On the Role of Polarimetric Decomposition and Speckle Filtering Methods for C-Band SAR Wetland Classification Purposes

Gierszewska, Monika; Berezowski, Tomasz

doi:10.1109/jstars.2022.3162641

Cited by 4 publications

(3 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, despite more polarimetric information being provided by quad-pol than dual-pol SAR images, the SI g of Hansbreen and Hornbreen was not recognized as a separate class by any of the methods in this study, or by using different SAR band lengths. This can be due to (1) the thin layer of SI g [25], which does not have a large influence on overall SAR reflectance in either C-or L-band; (2) low representation, or poor separation, of data representing the local SI g , so it is not recognized by the GMM-EM algorithm [81]; or (3) too strong a speckle filtering algorithm [82,83], so that the local SI g is averaged with other zones.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data

et al. 2023

View full text Add to dashboard Cite

Changes in glacier zones (e.g., firn, superimposed ice, ice) are good indicators of glacier response to climate change. There are few studies of glacier zone detection by SAR that are focused on more than one ice body and validated by terrestrial data. This study is unique in terms of the dataset collected—four C- and L-band quad-pol satellite SAR images, Ground Penetrating Radar data, shallow glacier cores—and the number of land ice bodies analyzed, namely, three tidewater glaciers in Svalbard and one ice cap in Iceland. The main aim is to assess how well popular methods of SAR analysis perform in distinguishing glacier zones, regardless of factors such as the morphologic differences of the ice bodies, or differences in SAR data. We test and validate three methods of glacier zone detection: (1) Gaussian Mixture Model–Expectation Maximization (GMM-EM) clustering of dual-pol backscattering coefficient (sigma0); (2) GMM-EM of quad-pol Pauli decomposition; and (3) quad-pol H/α Wishart segmentation. The main findings are that the unsupervised classification of both sigma0 and Pauli decomposition are promising methods for distinguishing glacier zones. The former performs better at detecting the firn zone on SAR images, and the latter in the superimposed ice zone. Additionally, C-band SAR data perform better than L-band at detecting firn, but the latter can potentially separate crevasses via the classification of sigma0 or Pauli decomposition. H/α Wishart segmentation resulted in inconsistent results across the tested cases and did not detect crevasses on L-band SAR data.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The SAR imaging system introduces the speckle noise of SAR images. Unlike the widely-studied additive noise in optical images, it belongs to multiplicative noise, a large proportion of which is of high-frequency [12]- [14], [50]- [52]. To prove the effectiveness of target recognition of the suggested method for speckle noise SAR images, we began with speckle noise modeling referring to the literature [11] for the generation of SAR images with various degrees of speckle noise.…”

Section: A Speckle Noise Data Augmentation Mechanismmentioning

confidence: 99%

Dual Consistency Alignment Based Self-Supervised Learning for SAR Target Recognition With Speckle Noise Resistance

Zhai

Liao

Sun

et al. 2023

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

View full text Add to dashboard Cite

Deep learning based on Convolutional Neural Networks (CNN) has been widely applied in Synthetic Aperture Radar (SAR) target recognition and made significant progress. However, due to the physical effects of the equipment used to collect images, various degrees of speckle noise will be introduced into SAR images. Traditional CNN based SAR target recognition methods are premised on the same noise intensity in the training and testing set, which is contrary to the target recognition in practice. To alleviate this problem, we propose a novel speckle noise resistant framework for SAR target recognition, called Dual Consistency Alignment based Self-Supervised Learning(DCA-SSL). Firstly, original SAR images are randomly added to speckle noise with different thresholds through multiplicative noise, after which contrastive pre-training is performed on unlabeled data. During this period, we combine instance pseudo-label consistency alignment and feature consistency alignment to align multiple threshold speckle noise views with original views under the same targets. Finally, the pre-trained model is migrated to the downstream SAR speckle noise target recognition task. In this article, speckle noise modeling is conducted based on MSTAR data testing set, and experiment results reveal that this method can adapt to different intensities of speckle noise, is robust to modeled SAR image recognition and maintains a high recognition rate even in small sample learning. Our code is available at: https://github.com/Jordan-Liao/DCA-SSL.

show abstract

“…The polarization scattering feature is used to represent the original SAR data, including matrix elements, polarization decomposition, and backscattering features (Table 4). They are designed to reflect the different geometric and dielectric properties of ground objects [39][40][41][42]. The H-A-α decomposition method proposed by Cloude and Pottier is used to extract the eigenvalues and eigenvectors of dual-polarization (DP) SAR data by covari-ance matrix [C] Equation (8).…”

Section: Polarimetric Scattering Featurementioning

confidence: 99%

A Deep Feature Fusion Method for Complex Ground Object Classification in the Land Cover Ecosystem Using ZY1-02D and Sentinel-1A

Tian

2023

Land

View full text Add to dashboard Cite

Despite the successful application of multimodal deep learning (MDL) methods for land use/land cover (LULC) classification tasks, their fusion capacity has not yet been substantially examined for hyperspectral and synthetic aperture radar (SAR) data. Hyperspectral and SAR data have recently been widely used in land cover classification. However, the speckle noise of SAR and the heterogeneity with the imaging mechanism of hyperspectral data have hindered the application of MDL methods for integrating hyperspectral and SAR data. Accordingly, we proposed a deep feature fusion method called Refine-EndNet that combines a dynamic filter network (DFN), an attention mechanism (AM), and an encoder–decoder framework (EndNet). The proposed method is specifically designed for hyperspectral and SAR data and adopts an intra-group and inter-group feature fusion strategy. In intra-group feature fusion, the spectral information of hyperspectral data is integrated by fully connected neural networks in the feature dimension. The fusion filter generation network (FFGN) suppresses the presence of speckle noise and the influence of heterogeneity between multimodal data. In inter-group feature fusion, the fusion weight generation network (FWGN) further optimizes complementary information and improves fusion capacity. Experimental results from ZY1-02D satellite hyperspectral data and Sentinel-1A dual-polarimetric SAR data illustrate that the proposed method outperforms the conventional feature-level image fusion (FLIF) and MDL methods, such as S2ENet, FusAtNet, and EndNets, both visually and numerically. We first attempt to investigate the potentials of ZY1-02D satellite hyperspectral data affected by thick clouds, combined with SAR data for complex ground object classification in the land cover ecosystem.

show abstract

On the Role of Polarimetric Decomposition and Speckle Filtering Methods for C-Band SAR Wetland Classification Purposes

Cited by 4 publications

References 52 publications

Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data

Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data

Dual Consistency Alignment Based Self-Supervised Learning for SAR Target Recognition With Speckle Noise Resistance

A Deep Feature Fusion Method for Complex Ground Object Classification in the Land Cover Ecosystem Using ZY1-02D and Sentinel-1A

Contact Info

Product

Resources

About