SAR Target Configuration Recognition via Product Sparse Representation

Ming Liu; Shichao Chen; Fugang Lu; Mengdao Xing

doi:10.3390/s18103535

Product Dictionary Learning-Based SAR Target Configuration Recognition

International Journal of Antennas and Propagation ◽

10.1155/2020/9704829 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Ming Liu ◽

Shichao Chen ◽

Fugang Lu ◽

Junsheng Liu

Keyword(s):

Synthetic Aperture Radar ◽

Sparse Representation ◽

Dictionary Learning ◽

Synthetic Aperture ◽

Sar Image ◽

Product Model ◽

Sar Images ◽

Stationary Target ◽

Target Configuration ◽

Key Factor

Dictionary construction is a key factor for the sparse representation- (SR-) based algorithms. It has been verified that the learned dictionaries are more effective than the predefined ones. In this paper, we propose a product dictionary learning (PDL) algorithm to achieve synthetic aperture radar (SAR) target configuration recognition. The proposed algorithm obtains the dictionaries from a statistical standpoint to enhance the robustness of the proposed algorithm to noise. And, taking the inevitable multiplicative speckle in SAR images into account, the proposed algorithm employs the product model to describe SAR images. A more accurate description of the SAR image results in higher recognition rates. The accuracy and robustness of the proposed algorithm are validated by the moving and stationary target acquisition and recognition (MSTAR) database.

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.

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

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch028 ◽

2020 ◽

pp. 489-510

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.

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.

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.

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.

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.

A Concurrent and Hierarchy Target Learning Architecture for Classification in SAR Application

Sensors ◽

10.3390/s18103218 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3218

Author(s):

Mohamed Touafria ◽

Qiang Yang

Keyword(s):

State Of The Art ◽

Automatic Target Recognition ◽

Image Features ◽

Training Data ◽

Synthetic Aperture ◽

Learning Networks ◽

Sar Images ◽

Stationary Target ◽

Fully Connected ◽

New Framework

This article discusses the issue of Automatic Target Recognition (ATR) on Synthetic Aperture Radar (SAR) images. Through learning the hierarchy of features automatically from a massive amount of training data, learning networks such as Convolutional Neural Networks (CNN) has recently achieved state-of-the-art results in many tasks. To extract better features about SAR targets, and to obtain better accuracies, a new framework is proposed: First, three CNN models based on different convolution and pooling kernel sizes are proposed. Second, they are applied simultaneously on the SAR images to generate image features via extracting CNN features from different layers in two scenarios. In the first scenario, the activation vectors obtained from fully connected layers are considered as the final image features; in the second scenario, dense features are extracted from the last convolutional layer and then encoded into global image features through one of the commonly used feature coding approaches, which is Fisher Vectors (FVs). Finally, different combination and fusion approaches between the two sets of experiments are considered to construct the final representation of the SAR images for final classification. Extensive experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset are conducted. Experimental results prove the capability of the proposed method, as compared to several state-of-the-art methods.

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.

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.