A New Approach for SAR Image Denoising

Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists of an input layer, several hidden layers, and an output layer. We have simulated MLP with two hidden layer in Matlab. Speckle noises were added to the target SAR image and applied MLP for speckle noise reduction. It is found that speckle noise in SAR images can be reduced by using MLP. We have considered Log-sigmoid, Tan-Sigmoid and Linear Transfer Function for the hidden layers. The MLP network are trained using Gradient descent with momentum back propagation, Resilient back propagation and Levenberg-Marquardt back propagation and comparatively evaluated the performance.

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Indoor Experiments of Bistatic/Multistatic GB-SAR with One-Stationary and One-Moving Antennae

Remote Sensing ◽

10.3390/rs13183733 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3733

Author(s):

Hoonyol Lee ◽

Jihyun Moon

Keyword(s):

Time Domain ◽

Speckle Noise ◽

The Other ◽

Synthetic Aperture ◽

Sar Image ◽

Sar Images ◽

Airborne Sar ◽

Transmitting Antenna ◽

Azimuth Resolution ◽

Ku Band

Ground-based synthetic aperture radar (GB-SAR) is a useful tool to simulate advanced SAR systems with its flexibility on RF system and SAR configuration. This paper reports an indoor experiment of bistatic/multistatic GB-SAR operated in Ku-band with two antennae: one antenna was stationary on the ground and the other was moving along a linear rail. Multiple bistatic GB-SAR images were taken with various stationary antenna positions, and then averaged to simulate a multistatic GB-SAR configuration composed of a moving Tx antenna along a rail and multiple stationary Rx antennae with various viewing angles. This configuration simulates the use of a spaceborne/airborne SAR system as a transmitting antenna and multiple ground-based stationary antennae as receiving antennae to obtain omni-directional scattering images. This SAR geometry with one-stationary and one-moving antennae configuration was analyzed and a time-domain SAR focusing algorithm was adjusted to this geometry. Being stationary for one antenna, the Doppler rate was analyzed to be half of the monostatic case, and the azimuth resolution was doubled. Image quality was enhanced by identifying and reducing azimuth ambiguity. By averaging multiple bistatic images from various stationary antenna positions, a multistatic GB-SAR image was achieved to have better image swath and reduced speckle noise.

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A Fusion Method of Optical Image and SAR Image Based on Dense-UGAN and Gram–Schmidt Transformation

Remote Sensing ◽

10.3390/rs13214274 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4274

Author(s):

Yingying Kong ◽

Fang Hong ◽

Henry Leung ◽

Xiangyang Peng

Keyword(s):

Loss Function ◽

Texture Features ◽

Speckle Noise ◽

Source Image ◽

Fusion Method ◽

Sar Image ◽

Sar Images ◽

Spatial Features ◽

Optical Images ◽

Fusion Methods

To solve the problems such as obvious speckle noise and serious spectral distortion when existing fusion methods are applied to the fusion of optical and SAR images, this paper proposes a fusion method for optical and SAR images based on Dense-UGAN and Gram–Schmidt transformation. Firstly, dense connection with U-shaped network (Dense-UGAN) are used in GAN generator to deepen the network structure and obtain deeper source image information. Secondly, according to the particularity of SAR imaging mechanism, SGLCM loss for preserving SAR texture features and PSNR loss for reducing SAR speckle noise are introduced into the generator loss function. Meanwhile in order to keep more SAR image structure, SSIM loss is introduced to discriminator loss function to make the generated image retain more spatial features. In this way, the generated high-resolution image has both optical contour characteristics and SAR texture characteristics. Finally, the GS transformation of optical and generated image retains the necessary spectral properties. Experimental results show that the proposed method can well preserve the spectral information of optical images and texture information of SAR images, and also reduce the generation of speckle noise at the same time. The metrics are superior to other algorithms that currently perform well.

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Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image

Research Advances in the Integration of Big Data and Smart Computing - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-8737-0.ch016 ◽

2016 ◽

pp. 280-301

Author(s):

Khwairakpam Amitab ◽

Debdatta Kandar ◽

Arnab K. Maji

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Synthetic Aperture Radar ◽

Back Propagation ◽

Speckle Noise ◽

Synthetic Aperture ◽

Sar Image ◽

Multi Layer Perceptron ◽

Sar Images ◽

Artificial Neural

Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists of an input layer, several hidden layers, and an output layer. We have simulated MLP with two hidden layer in Matlab. Speckle noises were added to the target SAR image and applied MLP for speckle noise reduction. It is found that speckle noise in SAR images can be reduced by using MLP. We have considered Log-sigmoid, Tan-Sigmoid and Linear Transfer Function for the hidden layers. The MLP network are trained using Gradient descent with momentum back propagation, Resilient back propagation and Levenberg-Marquardt back propagation and comparatively evaluated the performance.

