Change detection under the forest in multitemporal full-polarimetric P-band SAR images using Pauli decomposition

Rosa, Rafael A. S.; Fernandes, David; Barreto, Thiago L. M.; Wimmer, Christian; Nogueira, Joao B.

doi:10.1109/igarss.2016.7730623

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…Pauli decomposition is the most popular and widely used coherent decomposition due to its simplicity and availability [14]. The aim of the Pauli decomposition is to express the scatter matrix [S] as the weighted sum of elementary scattering matrices, which represents certain types of deterministic scattering mechanisms [4, 15]. On the condition of orthogonal linear ( h , v ) basis and S hv = S vh , the Pauli basis is expressed as follows:

(][, S_{a}) = \frac{1}{\sqrt{2}} [\begin{matrix} 1 & 0 \\ 0 & 1 \end{matrix}]

(][, S_{b}) = \frac{1}{\sqrt{2}} [\begin{matrix} 1 & 0 \\ 0 & - 1 \end{matrix}]

(][, S_{c}) = \frac{1}{\sqrt{2}} [\begin{matrix} 0 & 1 \\ 1 & 0 \end{matrix}]

Consequently, given a scattering matrix [S], it is expressed as

(][, S) = (][, \begin{matrix} 1em4pt \\ S_{hh} & S_{hv} \\ S_{hv} & S_{vv} \end{matrix}) = α (][, S_{a}) + β (][, S_{b}) + γ (][, S_{c})

where:

α = \frac{S_{hh} + S_{vv}}{\sqrt{2}}

β <...>

…”

Section: Methodsmentioning

confidence: 99%

“…At present, polarisation decomposition methods include the coherent target decomposition methods based on the scattering matrix and the non‐coherent polarisation decomposition methods based on the second‐order moment. Coherent target decomposition methods include Pauli decomposition [4], Krogager decomposition [5], Cameron decomposition [6] and so on. Non‐coherent target polarisation decomposition methods include Freeman–Durden decomposition [7], Yamaguchi decomposition [8], and Van Zyl decomposition [9].…”

Section: Introductionmentioning

confidence: 99%

“…Non‐coherent target polarisation decomposition methods include Freeman–Durden decomposition [7], Yamaguchi decomposition [8], and Van Zyl decomposition [9]. Pauli decomposition expresses the scatter matrix [S] as the weighted sum of elementary scattering matrices to represent the combination of certain types of deterministic scattering mechanisms [3, 4]. Now, Pauli decomposition is the most popular and widely used decomposition method due to its simplicity and availability [10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of polarisation features of typical targets in Chengdu using GF‐3 fully polarimetric synthetic aperture radar data

Wang¹,

Zhang²,

Ding³

et al. 2019

J. eng.

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(][, S_{a}) = \frac{1}{\sqrt{2}} [\begin{matrix} 1 & 0 \\ 0 & 1 \end{matrix}]

(][, S_{b}) = \frac{1}{\sqrt{2}} [\begin{matrix} 1 & 0 \\ 0 & - 1 \end{matrix}]

(][, S_{c}) = \frac{1}{\sqrt{2}} [\begin{matrix} 0 & 1 \\ 1 & 0 \end{matrix}]

Consequently, given a scattering matrix [S], it is expressed as

(][, S) = (][, \begin{matrix} 1em4pt \\ S_{hh} & S_{hv} \\ S_{hv} & S_{vv} \end{matrix}) = α (][, S_{a}) + β (][, S_{b}) + γ (][, S_{c})

where:

α = \frac{S_{hh} + S_{vv}}{\sqrt{2}}

β <...>

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of polarisation features of typical targets in Chengdu using GF‐3 fully polarimetric synthetic aperture radar data

Wang¹,

Zhang²,

Ding³

et al. 2019

J. eng.

View full text Add to dashboard Cite

“…Among these features, it is concluded that backscattering coefficients of HV polarization mode related to the forest canopy showed high sensitivity to the forest GSV. Although several features can be extracted from C2 matric by dual decomposition approaches, the sensitivity between the forest GSV and extracted features is so low that it cannot be involved in the estimation of forest GSV [31,42,43]. In addition, textural features obtained by GLCM are widely extracted from intensity images of different polarizations for vegetation classification.…”

Section: The Sensitivity Of Features Extracted From Dual-polarization...mentioning

confidence: 99%

Mapping Growing Stem Volume Using Dual-Polarization GaoFen-3 SAR Images in Evergreen Coniferous Forests

