A Markov chain model based on Cameron's CTD ship detection scheme

Kouroupis, Georgios; Anastassopoulos, V.

doi:10.1109/ist.2016.7738205

Cited by 6 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, second-order statistics regarding the appearance of the elementary scatterers are used. The work in [38] presents a CFAR detection approach which is based on the features obtained in [36].…”

Section: Introductionmentioning

confidence: 99%

Ocean Clutter Characterization Based on PolSAR Data and Second-Order Statistics of Elementary Scatterers

Κούκιου

Anastassopoulos

2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

New features are proposed for sea clutter characterization when PolSAR data are employed. Cameron coherent target decomposition is applied to characterize each SAR pixel by means of the 8 basic elementary scatterers obtained by this decomposition. Since the examined SAR pixel does not match exactly to a specific ideal elementary scatterer, the closest scatterer is assigned to this pixel along with its distance (closeness) from the ideal one. The features proposed are (a) the percentage of each elementary scatterer in the investigated region; (b) the distribution of the closeness of each declared elementary scattering mechanism to its ideal counterpart; (c) the co-occurrence of the same scattering mechanism, taking into consideration its distance from the ideal one; and (d) The co-occurrence of the different scattering mechanisms in pairs, taking again into consideration their distance from the ideal ones. Simultaneously, the skewness and the kurtosis and their 2D versions of the previously mentioned probabilities are employed to further improve ocean clutter characterization. The above features correspond to the sea state condition in each separate region, i.e., the wave height and the wind speed. A clear correspondence between the proposed features and the sea state conditions is established. Data were available from RADARSAT-2 and ALOS-1 PALSAR systems for four different ocean regions on Earth.

show abstract

Section: Introductionmentioning

confidence: 99%

Ocean Clutter Characterization Based on PolSAR Data and Second-Order Statistics of Elementary Scatterers

Κούκιου

Anastassopoulos

2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The approach of target detection using SAR imagery is varied. Papers such as Kouroupis et al [13], Allard et al [14] and Ringrose et al [15] use coherent decompositions of fully polarimetric SAR scattering matrices, such as Cameron's coherent target decomposition (CTD) and the Odd-Even basis decomposition or use spectral analysis, such as the subaperture coherence, ref. [14], to detect the targets.…”

Section: Introductionmentioning

confidence: 99%

A New Automated Ship Wake Detector for Small and Go-Fast Ships in Sentinel-1 Imagery

Grosso

Guida

2022

Remote Sensing

View full text Add to dashboard Cite

In the field of maritime surveillance with satellite imagery, ship detection with Synthetic Aperture Radar (SAR) is largely successful with the exception of those cases where the target Radar Cross Section (RCS) results very low and hard to detect. The paper presents an automatic algorithm that tackles the problem of ship detection in situations where the ship’s RCS is not measurable but its wake is. At this purpose, the algorithm proposed uses a combination of image processing techniques in order to identify ships exclusively by detecting the wakes they leave behind. Once a ship is identified through its wake, its position in the image, its heading and its speed are determined. The algorithm was developed, tested and validated on SAR imagery from the European Space Agency mission Sentinel-1; the results are here presented and discussed. The algorithm was validated on a total of 43 images and resulted in a ship detection rate of 93% with wakes’ components recognized in more than 80% of the cases analysed.

show abstract

“…Markov sequences of elementary scatterers are used as feature vector for ship detection. The work in [25] showed that studying the alternation of the elementary scatterers ships can be easily and with high reliability distinguished from the sea background. This approach was based on an innovative CFAR detection scheme.…”

Section: Introductionmentioning

confidence: 99%

Fully Polarimetric Land Cover Classification Based on Markov Chains

Κούκιου¹,

Anastassopoulos²

2021

ARS

Self Cite

View full text Add to dashboard Cite

A novel land cover classification procedure is presented utilizing the information content of fully polarimetric SAR images. The Cameron coherent target decomposition (CTD) is employed to characterize land cover pixel by pixel. Cameron's CTD is employed since it provides a complete set of elementary scattering mechanisms to describe the physical properties of the scatterer. The novelty of the proposed land classification approach lies on the fact that the features used for classification are not the types of the elementary scatterers themselves, but the way these types of scatterers alternate from pixel to pixel on the SAR image. Thus, transition matrices that represent local Markov models are used as classification features for land cover classification. The classification rule employs only the most important transitions for decision making. The Frobenius inner product is employed as similarity measure. Ten different types of land cover are used for testing the proposed method. In this aspect, the classification performance is significantly high.

show abstract

A Markov chain model based on Cameron's CTD ship detection scheme

Cited by 6 publications

References 11 publications

Ocean Clutter Characterization Based on PolSAR Data and Second-Order Statistics of Elementary Scatterers

Ocean Clutter Characterization Based on PolSAR Data and Second-Order Statistics of Elementary Scatterers

A New Automated Ship Wake Detector for Small and Go-Fast Ships in Sentinel-1 Imagery

Fully Polarimetric Land Cover Classification Based on Markov Chains

Contact Info

Product

Resources

About