Incoherent Target Scattering Decomposition of Polarimetric SAR Data Based on Vector Model Roll-Invariant Parameters

Aghababaee, Hossein; Sahebi, Mahmod Reza

doi:10.1109/tgrs.2016.2540807

Cited by 20 publications

(5 citation statements)

References 24 publications

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“…Typically, decomposition is an alternative approach that may provide a promising solution in the scattering-mechanism separation. The scattering-mechanism decomposition effectiveness in PolSAR [40], [41] and polarimetric interferometric SAR (PolInSAR) [42] images has already been proved. With respect to polarimetric MB data, different processing strategies have been presented in the literature [17]- [19]; among them, one widely used approach is the sum of Kronecker products (SKP) [17], which provides the basis for the decomposition of the MB data from a forested area along the elevation direction.…”

Section: Polarimetric Backscattering Separationmentioning

confidence: 99%

Forest SAR Tomography: Principles and Applications

Aghababaei

Ferraioli

Ferro-Famil

et al. 2020

IEEE Geosci. Remote Sens. Mag.

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Section: Polarimetric Backscattering Separationmentioning

confidence: 99%

Forest SAR Tomography: Principles and Applications

Aghababaei

Ferraioli

Ferro-Famil

et al. 2020

IEEE Geosci. Remote Sens. Mag.

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“…Due to the aperture synthesis in the azimuth direction, the aforementioned anomalies affect the decomposition modeling. Therefore, various modifications have been introduced in model-based decomposition to enhance the decomposition results such as nonnegative eigenvalue constraint (Van Zyl et al, 2011), deorientation (Xiang et al, 2016), generalizing, and modification of model components as per the use case (Aghababaee & Sahebi, 2016;An et al, 2010;Bhattacharya, Muhuri, et al, 2015;Duan & Wang, 2017;Shuang et al, 1985;Yamaguchi et al, 2010;Zou et al, 2015). The modified decomposition models address the problem to some extent; however, the predominant volume scattering observed over clutter urban targets persists.…”

Section: Introductionmentioning

confidence: 99%

PolInSAR Coherence and Entropy‐Based Hybrid Decomposition Model

Shafai

Kumar

2020

Earth and Space Science

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Target characterization is an essential aspect of polarimetric decomposition. This technique is capable of categorizing polarimetric signatures for different types of targets based on the scattering mechanisms they follow, enabling straightforward physical interpretation of the targets. The geometric anomalies associated with human-made targets escalate the degree of randomness in the scattering process, which causes scattering ambiguity for such targets. The second-order model descriptors do not relate to the actual physical structure and yield predominant volume scattering power. Such urban targets are decomposed as natural targets leading to irrelevant decomposition results. The methods developed to curb the problem are unable to maintain the consistency in the decomposition modeling as they underestimate volume scattering powers for natural landcover. A hybrid decomposition model is proposed herein to solve the problem of predominant volume scattering observed from urban targets by preserving volume scattering powers for natural targets. The model uses eigenvalue-based decomposition parameters and polarimetric interferometric synthetic aperture radar (PolInSAR) coherence to decompose ambiguous targets. The proposed model has been tested on NISAR UAVSAR PolInSAR data acquired over the Greenville region, MS, USA. The proposed model has increased the double-bounce scattering from the urban targets and enhanced the volume scattering from natural landcover as well. By comparing the results with existing decomposition models, it is observed that the proposed model gives a more robust representation of the landcover than the compared decomposition models. Plain Language Summary The SAR sensor illuminates the surface with polarized microwaves and receives the interacted backscattered wave. The backscattered wave is transformed into information, which is represented by a scattering matrix. Due to different geophysical properties of the targets, there is a change in the backscattered wave. Polarimetric SAR decomposition is a technique that can relate the change in backscatter to the physical structure of the target, enabling simple and robust classification of the landcover. The techniques decompose or disintegrate the scattering matrix into scattering mechanisms, and each scattering mechanism represents a broad category of different landcover. Owning to the random nature of real-world observations, the backscatter for two different categories of landcover can be the same, such as a building and a tree. Therefore, for such cases, the polarimetric decomposition yields inaccurate classification results, and from the polarimetric point of view, this problem is referred to as overestimation of volume scattering. The reason for this is the assumptions and the design of the model. Various modifications have been proposed to solve the problem of overestimation. We find that direct modification may yield inconsistent decomposition results, which can be critical for different utilities of polarimetric decompositions. We propose...

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“…The coherent target decomposition algorithms include the Pauli decomposition, the sphere-diplane-helix (SDH) decomposition [5], the symmetric scattering characterization method (SSCM) [6], Cameron decomposition [7], Yamaguchi Fourcomponent scattering power decomposition [8], General polarimetric model-based decomposition [9], [10], and some advances [11], [12]. The incoherent target decomposition algorithms include Huynen decomposition [13], Freeman-Durden decomposition [14], Yamguchi four-component decomposition [15], Cloude-Pottier decomposition [16], [17], and a number of approaches have been reported [18], [19], [20]. In addition to feature based on the polarization mechanism [21], [22], [23], [24], there are some traditional features of natural images, which have been utilized to analyze PolSAR image, such as color features [25], texture features [26], spatial relations [27], etc.…”

Section: Introductionmentioning

confidence: 99%

Polarimetric Convolutional Network for PolSAR Image Classification

Liu

Jiao

Tang

et al. 2019

IEEE Trans. Geosci. Remote Sensing

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The approaches for analyzing the polarimetric scattering matrix of polarimetric synthetic aperture radar (PolSAR) data have always been the focus of PolSAR image classification. Generally, the polarization coherent matrix and the covariance matrix obtained by the polarimetric scattering matrix are used as the main research object to extract features. In this paper, we focus on the original polarimetric scattering matrix and propose a polarimetric scattering coding way to deal with polarimetric scattering matrix and obtain a close complete feature. This encoding mode can also maintain polarimetric information of scattering matrix completely. At the same time, in view of this encoding way, we design a corresponding classification algorithm based on convolution network to combine this feature. Based on polarimetric scattering coding and convolution neural network, the polarimetric convolutional network is proposed to classify PolSAR images by making full use of polarimetric information. We perform the experiments on the PolSAR images acquired by AIRSAR and RADARSAT-2 to verify the proposed method. The experimental results demonstrate that the proposed method get better results and has huge potential for PolSAR data classification. Source code for polarimetric scattering coding is available at https://github.com/liuxuvip/Polarimetric-Scattering-Coding.

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Incoherent Target Scattering Decomposition of Polarimetric SAR Data Based on Vector Model Roll-Invariant Parameters

Cited by 20 publications

References 24 publications

Forest SAR Tomography: Principles and Applications

Forest SAR Tomography: Principles and Applications

PolInSAR Coherence and Entropy‐Based Hybrid Decomposition Model

Polarimetric Convolutional Network for PolSAR Image Classification

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