Target Characterization and Scattering Power Decomposition for Full and Compact Polarimetric SAR Data

Dey, Subhadip; Bhattacharya, Avik; Ratha, Debanshu; Mandal, Dipankar; Frery, Alejandro C.

doi:10.1109/tgrs.2020.3010840

Cited by 51 publications

(34 citation statements)

References 43 publications

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“…We extract a set of seven features (F 2 ) from the TD-X data. We obtain the first three features from the model-free threecomponent scattering power decomposition technique [21,24], and the following four features as the interferometric coherence information.…”

Section: Tandem-x (Td-x)mentioning

confidence: 99%

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Synergistic Use of TanDEM-X and Landsat-8 Data for Crop-Type Classification and Monitoring

Dey

Chaudhuri

Bhogapurapu

et al. 2021

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

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Classification of crop types using Earth Observation (EO) data is a challenging task. The challenge increases many folds when we have diverse crops within a resolution cell. In this regard, optical and Synthetic Aperture Radar (SAR) data provide complementary information about the characteristics of a target. Therefore, we propose to leverage the synergy between the multispectral and Synthetic Aperture Radar (SAR) remote sensing data for crop classification. In this study, we propose to use the newly developed model-free three-component scattering power components to capture the changes of scattering mechanisms at different phenological stages. Furthermore, by incorporating interferometric coherence information, we consider the morphological characteristics of the crops that are not available with only polarimetric information. We also utilize the reflectance values from Landsat-8 spectral bands as complementary biochemical information of crops. These two pieces of information are effectively combined using a neural network-based architecture with an attention mechanism to enhance classification accuracy. In particular, the attention mechanism helps identify the most essential input information to accomplish the classification task. We utilize the time series dual co-polarimetric (i.e., HH-VV) TanDEM-X SAR data and the multispectral Landsat-8 data acquired over an agricultural area in Seville, Spain, to demonstrate the effectiveness of the proposed fusion and classification method. The use of the proposed attention mechanism for fusing SAR and optical data shows a significant improvement in classification accuracy by 6.0 % to 9.0 % as compared to the sole use of either the optical or SAR data. Besides, we also demonstrate that the utilization of single-pass interferometric coherence maps in the fusion framework enhances the overall classification accuracy by ≈ 3.0 %. Therefore, the proposed synergistic approach will facilitate accurate and robust crop mapping with high-resolution EO data at larger scales.

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Section: Tandem-x (Td-x)mentioning

confidence: 99%

“…• We utilize the scattering power components derived from the novel model-free decomposition technique for dual co-polarimetric (i.e., HH-VV) TanDEM-X SAR data [21,24]. Together, we also use the reflectance of distinct Landsat-8 multispectral bands to analyze crop phenology trends.…”

Section: Introductionmentioning

confidence: 99%

Synergistic Use of TanDEM-X and Landsat-8 Data for Crop-Type Classification and Monitoring

Dey

Chaudhuri

Bhogapurapu

et al. 2021

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

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“…Unlike the parameterization of the eigenvectors, Dey et al [9,10] proposed a scattering-type parameter θ FP , and a scattering asymmetry parameter (i.e., helicity) τ FP for full polarimetric (FP) SAR data. The method directly utilizes the coherency matrix elements (or the Kennaugh matrix elements) and the Barakat degree of polarization [11].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, one must distinctly interpret the concept of waves and media while employing them. Nevertheless, the Barakat degree of polarization has been used in the study of full polarimetric SAR data for some applications [9,10,[25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Scattering Purity and Complexity in Radar Polarimetry

Bhattacharya

Dey

Frery

2022

IEEE Trans. Geosci. Remote Sensing

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The generalized degree of polarimetric purity is a vital descriptor widely studied and interpreted for electromagnetic wave characterization. It is invariant under the rotation of the reference frame. In this work, we first propose an alternate expression of this purity measure using the mean (m) and standard deviation (s) of the real positive eigenvalues of a Hermitian positive semi-definite matrix. We then use this expression to propose a polarimetric scattering purity and scattering complexity measure. To obtain these expressions, we use certain inequalities on the bounds of the condition number for Hermitian positive definite matrices defined in terms of m and s. The polarimetric scattering purity parameter characterizes the overall polarization structure in the scattered wave. In contrast, the polarimetric scattering complexity parameter describes the mixture of orthogonal polarized pure components in the scattered wave. At first, we demonstrate the two proposed measures by analyzing two cases: (i) multiple scattering, and (ii) a mixture of canonical targets. Then, we utilize full-polarimetric C-and L-band Synthetic Aperture Radar (SAR) data to describe the variation of these measures over various land cover classes. We compare their spatial variations over the ocean surface, built-up areas, and vegetation. We observe notable contrasts in the purity and the complexity parameters over a diverse mixture of targets in the scene. Finally, we critically interpret the variation of the two measures over the temporal scene of rice crop acquired by C-band full-polarimetric SAR data. These analyses affirm the importance of these measures for explicit target characterization. The open-source version of the code is available at: https://github.com/Subho07/scattering-purity-and-complexity

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“…Usually, polarimetric parameters have been suitably attributed to the physical properties of the crop canopy [40][41][42], and have therefore helped monitor crop phenology. Recently, Dey et al [43,44] proposed a model-free target characterization parameter using full, compact and dual co-pol data [22]. This parameter utilizes the Barakat degree of polarization [45] and elements of the coherency matrix to characterize diverse target types.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data

et al. 2021

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Global crop mapping and monitoring requires high-resolution spatio-temporal information. In this regard, dual polarimetric Synthetic Aperture Radar (SAR) sensors provide high temporal and high spatial resolutions with large swath width. Generally, crop phenological development studies utilized SAR backscatter intensity-based descriptors. However, these descriptors are derived either from the covariance matrix elements or from the eigendecomposition. Therefore, this approach fails to utilize the complete polarization information of the scattered wave. In this study, we propose a target characterization parameter, θxP that utilizes the 2D Barakat degree of polarization and the elements of the covariance matrix. We also propose an unsupervised clustering scheme using θxP and the scattering entropy, HxP. We utilize time-series Sentinel-1 data of canola and wheat fields over a Canadian test site to show the sensitivity of θxP to the development of crop morphology at different phenological stages. During the initial growth stages, θxP values are low due to the low vegetation density. In contrast, at advanced phenological stages, we observe decreased values of θxP due to the appearance of complex canopy structure. Similarly, the effectiveness of the unsupervised HxP/θxP clustering plane is also evident from the temporal clustering plots. This innovative clustering framework is beneficial for the operational use of Sentinel-1 SAR data for agricultural applications.

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Target Characterization and Scattering Power Decomposition for Full and Compact Polarimetric SAR Data

Cited by 51 publications

References 43 publications

Synergistic Use of TanDEM-X and Landsat-8 Data for Crop-Type Classification and Monitoring

Synergistic Use of TanDEM-X and Landsat-8 Data for Crop-Type Classification and Monitoring

Scattering Purity and Complexity in Radar Polarimetry

Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data

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