Ship Classification in Single-Pol SAR Images Based on Fuzzy Logic

Margarit, G.; Tabasco, Antonio

doi:10.1109/tgrs.2011.2112371

Cited by 107 publications

(51 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Radon transform [52] is used to carry out this task and estimate the ship target heading, which is the projection of the image intensity along a radial line oriented at a specific angle. For a spatial discrete image f (x,y), the general Radon transform is defined as:…”

Section: Fine Segmentation and Symmetrymentioning

confidence: 99%

A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery

Liu

Sun

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Maritime target detection from optical remote sensing images plays an important role in related military and civil applications and its weakness lies in its compromised performance under complex uncertain conditions. In this paper, a novel hierarchical ship detection method is proposed to overcome this issue. In the ship detection stage, based on Entropy information, we construct a combined saliency model with self-adaptive weights to prescreen ship candidates from across the entire maritime domain. To characterize ship targets and further reduce the false alarms, we introduce a novel and practical descriptor based on gradient features, and this descriptor is robust against clutter introduced by heavy clouds, islands, ship wakes as well as variation in target size. Furthermore, the proposed method is effective for not only color images but also gray images. The experimental results obtained using real optical remote sensing images have demonstrated that the locations and the number of ships can be determined accurately and that the false alarm rate is greatly decreased. A comprehensive comparison is performed between the proposed method and the state-of-the-art methods, which shows that the proposed method achieves higher accuracy and outperforms all the competing methods. Furthermore, the proposed method is robust under various backgrounds of maritime images and has great potential for providing more accurate target detection in engineering applications.

show abstract

Section: Fine Segmentation and Symmetrymentioning

confidence: 99%

A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery

Liu

Sun

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…A number of SAR target classification methods have been proposed in the literature [1][2][3][4], based on radiometric and polarimetric features extracted from the image. In actual images, however, this is not always feasible, as polarimetry could not be available and artefacts due to strong reflectors, insufficient sampling rates or antenna sidelobes could be present.…”

Section: Introductionmentioning

confidence: 99%

Size and Heading of SAR-Detected Ships through the Inertia Tensor

Bedini

Righi

Salerno

2018

International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)

View full text Add to dashboard Cite

We present a strategy to estimate the heading, the length overall and the beam overall of targets already detected as ships in a wide-swath SAR image acquired by a satellite platform. Such images are often affected by distortions due to marine clutter, spectral leakage, or antenna sidelobes. These can mask the target image, thus hampering the possibility of evaluating the size and the behaviour of the ship. Even in the presence of strong artefacts, we found that the principal inertia axes can help the estimation of the target heading and be included in an iterative procedure to erode the false target features, so to enable a more accurate evaluation of the overall measurements of the ship. Here we introduce our idea and present some results obtained from real SAR images.

show abstract

“…Synthetic aperture radar (SAR) can provide wide-area, all-day and nearly all-weather observation and, as such, has played an important role in maritime ship surveillance [1]. Methods of ship detection and classification based on single-polarization SAR data have been extensively studied [2][3][4][5][6][7][8]. Recent technological trends involving SAR are moving towards the utilization of polarimetric SAR (PolSAR) data in this paper, we refer to fully-polarimetric or quad-polarimetric SAR data as polarimetric SAR (PolSAR) data).…”

Section: Introductionmentioning

confidence: 99%

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

Lang

Tao

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Scattering mechanism (SM) analysis is a promising technique for ship detection and classification in polarimetric SAR (PolSAR) images. In this paper, a four-component model-based decomposition method incorporating surface, double-bounce, volume and cross-polarized components is proposed for analyzing the SMs of ships. A novel cross-polarized scattering component capable of discriminating between the HV scattering power generated by the oriented scatterers on ships from that by volume scatterers is proposed as a means to address the problem of volume scattering power overestimation. In the decomposition stage, by taking into account both the real and imaginary parts of the elements [T] HV (1, 3) and [T] HV (2, 3) of the observed coherency matrix, the proposed cross-polarized component can preserve the reflection asymmetry information completely, which is an essential property of man-made targets, such as ships. Based on the proposed decomposition method and an analysis of the different SMs between ships and sea clutter, a novel ship detection metric defined as M = lnis proposed. Experimental results conducted on RadarSat-2 quad-polarimetric data validate the proposed four-component decomposition method as being more suitable for analyzing the SMs of ship targets than the existing helix matrix-based decomposition methods. Additionally, we find that the proposed ship detection metric can effectively enhance the signal-to-clutter ratio (SCR) and improve ship detection performance.

show abstract

Ship Classification in Single-Pol SAR Images Based on Fuzzy Logic

Cited by 107 publications

References 18 publications

A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery

A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery

Size and Heading of SAR-Detected Ships through the Inertia Tensor

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

Contact Info

Product

Resources

About