Modifying the Yamaguchi Four-Component Decomposition Scattering Powers Using a Stochastic Distance

Bhattacharya, Avik; Muhuri, Arnab; De, Shaunak; Manickam, Surendar; Frery, Alejandro C.

doi:10.1109/jstars.2015.2420683

Cited by 63 publications

(30 citation statements)

References 40 publications

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“…Freeman-Durdeen [13], [14] and Yamaguchi [15], [16], [17] (Figure 2) techniques were used to get the scattering decomposition of physical scattering from the incoherent object model based. These techniques are based on physical scattering where Freeman-Durdeen used 3 physical scattering types: Double bounce scattering, Surface scattering, and Volume scattering while Yamaguchi technique used four physical scattering types: Double bounce scattering, Surface scattering, Volume scattering and Helix scattering.…”

Section: Resultsmentioning

confidence: 99%

Silica Sand Identification using ALOS PALSAR Full Polarimetry on The Northern Coastline of Rupat Island, Indonesia

Kausarian

Sumantyo²,

Kuze³

et al. 2016

International Journal on Advanced Science, Engineering and Information Technology

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Silica sand is one of the minerals which relatively abundant in Indonesia. One of the areas with abundant of silica sand distribution is the northern coastline of Rupat Island, Bengkalis district, Riau province, Indonesia. The distribution of silica sand in this island identified only on the northern coastline in this island. Some selected sample of silica sand was measured to get the percentage of silica sand mineral's content using X-Ray Fluorescence (XRF). Two adjacent scenes of ALOS PALSAR full-polarimetry were used. The physical properties of silica sand sample such as dielectric constant were measured using dielectric probe kit in the frequency range from 0.3 to 3.0 GHz and used for calculating the backscattering coefficient and the difference characteristics of silica sand with another object. Freeman-Durdeen and Yamaguchi techniques were used to get the scattering decomposition of physical scattering from the incoherent object model based. Surface scattering is the clearest of Scattering decomposition to show silica sand identification compares with other decompositions. From surface scattering, the backscattering coefficient value of silica sand was calculated starting from-59 dB until-52 dB. These values were given by the surface roughness condition, where the roughness is slightly rough planar. The flat condition supported by the grain size of silica sand particles that have almost the same size and shape, that were conducted by using microscopic photograph testing.

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Section: Resultsmentioning

confidence: 99%

Silica Sand Identification using ALOS PALSAR Full Polarimetry on The Northern Coastline of Rupat Island, Indonesia

Kausarian

Sumantyo²,

Kuze³

et al. 2016

International Journal on Advanced Science, Engineering and Information Technology

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“…Target decomposition can be applied to extract the scattering information, and many different target decomposition methods can be chosen for this purpose [ 37 ]. In the proposed method, Bhattacharya decomposition [ 38 ] is chosen to decide the dominant scattering mechanism of the pixels. For two adjacent pixels of the same scattering mechanism, we compute the revised Wishart distance [ 39 ].…”

Section: The Proposed Methodsmentioning

confidence: 99%

A PolSAR Image Segmentation Algorithm Based on Scattering Characteristics and the Revised Wishart Distance

Shi

et al. 2018

Sensors

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A novel segmentation algorithm for polarimetric synthetic aperture radar (PolSAR) images is proposed in this paper. The method is composed of two essential components: a merging order and a merging predicate. The similarity measured by the complex-kind Hotelling–Lawley trace (HLT) statistic is used to decide the merging order. The merging predicate is determined by the scattering characteristics and the revised Wishart distance between adjacent pixels, which can greatly improve the performance in speckle suppression and detail preservation. A postprocessing step is applied to obtain a satisfactory result after the merging operation. The decomposition and merging processes are iteratively executed until the termination criterion is met. The superiority of the proposed method was verified with experiments on two RADARSAT-2 PolSAR images and a Gaofen-3 PolSAR image, which demonstrated that the proposed method can obtain more accurate segmentation results and shows a better performance in speckle suppression and detail preservation than the other algorithms.

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“…Therefore, there usually exists overestimation of volume scattering in urban areas with orientated buildings. To overcome this problem, many methods modified the volume scattering model to reduce volume scattering power and enhance double-bounce scattering power for urban areas (Cui et al 2014;Shan et al 2012a;Cui et al 2012;An, Cui, and Yang 2010;Bhattacharya et al 2015). However, these new matrices may also bring underestimation of volume scattering over vegetated areas (Antropov, Rauste, and Hame 2011).…”

Section: Cross-scattering Coherency Matrix For Urban Areasmentioning

confidence: 99%

The cross-scattering component of polarimetric SAR in urban areas and its application to model-based scattering decomposition

Xiang

Ban

2016

International Journal of Remote Sensing

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After the work of Freeman, Durden, Pottier, and Yamaguchi, many decomposition techniques have been proposed for urban areas, mainly to resolve the overestimation problem of volume scattering. Since it has been validated that the cross-polarized (HV) scattering is caused not only by forests but also by rotated dihedrals, in this paper, we propose a cross-scattering coherency matrix to model the HV component from orientated and complex buildings and then demonstrate its performance on model-based scattering decomposition. The building orientation angle is considered in this coherency matrix, making it flexible and adaptive in the decomposition. Therefore, the HV components from forests and orientated urban areas can be modelled. Two decomposition procedures are applied in this paper. The first one is to validate the effectiveness of this scattering model. We regard the HV component from urban areas as cross-scattering, which is an independent scattering component added to the Yamaguchi's four-component decomposition. Another one is the urban area decomposition application using this scattering model. Decomposition is implemented for urban and natural areas, and the HV component from urban areas is regarded as their volume scattering. This procedure is similar to many other state-of-the-art methods for urban areas and needs to discriminate the urban and natural areas before decomposition. Spaceborne Radarsat-2 C-band, the airborne synthetic aperture radar (AIRSAR) L-band, and uninhabited aerial vehicle synthetic aperture radar (UAVSAR) L-band full polarimetric SAR data are used to validate the performance of this cross-scattering coherency matrix. The HV component of orientated buildings is generated, leading to a better decomposition result for urban areas.

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Modifying the Yamaguchi Four-Component Decomposition Scattering Powers Using a Stochastic Distance

Cited by 63 publications

References 40 publications

Silica Sand Identification using ALOS PALSAR Full Polarimetry on The Northern Coastline of Rupat Island, Indonesia

Silica Sand Identification using ALOS PALSAR Full Polarimetry on The Northern Coastline of Rupat Island, Indonesia

A PolSAR Image Segmentation Algorithm Based on Scattering Characteristics and the Revised Wishart Distance

The cross-scattering component of polarimetric SAR in urban areas and its application to model-based scattering decomposition

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