Speckle reduction in multipolarization, multifrequency SAR imagery

1991 ◽  
Vol 29 (4) ◽  
pp. 535-544 ◽  
Author(s):  
J.-S. Lee ◽  
M.R. Grunes ◽  
S.A. Mango
Keyword(s):  
Speckle Reduction ◽  
Sar Imagery

scholarly journals A Refined Bilateral Filtering Algorithm Based on Adaptively-Trimmed-Statistics for Speckle Reduction in SAR Imagery

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 103443-103455 ◽  
Author(s):  
Jiaqiu Ai ◽  
Ruiming Liu ◽  
Bo Tang ◽  
Lu Jia ◽  
Jinling Zhao ◽  
...  
Keyword(s):  
Speckle Reduction ◽  
Bilateral Filtering ◽  
Filtering Algorithm ◽  
Sar Imagery

Speckle Reduction In Polarimetric Sar Imagery

2005 ◽  
Author(s):  
Jong-Sen Lee ◽  
M.R. Grimes ◽  
S.A. Mango
Keyword(s):  
Speckle Reduction ◽  
Polarimetric Sar ◽  
Sar Imagery

SPECKLE REDUCTION IN POLARIMETRIC SAR IMAGERY COMBINING BY PWF AND STATIONARY WAVELET THRESHOLDING

2006 ◽  
Vol 04 (04) ◽  
pp. 589-599 ◽  
Author(s):  
QINGWEI GAO ◽  
DEXIANG ZHANG ◽  
YANG WANG
Keyword(s):  
Speckle Noise ◽  
Experimental Result ◽  
Speckle Reduction ◽  
Wavelet Thresholding ◽  
Sar Image ◽  
Stationary Wavelet Transform ◽  
Whitening Filter ◽  
Sar Imagery ◽  
Target Characteristics ◽  
Self Adaptive

As we know, a polarimetric whitening filter can efficiently reduce speckle under the circumstances of keeping the same resolution, but it is not self-adaptive. Although it can be transformed to be self-adaptive by adding a window, the blur is serious on the heterogeneous areas or brightness. In this paper, we present a new algorithm. First, fully polarimetric measurements (HH, HV, VV) are processed by polarimetric whitening filters (PWF) method. Second, the PWF result is de-noised by stationary wavelet transform thresholding. Experimental result shows that this method not only suppresses the speckle noise effectively, but also preserves as many target characteristics of original images as possible. The final visual effect of the recovery SAR image is satisfying.

Variational method of speckle reduction and boundary detection in SAR imagery

2008 ◽  
Author(s):  
Zhipeng Xia ◽  
Xiaona Tang ◽  
Fang Li ◽  
Guixu Zhang
Keyword(s):  
Variational Method ◽  
Boundary Detection ◽  
Speckle Reduction ◽  
Sar Imagery

scholarly journals Speckle reduction in polarimetric SAR imagery with stochastic distances and nonlocal means

2014 ◽  
Vol 47 (1) ◽  
pp. 141-157 ◽  
Author(s):  
Leonardo Torres ◽  
Sidnei J.S. Sant'Anna ◽  
Corina da Costa Freitas ◽  
Alejandro C. Frery
Keyword(s):  
Speckle Reduction ◽  
Polarimetric Sar ◽  
Nonlocal Means ◽  
Sar Imagery

scholarly journals Green Infrastructure Mapping in Urban Areas Using Sentinel-1 Imagery

2021 ◽  
Vol 42 (2) ◽  
pp. 337-356
Author(s):  
Mateo Gašparović ◽  
Dino Dobrinić
Keyword(s):  
Green Infrastructure ◽  
Classification Accuracy ◽  
Urban Areas ◽  
Texture Features ◽  
Computational Time ◽  
Speckle Reduction ◽  
Support Vector ◽  
Svm Classifier ◽  
Sar Imagery ◽  
Processing Steps

High temporal resolution of synthetic aperture radar (SAR) imagery (e.g., Sentinel-1 (S1) imagery) creates new possibilities for monitoring green vegetation in urban areas and generating land-cover classification (LCC) maps. This research evaluates how different pre-processing steps of SAR imagery affect classification accuracy. Machine learning (ML) methods were applied in three different study areas: random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB). Since the presence of the speckle noise in radar imagery is inevitable, different adaptive filters were examined. Using the backscattering values of the S1 imagery, the SVM classifier achieved a mean overall accuracy (OA) of 63.14%, and a Kappa coefficient (Kappa) of 0.50. Using the SVM classifier with a Lee filter with a window size of 5×5 (Lee5) for speckle reduction, mean values of 73.86% and 0.64 for OA and Kappa were achieved, respectively. An additional increase in the LCC was obtained with texture features calculated from a grey-level co-occurrence matrix (GLCM). The highest classification accuracy obtained for the extracted GLCM texture features using the SVM classifier, and Lee5 filter was 78.32% and 0.69 for the mean OA and Kappa values, respectively. This study improved LCC with an evaluation of various radiometric and texture features and confirmed the ability to apply an SVM classifier. For the supervised classification, the SVM method outperformed the RF and XGB methods, although the highest computational time was needed for the SVM, whereas XGB performed the fastest. These results suggest pre-processing steps of the SAR imagery for green infrastructure mapping in urban areas. Future research should address the use of multitemporal SAR data along with the pre-processing steps and ML algorithms described in this research.

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