Four-Component Model-Based Decomposition for Ship Targets Using PolSAR Data

Xi, Yuyang; Lang, Haitao; Tao, Yunhong; Huang, Lin Ping; Pei, Zijun

doi:10.3390/rs9060621

Cited by 25 publications

(14 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For cargo ships, containers, warships, and other large metal structure vessels, due to their complex structure and the influence of sea surface motion, the scattering of ship is complex, which mainly includes surface scattering on the surface of ships, double-bounce scattering caused by the hull's structure, double-bounce scattering between ships and sea, and volume scattering and helix scattering caused by the complex superstructure of ships. Among these, the main scattering components of ships is double-bounce scattering [11,14,28]. However, for small-sized ships, especially the nonmetallic fishing boats, it is not easy to generate strong dihedral angle scattering if there is no obvious dihedral angle or trihedral angle reflector structure found on the ship.…”

Section: Target Detection Methods Based On Scattering Difference For Pmentioning

confidence: 99%

“…In order to comprehensively evaluate the ship detection performance of the proposed method, the quality factor FOM, which considers both the detection rate (PD) and false alarm rate (PFA) of the target, was used to evaluate the detection results [11]. The higher FOM is, the better the detection performance will be.…”

Section: Ship Detection Performance Validationmentioning

confidence: 99%

“…Sugimoto et al analyzed the differences of scattering mechanisms between ship and sea, then applied the Yamaguchi decomposition theory and CFAR method to achieve the goal [10]. Xi et al developed a new four-component decomposition method based on the Yamaguchi decomposition theory, and defined a new detector using the component to ship detection [11]. Marino et al developed a polarmatric notch filter for ship detection depending on the coherence of two scattering vectors [12,13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Small Ship Target Detection Method Based on Polarimetric SAR

2019

View full text Add to dashboard Cite

The detection of small fishing ships is very important for maritime fishery supervision. However, it is difficult to detect small ships using synthetic aperture radar (SAR), due to the weak target scattering and very small number of pixels. Polarimetric synthetic aperture radar (PolSAR) has been widely used in maritime ship detection due to its abundant target scattering information. In the present paper, a new ship detector, named ΛM, is developed based on the analysis of polarization scattering differences between ship and sea, then combined with the two-parameter constant false alarm rate method (TP-CFAR) algorithm to conduct ship detection. The goals of the detector construction are to fully consider the ship’s depolarization effect, and further amplify it through sliding window processing. First, the signal-to-clutter ratio (SCR) enhancement performance of ΛM for ships with different lengths ranging from 8 to 230 m under 90 different combinations of windows are analyzed in detail using three set of RADARSAT-2 quad-polarization data, then the appropriate window size is determined. In addition, the SCR enlargement between ΛM and some typical polarization features is compared. Among these, for ships of length greater than 35 m, the average contrast of ΛM is 33.7 dB, which is 20 dB greater than that of the HV channel. For small vessels of length less than 16 m, the average contrast of ΛM is 16 dB higher than that of HV channel on average. Finally, the RADARSAT-2 data including nonmetallic small vessels are used to perform ship detection tests, and the detection ability for conventional and small ships of some classic algorithms are compared and analyzed. For large vessels of length greater than 35 m, the method proposed in this paper is able to obtain a superior detection result, maintain the ship contour well, and suppress false alarms caused by the cross side lobe in the SAR image. For small vessels of length less than 16 m, the method proposed in this paper can reduce the number of missed targets, while also obtaining superior detection results, especially for small nonmetallic vessels.

show abstract

Section: Target Detection Methods Based On Scattering Difference For Pmentioning

confidence: 99%

Section: Ship Detection Performance Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Small Ship Target Detection Method Based on Polarimetric SAR

2019

View full text Add to dashboard Cite

show abstract

“…Polarimetric SAR (PolSAR), in addition to the characteristics mentioned above, is able to use the backscattering of polarization waves from objects to form images. In recent years, research on PolSAR has attracted wide interest in the field of remote sensing, and applications of PolSAR images have gradually increased, such as crop classification [1], ship detection [2], and change detection [3]. PolSAR image classification assigns a specific ground category to each pixel.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

Xing

Jiao

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of-the-art classification methods heavily suffer from the speckle noise. To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed. First, the Pauli-RGB image is over-segmented into superpixels to obtain a large number of homogeneous areas. Then, features that can mitigate the effects of the speckle noise are obtained using spatial weighting in the same superpixel. Next, the training set is expanded iteratively utilizing a semi-supervised unlabeled sample selection strategy that elaborately makes use of spatial relations provided by superpixels. In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results. Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.

show abstract

“…Among these studies, incoherent target decomposition is one of the hottest branches, which has spawned attention from scholars of PolSAR field to a great extent [1][2][3][4]. The existing incoherent target decomposition methods can be classified into three categories, namely, model-based, eigenvalue-based, and hybrid decomposition categories.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Extension of General Four-Component Scattering Power Decomposition

et al. 2017

View full text Add to dashboard Cite

Abstract:The overestimation of volume scattering (OVS) is an intrinsic drawback in model-based polarimetric synthetic aperture radar (PolSAR) target decomposition. It severely impacts the accuracy measurement of scattering power and leads to scattering mechanism ambiguity. In this paper, a hierarchical extended general four-component scattering power decomposition method (G4U) is presented. The conventional G4U is first proposed by Singh et al. and it has advantages in full use of information and volume scattering characterization. However, the OVS still exists in the G4U and it causes a scattering mechanism ambiguity in some oriented urban areas. In the proposed method, matrix rotations by the orientation angle and the helix angle are applied. Afterwards, the transformed coherency matrix is applied to the four-component decomposition scheme with two refined models. Moreover, the branch condition applied in the G4U is substituted by the ratio of correlation coefficient (RCC), which is used as a criterion for hierarchically implementing the decomposition. The performance of this approach is demonstrated and evaluated with the Airborne Synthetic Aperture Radar (AIRSAR), Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), Radarsat-2, and the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) fully polarimetric data over different test sites. Comparison studies are carried out and demonstrated that the proposed method exhibits promising improvements in the OVS and scattering mechanism characterization.

show abstract

Four-Component Model-Based Decomposition for Ship Targets Using PolSAR Data

Cited by 25 publications

References 33 publications

A Small Ship Target Detection Method Based on Polarimetric SAR

A Small Ship Target Detection Method Based on Polarimetric SAR

Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

A Hierarchical Extension of General Four-Component Scattering Power Decomposition

Contact Info

Product

Resources

About