  
Component Decomposition ◽  
Distance Model ◽  
Radar Imagery ◽  
Synthetic Aperture Radar Imagery

For improving the accuracy of unsupervised classification based on scattering models, the four-component Yamaguchi model is introduced, which is an improved version of the best-known three-component Freeman model. Therewith, the four-component model is combined with the Wishart distance model. The new proposed algorithm of clustering is rolled out thereafter and the procedure of this new method is listed. In experiments, seven areas of various homogeneities are singled out from the Flevoland sample image in AIRSAR dataset. Qualitative and quantitative experiments are performed for a comparative study. It can be easily seen that the resolution and details are remarkably upgraded by the new proposed method. The accuracy of classification in homogeneous areas has also increased significantly by adopting the new iterative algorithm.

scholarly journals Ship detection in Madura Strait and Lamong Gulf using Sentinel-1 SAR Data

2018 ◽  
Vol 1 ◽  
pp. 00002
Author(s):  
Filsa Bioresita ◽  
Cherie Bhekti Pribadi ◽  
Hana Sugiastu Firdaus
Keyword(s):  
Rapid Detection ◽  
Extraction Method ◽  
Sea Surface ◽  
Synthetic Aperture ◽  
Ship Detection ◽  
Remote Sensing Technology ◽  
Maritime Surveillance ◽  
Sensing Technology ◽  
Detection And Identification ◽  
Sar Imaging

<p class="Abstract">During the recent years, maritime surveillance has been receiving a growing interest. Ship detection and identification are parts of maritime surveillance in order to dealing with illegal fishery, maritime traffic, sea border activity, or oil spill detection and monitoring. Nowadays, Synthetic Aperture Radar (SAR) as one of active remote sensing technology provide signals to penetrate cloud, can be advantage to be used in tropical region with the intention to monitor sea objects on the sea surface from the space. The availability of Sentinel-1 as SAR imaging mission, providing continuous all-weather, day-and-night imagery, makes it ideal for precise cueing and location of ship activities at sea. Utilization of CFAR (Constant False Alarm Rate) algorithm provided by SNAP (Sentinel Application Platform) software from ESA show rapid detection of ship in the study areas (Madura Strait and Lamong Gulf). Compared with manual ship extraction method, it gives sufficient results.<o:p></o:p></p>

scholarly journals PolSAR Ship Detection with Optimal Polarimetric Rotation Domain Features and SVM

2021 ◽  
Vol 13 (19) ◽  
pp. 3932
Author(s):  
Haoliang Li ◽  
Xingchao Cui ◽  
Siwei Chen
Keyword(s):  
Figure Of Merit ◽  
Detection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Sea Clutter ◽  
Correlation Pattern ◽  
Polarimetric Synthetic Aperture Radar ◽  
Ship Detection ◽  
Rotation Domain ◽  
Core Idea

Polarimetric synthetic aperture radar (PolSAR) can obtain fully polarimetric information, which provides chances to better understand target scattering mechanisms. Ship detection is an important application of PolSAR and a number of scattering mechanism-based ship detection approaches have been established. However, the backscattering of manmade targets including ships is sensitive to the relative geometry between target orientation and radar line of sight, which makes ship detection still challenging. This work aims at mitigating this issue by target scattering diversity mining and utilization in polarimetric rotation domain with the interpretation tools of polarimetric coherence and correlation pattern techniques. The core idea is to find an optimal combination of polarimetric rotation domain features which shows the best potential to discriminate ship target and sea clutter pixel candidates. With the Relief method, six polarimetric rotation domain features derived from the polarimetric coherence and correlation patterns are selected. Then, a novel ship detection method is developed thereafter with these optimal features and the support vector machine (SVM) classifier. The underlying physics is that ship detection is equivalent to ship and sea clutter classification after the ocean and land partition. Four kinds of spaceborne PolSAR datasets from Radarsat-2 and GF-3 are used for comparison experiments. The superiority of the proposed detection methodology is clearly demonstrated. The proposed method achieves the highest figure of merit (FoM) of 99.26% and 100% for two Radarsat-2 datasets, and of 95.45% and 99.96% for two GF-3 datasets. Specially, the proposed method shows better performance to detect inshore dense ships and reserve the ship structure.

scholarly journals A Deep Vector Quantization Clustering Method for Polarimetric SAR Images

2021 ◽  
Vol 13 (11) ◽  
pp. 2127
Author(s):  
Yixin Zuo ◽  
Jiayi Guo ◽  
Yueting Zhang ◽  
Bin Lei ◽  
Yuxin Hu ◽  
...  
Keyword(s):  
Vector Quantization ◽  
Visual Quality ◽  
Synthetic Aperture ◽  
Polarization Data ◽  
Sar Images ◽  
Polarimetric Synthetic Aperture Radar ◽  
Qualitative And Quantitative ◽  
Linear Dependency ◽  
Proposed Model ◽  
Convolutional Autoencoder

Convolutional Neural Network (CNN) models are widely used in supervised Polarimetric Synthetic Aperture Radar (PolSAR) image classification. They are powerful tools to capture the non-linear dependency between adjacent pixels and outperform traditional methods on various benchmarks. On the contrary, research works investigating unsupervised PolSAR classification are quite rare, because most CNN models need to be trained with labeled data. In this paper, we propose a completely unsupervised model by fusing the Convolutional Autoencoder (CAE) with Vector Quantization (VQ). An auxiliary Gaussian smoothing loss is adopted for better semantic consistency in the output classification map. Qualitative and quantitative experiments are carried out on satellite and airborne full polarization data (RadarSat2/E-SAR, AIRSAR). The proposed model achieves 91.87%, 83.58% and 96.93% overall accuracy (OA) on the three datasets, which are much higher than the traditional H/alpha-Wishart method, and it exhibits better visual quality as well.

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