SAR despeckling neural network with logarithmic convolutional product model

Yue, Dong-Xiao; Xu, Feng; Jin, Ya‐Qiu

doi:10.1080/01431161.2018.1471539

Cited by 20 publications

(24 citation statements)

References 23 publications

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“…Both models have analytical expressions. The non-parametric approach uses general mathematical models to fit real SAR images, such as the Parzen window method [31], neural networks [18,32,33], and support http://mc.manuscriptcentral.com/grsm IEEE Geoscience and Remote Sensing Magazine vector machines (SVM) [19]. The non-parametric approach is usually elaborate and has no analytical expressions.…”

Section: Figure 3: Development Of Sar Data Statistical Modelsmentioning

confidence: 99%

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Synthetic Aperture Radar Image Statistical Modeling: Part One-Single-Pixel Statistical Models

Yue

Frery

et al. 2021

IEEE Geosci. Remote Sens. Mag.

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Section: Figure 3: Development Of Sar Data Statistical Modelsmentioning

confidence: 99%

“…Eq. (33) represents the PDF of the intensity of scattered field as a function of ( ; ) and . Due to the complex integral, it is difficult to derive the analytical expression when ( ; ) is arbitrary, but its analytical expression can be written for specific probability distributions ( ; ).…”

Section: Non-rayleigh Speckle Modelmentioning

confidence: 99%

Synthetic Aperture Radar Image Statistical Modeling: Part One-Single-Pixel Statistical Models

Yue

Frery

et al. 2021

IEEE Geosci. Remote Sens. Mag.

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“…The size of the input and output of the convolutional layers keeps the same as the despeckling networks designed in [6,25,27], which increases the amount of computation to a certain extent. We reduce the amount of calculation by decreasing the dimension of the feature maps, i.e., adopting the pooling layer.…”

Section: Single Scale Networkmentioning

confidence: 99%

“…Meanwhile, the existence of speckles limits the effectiveness of the application of common optical image processing methods to SAR images [5]. It thus restricts the SAR images to further understanding and interpretation, increasing the difficulty of extracting roads, farmlands, and buildings in the image and the complexity of spatial feature extraction in image registration, and reducing the accuracy of detection and classification of the objects such as vehicles and ships [6]. Speckle suppression is, therefore, an important task in SAR image post-processing.…”

Section: Introductionmentioning

confidence: 99%

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Deep Multi-Scale Recurrent Network for Synthetic Aperture Radar Images Despeckling

et al. 2019

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For the existence of speckles, many standard optical image processing methods, such as classification, segmentation, and registration, are restricted to synthetic aperture radar (SAR) images. In this work, an end-to-end deep multi-scale recurrent network (MSR-net) for SAR image despeckling is proposed. The multi-scale recurrent and weights sharing strategies are introduced to increase network capacity without multiplying the number of weights parameters. A convolutional long short-term memory (convLSTM) unit is embedded to capture useful information and helps with despeckling across scales. Meanwhile, the sub-pixel unit is utilized to improve the network efficiency. Besides, two criteria, edge feature keep ratio (EFKR) and feature point keep ratio (FPKR), are proposed to evaluate the performance of despeckling capacity for SAR, which can assess the retention ability of the despeckling algorithm to edge and feature information more effectively. Experimental results show that our proposed network can remove speckle noise while preserving the edge and texture information of images with low computational costs, especially in the low signal noise ratio scenarios. The peak signal to noise ratio (PSNR) of MSR-net can outperform traditional despeckling methods SAR-BM3D (Block-Matching and 3D filtering) by more than 2 dB for the simulated image. Furthermore, the adaptability of optical image processing methods to real SAR images can be enhanced after despeckling. multiple segments. The incoherent sub-views are then superimposed to obtain the high signal-to-noise ratio (SNR) images [8]. However, multi-look processing reduces the utilization of Doppler bandwidth, resulting in a decrease of the spatial resolution of the imaging results, which cannot meet the requirements of high resolution [9].The filtering methods are mainly divided into three categories: the spatial filtering method, the transform domain filtering method, and the non-local mean filtering method. Median filtering and mean filtering are the earliest spatial filtering methods of traditional digital image processing. Although these two methods can suppress speckles to a certain extent, it leads to image blurring and objects edge information loss. Afterward, Lee filter [10], Frost filter [11], and Kuan filter [12] are designed for speckles suppression of SAR images. Based on the coherent speckle multiplier model, Lee filter selects a fixed local window in the image, assuming that the prior mean and variance can be calculated by the local region [10]. This method has a small amount of computation, but the selection of local window size has a great influence on the result, and the details and edge information of the image may be lost [13]. Frost filter assumes that the SAR image is a stationary random process and coherent spot noise is multiplying noise, and uses the least mean square error criterion to estimate the real image [11]. For the reason that the actual SAR image does not fully meet the hypothesis, the SAR image processed by this method will have blurre...

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