Three-Component Power Decomposition for Polarimetric SAR Data Based on Adaptive Volume Scatter Modeling

Cui, Yi; Yamaguchi, Yoshio; Yang, Jian; Park, Sang-Eun; Kobayashi, Hirokazu; Singh, Gulab

doi:10.3390/rs4061559

Cited by 39 publications

(28 citation statements)

References 11 publications

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“…In this study, an estimate of the stand volume (the base 10 logarithm of the stand volume) using the calculated parameters from the decomposition powers for P (θ) /TP (Equation (13)) is presented (Figure 8), aided by Equation (14) and achieved a relatively high correlation (R 2 = 0.557, P-value < 0.001). Only 26 sample data plots were available for our analysis, which is statistically very small, especially for young trees.…”

Section: Possibility Of Estimating the Forest Stand Volumementioning

confidence: 99%

“…The target decomposition theorem has been developed to extract surface features from radar polarimetry data. Both the three-component scattering model developed in [10] and the entropy/alpha decomposition method developed in [11] have been commonly used and are currently being improved for more accurate target classification [12][13][14][15]. The Freeman and Durden approach [16] was developed based on the three-component scattering model, which represents the surface, double-bounce and volume (canopy) scatterings.…”

Section: Introductionmentioning

confidence: 99%

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Characteristics of Decomposition Powers of L-Band Multi-Polarimetric SAR in Assessing Tree Growth of Industrial Plantation Forests in the Tropics

et al. 2012

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A decomposition scheme was applied to ALOS/PALSAR data obtained from a fast-growing tree plantation in Sumatra, Indonesia to extract tree stem information and then estimate the forest stand volume. The scattering power decomposition of the polarimetric SAR data was performed both with and without a rotation matrix and compared to the following field-measured forest biometric parameters: tree diameter, tree height and stand volume. The analytical results involving the rotation matrix correlated better than those without the rotation matrix even for natural scattering surfaces within the forests. Our primary finding was that all of the decomposition powers from the rotated matrix correlated significantly to the forest biometric parameters when divided by the total power. The surface scattering ratio of the total power markedly decreased with the forest growth, whereas the canopy and double-bounce scattering ratios increased. The observations of the OPEN ACCESS Remote Sens. 2012, 4 3059 decomposition powers were consistent with the tree growth characteristics. Consequently, we found a significant logarithmic relationship between the decomposition powers and the forest biometric parameters that can potentially be used to estimate the forest stand volume.

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Section: Possibility Of Estimating the Forest Stand Volumementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characteristics of Decomposition Powers of L-Band Multi-Polarimetric SAR in Assessing Tree Growth of Industrial Plantation Forests in the Tropics

et al. 2012

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“…In order to improve the model-fitting performance in the polarimetric decomposition, several recent studies have focused on improving the estimation accuracy of the volume scattering power [16][17][18][19]. However, experimental results in this study demonstrate that, even when the volume scattering power is successfully estimated, the ground-related scattering components can be significantly misestimated due to the topographic slope.…”

Section: Discussionmentioning

confidence: 48%

The Effect of Topography on Target Decomposition of Polarimetric SAR Data

Park

2015

Remote Sensing

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Polarimetric target decomposition enables the interpretation of radar images more easily, mostly based on physical assumptions, i.e., fitting physically-based scattering models to the polarimetric SAR observations. However, the model-fitting result cannot be always successful. Particularly, the performance of model-fitting in sloping forests is still an open question. In this study, the effect of ground topography on the model-fitting-based polarimetric decomposition techniques is investigated. The estimation accuracy of each scattering component in the decomposition results are evaluated based on the simulated target matrix by using the incoherent vegetation scattering model that accounts for the tilted scattering surface beneath the forest canopy. Experimental results show that the surface and the double-bounce scattering components can be significantly misestimated due to the topographic slope, even when the volume scattering power is successfully estimated.

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“…With the aim to favor the physical interpretation of the physical information that may be extracted from PolSAR data, in the future, additional efforts should be extended to consider alternative target decomposition theorems for classification based on a direct physical interpretation of the coherency matrix [35]. HNN can be extended by defining new regularization and separation coefficients to cope with this situation.…”

Section: Discussionmentioning

confidence: 99%

Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach

et al. 2012

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This paper proposes the optimization relaxation approach based on the analogue Hopfield Neural Network (HNN) for cluster refinement of pre-classified Polarimetric Synthetic Aperture Radar (PolSAR) image data. We consider the initial classification provided by the maximum-likelihood classifier based on the complex Wishart distribution, which is then supplied to the HNN optimization approach. The goal is to improve the classification results obtained by the Wishart approach. The classification improvement is verified by computing a cluster separability coefficient and a measure of homogeneity within the clusters. During the HNN optimization process, for each iteration and for each pixel, two consistency coefficients are computed, taking into account two types of relations between the pixel under consideration and its corresponding neighbors. Based on these coefficients and on the information coming from the pixel itself, the pixel under study is re-classified. Different experiments are carried out to verify that the proposed approach outperforms other strategies, achieving the best results in terms of separability and a trade-off with the homogeneity preserving relevant structures in the image. The performance is also measured in terms of computational central processing unit (CPU) times.

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Three-Component Power Decomposition for Polarimetric SAR Data Based on Adaptive Volume Scatter Modeling

Cited by 39 publications

References 11 publications

Characteristics of Decomposition Powers of L-Band Multi-Polarimetric SAR in Assessing Tree Growth of Industrial Plantation Forests in the Tropics

Characteristics of Decomposition Powers of L-Band Multi-Polarimetric SAR in Assessing Tree Growth of Industrial Plantation Forests in the Tropics

The Effect of Topography on Target Decomposition of Polarimetric SAR Data

Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach

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