Unsupervised change detection from multitemporal multichannel SAR images based on stationary wavelet transform

2013 ◽  
Author(s):  
Boulerbah Chabira ◽  
Takieddine Skanderi ◽  
Aichouche Belhadj aissa
Keyword(s):  
Wavelet Transform ◽  
Change Detection ◽  
Stationary Wavelet Transform ◽  
Sar Images

scholarly journals Change Detection from Remotely Sensed Images Based on Stationary Wavelet Transform

2017 ◽  
Vol 7 (6) ◽  
pp. 3395
Author(s):  
Abhishek Sharma ◽  
Tarun Gulati
Keyword(s):  
Wavelet Transform ◽  
Change Detection ◽  
High Frequency ◽  
Low Frequency ◽  
Speckle Noise ◽  
False Alarms ◽  
Stationary Wavelet Transform ◽  
Fusion Rules ◽  
Number Of False Alarms ◽  
Log Ratio

The major issue of concern in change detection process is the accuracy of the algorithm to recover changed and unchanged pixels. The fusion rules presented in the existing methods could not integrate the features accurately which results in more number of false alarms and speckle noise in the output image. This paper proposes an algorithm which fuses two multi-temporal images through proposed set of fusion rules in stationary wavelet transform. In the first step, the source images obtained from log ratio and mean ratio operators are decomposed into three high frequency sub-bands and one low frequency sub-band by stationary wavelet transform. Then, proposed fusion rules for low and high frequency sub-bands are applied on the coefficient maps to get the fused wavelet coefficients map. The fused image is recovered by applying the inverse stationary wavelet transform (ISWT) on the fused coefficient map. Finally, the changed and unchanged areas are classified using Fuzzy c means clustering. The performance of the algorithm is calculated in terms of percentage correct classification (PCC), overall error (OE) and Kappa coefficient (K<sub>c</sub>). The qualitative and quantitative results prove that the proposed method offers least error, highest accuracy and Kappa value as compare to its preexistences.

An Adaptive Rayleigh-Laplacian Based MAP Estimation Technique for Despeckling SAR Images using Stationary Wavelet Transform

2013 ◽  
Vol 4 (4) ◽  
pp. 88-102
Author(s):  
Amlan Jyoti Das ◽  
Anjan Kumar Talukdar ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Parameter Estimation ◽  
Wavelet Transform ◽  
Speckle Noise ◽  
Posteriori Probability ◽  
Wavelet Coefficients ◽  
Stationary Wavelet Transform ◽  
Sar Images ◽  
Edge Information ◽  
A Posteriori Probability ◽  
Map Estimator

Removal of speckle noise from Synthetic Aperture Radar (SAR) images is an important step before performing any image processing operations on these images. This paper presents a novel Stationary Wavelet Transform (SWT) based technique for the purpose of removing the speckle noise from the SAR returns. Maximum a posteriori probability (MAP) condition which uses a prior knowledge is used to estimate the noise free wavelet coefficients. The proposed MAP estimator is designed for this purpose which uses Rayleigh distribution for modeling the speckle noise and Laplacian distribution for modeling the statistics of the noise free wavelet coefficients. The parameters required for MAP estimator is determined by technique used for parameter estimation after SWT. Moreover an Laplacian – Gaussian based MAP estimator is also applied and the parameter estimation is done using the same method used for the proposed algorithm. For the purpose of enhancing the visual quality and to restore more edge information, a wavelet based resolution enhancement technique is also used after applying the Inverse stationary Wavelet Transform (ISWT), using interpolation technique. The experimental results show that the proposed despeckling algorithm efficiently removes speckle noise from the SAR images and restores the edge information as well.

scholarly journals AN ADAPTIVE SAR IMAGE DESPECKLING ALGORITHM USING STATIONARY WAVELET TRANSFORM

2013 ◽  
pp. 42-47
Author(s):  
AMLAN JYOTI DAS ◽  
ANJAN KUMAR TALUKDAR ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Wavelet Transform ◽  
Speckle Noise ◽  
Rayleigh Distribution ◽  
Posteriori Probability ◽  
Wavelet Coefficients ◽  
Stationary Wavelet Transform ◽  
Sar Images ◽  
A Posteriori Probability ◽  
Map Estimator ◽  
Laplacian Distribution

In this paper, we present a Stationary Wavelet Transform (SWT) based method for the purpose of despeckling the Synthetic Aperture radar (SAR) images by applying a maximum a posteriori probability (MAP) condition to estimate the noise free wavelet coefficients. The solution of the MAP estimator is based on the assumption that the wavelet coefficients have a known distribution. Rayleigh distribution is used for modeling the speckle noise and Laplacian distribution for modeling the statistics of the noise free wavelet coefficients for the purpose of designing the MAP estimator. Rayleigh distribution is used for modeling the speckle noise since speckle noise can be well described by it. The parameters required for MAP estimator is determined by the technique used for parameter estimation after SWT. The experimental results show that the proposed despeckling algorithm efficiently removes speckle noise from the SAR images.

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