A Four-Component Decomposition of POLSAR Images Based on the Coherency Matrix

Yamaguchi, Yoshio; Yuki, Yasuo; Yamada, Hiroyoshi

doi:10.1109/lgrs.2006.869986

Cited by 242 publications

(150 citation statements)

References 4 publications

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“…The surface scattering component models the scattering from first-order Bragg surfaces (sea surface) and planes (ship deck). Following [26], the coherency matrices for double-bounce and surface components are given as Equations (13) and (14), respectively:…”

Section: Double-bounce and Surface Scattering Modelmentioning

confidence: 99%

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

Lang

Tao

et al. 2017

Remote Sensing

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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.

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Section: Double-bounce and Surface Scattering Modelmentioning

confidence: 99%

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

Lang

Tao

et al. 2017

Remote Sensing

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“…From a scattering matrix, the coherency matrix is derived by Equation (2): From the coherency matrix, four-component decomposition decomposes the observed backscattering into four components, the surface scattering power (Ps), the double-bounce scattering power (P d ), the volume scattering power (Pv), and the helix scattering power (Pc) (Yamaguchi et al, 2005). Yamaguchi et al (2006) proposed an algorithm that rotates the coherency matrix by the POA in order to reduce the dependence of the components on the relative azimuth. The POA is defined by Equation (3).…”

Section: Four Component Decomposition and Multilooingmentioning

confidence: 99%

Urban Area Extraction Using Airborne X-Band Fully Polarimetric Pi-Sar2 Imagery

Susaki

Kishimoto

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, III, VII WG I/2, ICWG III/VII KEY WORDS: PiSAR-2, Urban area, VHR SAR, Fully polarimetric SAR ABSTRACT:In this paper, we present a method to extract urban areas from X-band fully polarimetric synthetic aperture radar (SAR) data. It is known that very high resolution (VHR) SAR can extract buildings, but it requires more processes to map urban areas that should include other objects. The proposed method is mainly composed of two classifications. One classification uses total power of scattering and volume scattering derived by using four component decomposition method with correction of the polarization orientation angle (POA) effect. The other classification uses polarimetric coherency between SHH and SV V . The two results are intersected and final urban areas are extracted after post-classification processing. We applied the proposed method to airborne X-band fully polarimetric SAR data of Polarimetric and Interferometric Airborne Synthetic Aperture Radar System (Pi-SAR2), developed by the National Institute of Information and Communications Technology (NICT), Japan. The validation show that the results of the proposed method were acceptable, with an overall accuracy of approximately 80 to 90% at 100-m spatial scale.

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“…These concepts are decomposing the total received energy (the sum of all acquired polarimetric channels and called span) into individual components. The frequently used decomposition models are Power Decompositions (e.g., Freeman-Durden Decomposition [43] and the Yamaguchi Decomposition [44]) and Eigendecompositions (e.g., Cloude Pottiers' Entropy/Alpha decomposition [45]). Most of these models were designed for the usage of quad-polarized data but some of them can be adapted to dual-polarized data as specified in the following.…”

Section: Decomposition Of Polsar Datamentioning

confidence: 99%

“…The four component Yamaguchi Decomposition [44] is decomposing the span into the scattering power components of surface scattering, double bounce, volume scattering and helix scattering. It is based on the spatial average Coherency Matrix (T-Matrix) of quad-polarized data.…”

Section: Decomposition Of Polsar Datamentioning

confidence: 99%

See 1 more Smart Citation

Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada

et al. 2014

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Abstract:In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate 8566(dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques.

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A Four-Component Decomposition of POLSAR Images Based on the Coherency Matrix

Cited by 242 publications

References 4 publications

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

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

Urban Area Extraction Using Airborne X-Band Fully Polarimetric Pi-Sar2 Imagery

Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada

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