A Modified General Polarimetric Model-Based Decomposition Method With the Simplified Neumann Volume Scattering Model

Xie, Qinghua; Zhu, Jianjun; López-Sánchez, Juan M.; Wang, Changcheng; Fu, Haiqiang

doi:10.1109/lgrs.2018.2830503

Cited by 17 publications

(19 citation statements)

References 17 publications

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“…With the variation of τ, the volume can change from a preferred orientation direction (τ = 0) to completely random (τ = 1). Under the linear approximation for the orientation distribution, one can obtain two linear models for the coherency matrix form [33,[37][38][39]]…”

Section: Neumann Decompositionmentioning

confidence: 99%

“…Two Yamaguchi volume scattering models are also approximately considered as two special cases of it [37,38]. It should be noted that Neumann generalized volume scattering model could be incorporated in any PolSAR/PolInSAR model-based decomposition framework [33]. However, the Neumann decomposition is generally employed by matching completely the Neumann volume scattering model to the observed coherency matrix in order to directly invert two model parameters.…”

Section: Neumann Decompositionmentioning

confidence: 99%

“…The Freeman-Durden three-component decomposition is the pioneering model-based decomposition method, which defines three scattering models to represent surface scattering, double-bounce scattering and volume scattering in the scene [23]. Afterwards, plenty of improvements to enhance decomposition performance have been proposed [24][25][26][27][28][29][30][31][32][33]. The obtained scattering powers from different scattering components can be used as features for crop classification.…”

Section: Introductionmentioning

confidence: 99%

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On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

et al. 2019

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In previous studies, parameters derived from polarimetric target decompositions have proven as very effective features for crop classification with single/multi-temporal polarimetric synthetic aperture radar (PolSAR) data. In particular, a classical eigenvalue-eigenvector-based decomposition approach named after Cloude–Pottier decomposition (or “H/A/α”) has been frequently used to construct classification approaches. A model-based decomposition approach proposed by Neumann some years ago provides two parameters with very similar physical meanings to polarimetric scattering entropy H and the alpha angle α in Cloude–Pottier decomposition. However, the main aim of the Neumann decomposition is to describe the morphological characteristics of vegetation. Therefore, it is worth investigating the performance of Neumann decomposition on crop classification, since vegetation is the principal type of targets in agricultural scenes. In this paper, a multi-temporal supervised classification method based on Neumann decomposition and Random Forest Classifier (named “ND-RF”) is proposed. The three parameters from Neumann decomposition, computed along the time series of data, are used as classification features. Finally, the Random Forest Classifier is applied for supervised classification. For comparison, an analogue classification scheme is constructed by replacing the Neumann decomposition with the Cloude–Pottier decomposition, hence named CP-RF. For validation, a time series of 11 polarimetric RADARSAT-2 SAR images acquired over an agricultural site in London, Ontario, Canada in 2015 is employed. Totally, 10 multi-temporal combinations of datasets were tested by adding images one by one sequentially along the SAR observation time. The results show that the ND-RF method generally produces better classification performance than the CP-RF method, with the largest improvement of over 12% in overall accuracy. Further tests show that the two parameters similar to entropy and alpha angle produce classification results close to those of CP-RF, whereas the third parameter in the Neumann decomposition is more effective in improving the classification accuracy with respect to the Cloude–Pottier decomposition.

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Section: Neumann Decompositionmentioning

confidence: 99%

Section: Neumann Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

et al. 2019

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“…Then, some excellent publications have appeared in the literature [8]- [14], and the review of these decomposition theorems is summarized in [15]. The decomposition technique is divided into two main categories: Model-based decomposition [16]- [22] and eigenvalue-eigenvector-based decomposition [23]- [25]. Among them, the model-based decomposition is directly related to the physical scattering mechanism, which can be constructed based on coherency or covariance matrix.…”

mentioning

confidence: 99%

“…Several models were compared in benchmarking exercises to evaluate the performance of the existing decomposition methods [29], [34]. To overcome the OVS, the volume scattering models, which continuously cover all possible variations are widely used and the extended decomposition methods have been proposed [19], [22], [28], [30], [35]. Among these extended decomposition methods, the generalized volume scattering model (GVSM) proposed by Antropov et al [19] is widely used to estimate the volume scattering component (FD/GVSM).…”

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confidence: 99%

A Modified Four-Component Decomposition Method With Refined Volume Scattering Models

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this article, a modified four-component decomposition method with refined volume scattering models is proposed for polarimetric synthetic aperture radar (SAR) image processing. In the new decomposition method, after the orientation angle compensation, the orientation angle is placed in the probability density functions. General forms of the volume scattering models and branch conditions can be obtained. Similar to the four-component scattering power decomposition with extended volume scattering model (S4R) proposed by Sato et al., refined volume scattering models can be used for various land covers based on the criteria. Since the orientation angles are contained in the refined volume scattering models, the oriented buildings can be discriminated from the vegetation areas and the overestimation problem of volume scattering is substantially overcome. In this article, the performance of the proposed method is evaluated by the spaceborne C-band Gaofen-3 data and airborne L-band E-SAR data. Several approaches are employed as a comparison of the proposed methods. Experimental results show that, compared with the existing decomposition methods, the proposed method can effectively represent the scattering characteristics of the ambiguous regions, and the double-bounce scattering contributions over the urban areas can be substantially enhanced.

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An Optimal Eigenvalue-Based Decomposition Approach for Estimating Forest Parameters Over Forest Mountain Areas

Tan

Pham

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A Modified General Polarimetric Model-Based Decomposition Method With the Simplified Neumann Volume Scattering Model

Cited by 17 publications

References 17 publications

On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

A Modified Four-Component Decomposition Method With Refined Volume Scattering Models

An Optimal Eigenvalue-Based Decomposition Approach for Estimating Forest Parameters Over Forest Mountain Areas

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