Speckle noise removal in SAR images using Multi-Objective PSO (MOPSO) algorithm

Sivaranjani, R.; Roomi, S. Mohamed Mansoor; Senthilarasi, M.

doi:10.1016/j.asoc.2018.12.030

Cited by 39 publications

(9 citation statements)

References 35 publications

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“…Taken together, the two previously mentioned weaknesses imply the importance of pre-processing the images to reduce the speckle noise [39][40][41], and potential difficulties in properly detecting, segmenting or delineating small elements. This has been proven to be a problem when detecting damage at the building level for lower-resolution images [42], but it does not prevent the broader categorization of the different levels of damage that buildings can suffer from a tsunami over large areas [13], namely being slightly damaged, collapsed or washed away.…”

Section: Synthetic Aperture Radar Images As Source Datamentioning

confidence: 99%

The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment

Sublime

2021

Geosciences

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The Tohoku tsunami was a devastating event that struck North-East Japan in 2011 and remained in the memory of people worldwide. The amount of devastation was so great that it took years to achieve a proper assessment of the economical and structural damage, with the consequences still being felt today. However, this tsunami was also one of the first observed from the sky by modern satellites and aircrafts, thus providing a unique opportunity to exploit these data and train artificial intelligence methods that could help to better handle the aftermath of similar disasters in the future. This paper provides a review of how artificial intelligence methods applied to case studies about the Tohoku tsunami have evolved since 2011. We focus on more than 15 studies that are compared and evaluated in terms of the data they require, the methods used, their degree of automation, their metric performances, and their strengths and weaknesses.

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Section: Synthetic Aperture Radar Images As Source Datamentioning

confidence: 99%

The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment

Sublime

2021

Geosciences

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“…For the diagonal sub-band image, we employ an IGF that is based on a new edge-aware weighting method to preserve a low original signal and suppress the noise signal using this new edge-aware weighting method. The approximate sub-band image contains significant components of the image and is less affected by the noise [44]; however, the noise exists in the approximate sub-band image. The GF [45] is employed to reduce the speckle noise and preserve the edges in the approximate sub-band image.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Finally, we obtain the despeckled image. of the image and is less affected by the noise [44]; however, the noise exists in the approximate subband image. The GF [45] is employed to reduce the speckle noise and preserve the edges in the approximate sub-band image.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform

Choi

Jeong

2019

Remote Sensing

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Synthetic aperture radar (SAR) images map Earth’s surface at high resolution, regardless of the weather conditions or sunshine phenomena. Therefore, SAR images have applications in various fields. Speckle noise, which has the characteristic of multiplicative noise, degrades the image quality of SAR images, which causes information loss. This study proposes a speckle noise reduction algorithm while using the speckle reducing anisotropic diffusion (SRAD) filter, discrete wavelet transform (DWT), soft threshold, improved guided filter (IGF), and guided filter (GF), with the aim of removing speckle noise. First, the SRAD filter is applied to the SAR images, and a logarithmic transform is used to convert multiplicative noise in the resulting SRAD image into additive noise. A two-level DWT is used to divide the resulting SRAD image into one low-frequency and six high-frequency sub-band images. To remove the additive noise and preserve edge information, horizontal and vertical sub-band images employ the soft threshold; the diagonal sub-band images employ the IGF; while, the low- frequency sub-band image removes additive noise using the GF. The experiments used both standard and real SAR images. The experimental results reveal that the proposed method, in comparison to state-of-the art methods, obtains excellent speckle noise removal, while preserving the edges and maintaining low computational complexity.

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“…Though these changes may seem trivial, now ALM can be used to solve upper problem Eq. (15). We use L 2 penalization for constraints w = z, p = ∇u, q = ∇ • ( n) and n = m. For…”

Section: Proposed Model a A Brief Review Of Strictly Convex So-mentioning

confidence: 99%

Speckle Noise Removal Convex Method Using Higher-Order Curvature Variation

Huang

Pan

et al. 2019

IEEE Access

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In order to remove speckle noise while preserving image features, a novel variational model for image restoration based on total curvature is proposed in this paper. Due to the characteristics of nonlinear, non-convex, and non-smooth, the proposed variational model is transformed into an alternating optimization problem through introducing a series of auxiliary variables and using the alternating direction method of multipliers. In each loop of optimization, the Fast Fourier Transform is employed to solve the involved partial differential equations numerically, and the generalized soft thresholding formula is used to solve the involved algebra equations analytically. Moreover, the projection method is applied to fulfill some related inequality constraints simply for different sub-problems, thus the computational efficiency is improved totally. The numerous experiments on synthetic and real cases are implemented to demonstrate the advantages of the proposed model in image edge and corner preserving, and to indicate the high computation efficiency of the designed algorithm through comparison with other fast algorithms. INDEX TERMS Speckle noise, curvature-dependent, alternating direction method of multipliers, discrete Fourier transform.

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Speckle noise removal in SAR images using Multi-Objective PSO (MOPSO) algorithm

Cited by 39 publications

References 35 publications

The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment

The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment

Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform

Speckle Noise Removal Convex Method Using Higher-Order Curvature Variation

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