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SAR Target Configuration Recognition via Product Sparse Representation

Sensors ◽

10.3390/s18103535 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3535

Author(s):

Ming Liu ◽

Shichao Chen ◽

Fugang Lu ◽

Mengdao Xing

Keyword(s):

Sparse Representation ◽

State Of The Art ◽

Speckle Noise ◽

Synthetic Aperture ◽

Sar Image ◽

Product Model ◽

Sar Images ◽

Stationary Target ◽

Target Configuration ◽

Sparse Vector

Sparse representation (SR) has been verified to be an effective tool for pattern recognition. Considering the multiplicative speckle noise in synthetic aperture radar (SAR) images, a product sparse representation (PSR) algorithm is proposed to achieve SAR target configuration recognition. To extract the essential characteristics of SAR images, the product model is utilized to describe SAR images. The advantages of sparse representation and the product model are combined to realize a more accurate sparse representation of the SAR image. Moreover, in order to weaken the influences of the speckle noise on recognition, the speckle noise of SAR images is modeled by the Gamma distribution, and the sparse vector of the SAR image is obtained from q statistical standpoint. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) database. The experimental results validate the effectiveness and robustness of the proposed algorithm, which can achieve higher recognition rates than some of the state-of-the-art algorithms under different circumstances.

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Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis

Remote Sensing ◽

10.3390/rs13173444 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3444

Author(s):

Hao Wang ◽

Zhendong Ding ◽

Xinyi Li ◽

Shiyu Shen ◽

Xiaodong Ye ◽

...

Keyword(s):

Neural Network ◽

Synthetic Aperture Radar ◽

Convolutional Neural Network ◽

Speckle Noise ◽

Activation Function ◽

Google Earth ◽

Synthetic Aperture ◽

Sar Image ◽

Sar Images ◽

Image Despeckling

Synthetic aperture radar (SAR) images are often disturbed by speckle noise, making SAR image interpretation tasks more difficult. Therefore, speckle suppression becomes a pre-processing step. In recent years, approaches based on convolutional neural network (CNN) achieved good results in synthetic aperture radar (SAR) images despeckling. However, these CNN-based SAR images despeckling approaches usually require large computational resources, especially in the case of huge training data. In this paper, we proposed a SAR image despeckling method using a CNN platform with a new learnable spatial activation function, which required significantly fewer network parameters without incurring any degradation in performance over the state-of-the-art despeckling methods. Specifically, we redefined the rectified linear units (ReLU) function by adding a convolutional kernel to obtain the weight map of each pixel, making the activation function learnable. Meanwhile, we designed several experiments to demonstrate the advantages of our method. In total, 400 images from Google Earth comprising various scenes were selected as a training set in addition to 10 Google Earth images including athletic field, buildings, beach, and bridges as a test set, which achieved good despeckling effects in both visual and index results (peak signal to noise ratio (PSNR): 26.37 ± 2.68 and structural similarity index (SSIM): 0.83 ± 0.07 for different speckle noise levels). Extensive experiments were performed on synthetic and real SAR images to demonstrate the effectiveness of the proposed method, which proved to have a superior despeckling effect and higher ENL magnitudes than the existing methods. Our method was applied to coniferous forest, broad-leaved forest, and conifer broad-leaved mixed forest and proved to have a good despeckling effect (PSNR: 23.84 ± 1.09 and SSIM: 0.79 ± 0.02). Our method presents a robust framework inspired by the deep learning technology that realizes the speckle noise suppression for various remote sensing images.

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Application of the method of renormalization with limitation to remote sensing images obtained with synthetic aperture radars.

Journal of Radio Electronics ◽

10.30898/1684-1719.2021.3.4 ◽

2021 ◽

Vol 2021 (3) ◽

Author(s):

A.V. Kokoshkin ◽

Keyword(s):

Remote Sensing ◽

Speckle Noise ◽

Synthetic Aperture ◽

Reference Spectrum ◽

Sar Image ◽

Remote Sensing Images ◽

Sar Images ◽

Synthetic Aperture Radars ◽

Universal Reference ◽

Spectrum Model

This article proposes an application of the method of renormalization with limitation (MRL) to suppress speckle noise in SAR images. This is because the method of renormalization with limitation, by its definition, renormalizes the SAR image spectrum to a universal reference spectrum (URS) model, which is a "good" quality grayscale spectrum model. To increase the overall sharpness of the image, consistently with the MRL, it is proposed to apply the classical Laplacian. This study allows us to conclude that the application of MRL to SAR images can significantly reduce speckle noise.