Long

Zheng

et al. 2023

Remote Sensing

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Unaffected by cloud cover and solar illumination, synthetic aperture radar (SAR) images have great capability to map forest growing stem volume (GSV) in complex biophysical environments. Up to now, c-band dual-polarization Gaofen-3 (GF-3) SAR images, acquired by the first Chinese civilian satellite equipped with multi-polarized modes, are rarely applied in mapping forest GSV. To evaluate the capability of dual-polarization GF-3 SAR images in mapping forest GSV, several proposed derived features were initially extracted by mathematical operations and applied to obtain optimal feature sets by different feature sorting methods and feature selection methods. Then, the maps of GSV in an evergreen coniferous forest were inverted by various machine learning algorithms and stacking ensemble learning methods with different strategies. The results implied that backscattering coefficients and partially proposed derived features showed high sensitivity to the forest GSV, and the saturation phenomenon also obviously occurred once the forest GSV was larger than 300 m3/ha. Furthermore, the results showed that the accuracy of the mapped GSV was significantly improved using the stacking ensemble learning methods. Using various optimal feature sets and base models (MLR, KNN, SVM, and RF), the rRMSE values mainly ranged from 30% to 40%. After using the stacking ensemble learning methods, the values of rRMSE ranged from 16.71% to 20.51%. This confirmed that dual-polarization GF-3 images have great potential to map forest GSV in evergreen coniferous forests.

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“…In other words, polarimetric target decompositions are a powerful approach for the interpretation and analysis of complex scattering mechanisms, since they separate the polarimetric measurements (covariance/coherency matrices) into several independent secondary elements, and then provide significant information regarding various scattering mechanisms [20]. During the last decades, researchers have studied polarimetric target decomposition for a long time, and numerous approaches have been put forward, such as Krogager decomposition [21], Cloude-Pottier [19], Freeman decomposition [22], Pauli decomposition [23], Huynen decomposition [24], and so on. Furthermore, some machine learning algorithms have broken the limitations of traditional algorithms and have made great achievements in the field of PolSAR image classification, such as the Wishart Classifier [25], Bagging [26], the Support Vector Machine (SVM) [27,28], and Random Forest (RF) [29,30].…”

Section: Introductionmentioning

confidence: 99%

A novel self‐supervised ensemble learning framework for land use and land cover classification of polarimetric synthetic aperture radar images

Darvishnezhad,

Sebt

2023

IET Radar Sonar & Navi

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Classification with a few samples of training set has been a longstanding issue in the field of polarimetric synthetic aperture radar (PolSAR) image analysis and processing. Aiming at the small number of training samples of the PolSAR image classification task, a novel Self‐Supervised Ensemble Learning Framework (SSELF) is designed. The designed SSELF can automatically extract PolSAR features conducive to PolSAR image classification with a small number of training samples. In addition, it can significantly decrease the dependence of neural network algorithms on large labelled samples of training set. First, utilise the spatial–polarimetric features of PolSAR data perfectly, the EfficientNet‐B0 is presented and utilised as the main section of the Deep Learning (DL) model to extract DL features of PolSAR data. Then, using an optimisation function that constrains the cross‐correlation matrix of various distortions of each sample to the identity matrix, the designed DL model can obtain the effective features of homogeneous samples gathering and heterogeneous samples separating from each other in a self‐supervised manner. Moreover, following the great success of curriculum learning in the area of machine learning, a novel deep curriculum‐learning model is proposed, entitled Deep Curriculum Learning (DCL), to train the DL network in our self‐supervised model. The proposed DCL utilises the entropy‐alpha target decomposition to estimate the degree of complexity of each PolSAR image patch before applying it to the EfficientNet‐B0. Also, an accumulative mini‐batch pacing function is used to introduce more difficult patches to EfficientNet‐B0 in the training process of the designed self‐supervised model. Furthermore, two EL models, feature‐level and view‐level ensemble, are proposed to increase the feature extraction capability and classification result of PolSAR data by jointly using spatial features at different scales and polarimetric information at different bands. In fact, in the proposed feature‐level ensemble strategy, to improve the classification result, the extracted features of the different scales are used as the input of the designed feature fusion algorithm. Therefore, the extracted features are mapped to the new space with lower dimension. In general, in the proposed view‐level ensemble strategy, first, the Mutual Information (MI) between each feature and the other features calculates, and then based‐on the calculated MI, different group of features are selected as various views. Next, the final classification result of the PolSAR image classification is obtained by using the designed majority vote of the result in each view. It should be noted that unlike existing traditional methods, both amplitude and phase information of SAR images to devolve the training process of the proposed model are used. In addition, all of the parameters of the proposed network are developed to a complex domain. In addition, a complex backpropagation algorithm by using the gradient‐based model is used for training the network. Finally, experimental results on three well‐known PolSAR data sets illustrate that the designed SSELF can extract more discriminant features using the designed DL model and can achieve better classification results than the other six novel DL models in terms of small training samples and adequate labelled samples.

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Change detection under the forest in multitemporal full-polarimetric P-band SAR images using Pauli decomposition

Cited by 10 publications

References 9 publications

Analysis of polarisation features of typical targets in Chengdu using GF‐3 fully polarimetric synthetic aperture radar data

Analysis of polarisation features of typical targets in Chengdu using GF‐3 fully polarimetric synthetic aperture radar data

Mapping Growing Stem Volume Using Dual-Polarization GaoFen-3 SAR Images in Evergreen Coniferous Forests

A novel self‐supervised ensemble learning framework for land use and land cover classification of polarimetric synthetic aperture radar images

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