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SAR Image Despeckling with Adaptive Multiscale Products Based on Directionlet Transform

Mathematical Problems in Engineering ◽

10.1155/2013/197159 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9

Author(s):

Yixiang Lu ◽

Qingwei Gao ◽

Dong Sun ◽

Dexiang Zhang

Keyword(s):

Synthetic Aperture Radar ◽

Speckle Noise ◽

Experimental Results ◽

Synthetic Aperture ◽

Synthetic Image ◽

Sar Image ◽

Sar Images ◽

Coherent Wave ◽

Multiscale Products ◽

Suppress Noise

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise generated by radar coherent wave. In this paper, a new despeckling algorithm based on directionlets using multiscale products is proposed. We first take an anisotropic directionlet transform on the logarithmically transformed SAR images and multiply the coefficients at adjacent scales to enhance the details of image under consideration. Then, different from traditional thresholding methods, a threshold is applied to the multiscale products of the directionlet coefficients to suppress noise. Since the multiplication amplifies the significant features of signal and dilute noise, the proposed method reduces noise effectively while preserving edge structures. Finally, we compare the performance of the proposed algorithm with other despeckling methods applied to synthetic image and real SAR images. Experimental results demonstrate the effectiveness of the proposed method in SAR images despeckling.

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Removing Speckle Noise in Synthetic Aperture Radar Images using Combined Intensity Coherence Vector & Hybrid Filtering

Psychology and Education Journal ◽

10.17762/pae.v58i1.1497 ◽

2021 ◽

Vol 58 (1) ◽

pp. 4289-4295

Author(s):

Dr. D. Suresh Et al.

Keyword(s):

Synthetic Aperture Radar ◽

Speckle Noise ◽

Synthetic Aperture ◽

Sar Image ◽

Signal To Noise ◽

Sar Images ◽

Radar Images ◽

Hybrid Filtering ◽

Edge Based ◽

Aperture Radar

Noise will be unavoidable in image securing practice and denoising is a fundamental advance to recoup the image quality. The image of Synthetic Aperture Radar (SAR) is intrinsically misrepresented in dot noise that happens because of coherent nature of the dispersing wonders. Denoising SAR images target eliminating dot while safeguarding image highlights, for example, surface, edges, and point targets. The blend of nonlocal gathering and changed area filtering has coordinated the cutting edge denoising methods. Notwithstanding, this methodology makes an intense suspicion that image fix itself gives a brilliant guess on the genuine boundary, which prompts predisposition issue transcendently under genuine dot noise. Another impediment is that the for the most part utilized fix pre-determination techniques can't productively avoid the exceptions and harm the edges. The SAR image is infused with spot noise, and afterward edge based marker controlled watershed division is applied to recognize the homogeneous locales in SAR image. For every locale, the local pixels are distinguished by utilizing Intensity Coherence Vector (ICV) and are denoised autonomously by utilizing a half and half filtering, which contains the improved forms of ice, middle and mean channel. The exploratory outcomes show that the proposed strategy outflanks different techniques, for example, fix based filtering, non-nearby methods, wavelets and old style dot channels in wording higher wavelets signal-to-noise and edge conservation proportions relatively.

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Segmentation of SAR Image using Fuzzy C-Means and Filters

Science & Technology Journal ◽

10.22232/stj.2020.08.01.11 ◽

2020 ◽

Vol 8 (1) ◽

pp. 84-90

Author(s):

R. Lalchhanhima ◽

◽

Debdatta Kandar ◽

R. Chawngsangpuii ◽

Vanlalmuansangi Khenglawt ◽

...

Keyword(s):

Clustering Algorithm ◽

State Of The Art ◽

Speckle Noise ◽

Synthetic Aperture Radar Image ◽

Synthetic Aperture ◽

Sar Image ◽

Spatial Filters ◽

Fuzzy C Means ◽

Automatic Clustering ◽

Intensity Information

Fuzzy C-Means is an unsupervised clustering algorithm for the automatic clustering of data. Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore the segmentation process can not directly rely on the intensity information alone but must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use the fuzzy nature of classification for the purpose of unsupervised region segmentation in which FCM is employed. Different features are obtained by filtering of the image by using different spatial filters and are selected for segmentation criteria. The segmentation performance is determined by the accuracy compared with a different state of the art techniques proposed recently.

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