scholarly journals A Modification of Rician Distribution for SAR Image Modelling

2020 ◽  
Author(s):  
Oktay Karakuş ◽  
Ercan E Kuruoglu ◽  
Alin Achim
Keyword(s):  
Statistical Models ◽  
State Of The Art ◽  
Sea Surface ◽  
Superior Performance ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Frequency Bands ◽  
Rician Distribution ◽  
Proposed Model ◽  
Image Modelling

This paper presents a novel statistical model i.e. the Laplace-Rician distribution, for the characterisation of synthetic aperture radar (SAR) images. Since accurate statistical models lead to better results in applications such as target tracking, classification, or despeckling, characterising SAR images of various scenes including urban, sea surface, or agricultural, is essential. The proposed Laplace-Rician model is investigated for SAR images of several frequency bands and various scenes in comparison to state-of-the-art statistical models that include K, Weibull, and Lognormal. The results demonstrate the superior performance and flexibility of the proposed model for all frequency bands and scenes.

scholarly journals HDRANet: Hybrid Dilated Residual Attention Network for SAR Image Despeckling

2019 ◽  
Vol 11 (24) ◽  
pp. 2921 ◽  
Author(s):  
Jingyu Li ◽  
Ying Li ◽  
Yayuan Xiao ◽  
Yunpeng Bai
Keyword(s):  
Network Architecture ◽  
State Of The Art ◽  
Speckle Noise ◽  
Superior Performance ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Attention Network ◽  
Quantitative Metrics ◽  
Proposed Model ◽  
And Performance

In order to remove speckle noise from original synthetic aperture radar (SAR) images effectively and efficiently, this paper proposes a hybrid dilated residual attention network (HDRANet) with residual learning for SAR despeckling. Firstly, HDRANet employs the hybrid dilated convolution (HDC) in lightweight network architecture to enlarge the receptive field and aggregate global information. Then, a simple yet effective attention module, convolutional block attention module (CBAM), is integrated into the proposed model to constitute a residual HDC attention block through skip connection, which further enhances representation power and performance of the model. Extensive experimental results on the synthetic and real SAR images demonstrate the superior performance of HDRANet over the state-of-the-art methods in terms of quantitative metrics and visual quality.

Robust and discriminative dictionary learning for face recognition

2018 ◽  
Vol 16 (02) ◽  
pp. 1840004 ◽  
Author(s):  
Guojun Lin ◽  
Meng Yang ◽  
Linlin Shen ◽  
Mingzhong Yang ◽  
Mei Xie
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Dictionary Learning ◽  
State Of The Art ◽  
Face Image ◽  
Superior Performance ◽  
Face Images ◽  
Proposed Model ◽  
Classification Capability ◽  
Discriminative Dictionary Learning

For face recognition, conventional dictionary learning (DL) methods have some disadvantages. First, face images of the same person vary with facial expressions and pose, illumination and disguises, so it is hard to obtain a robust dictionary for face recognition. Second, they don’t cover important components (e.g., particularity and disturbance) completely, which limit their performance. In the paper, we propose a novel robust and discriminative DL (RDDL) model. The proposed model uses sample diversities of the same face image to learn a robust dictionary, which includes class-specific dictionary atoms and disturbance dictionary atoms. These atoms can well represent the data from different classes. Discriminative regularizations on the dictionary and the representation coefficients are used to exploit discriminative information, which improves effectively the classification capability of the dictionary. The proposed RDDL is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art dictionary learning methods for face recognition.

scholarly journals Incidence angle normalization of Wide Swath SAR data for oceanographic applications

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Konstantinos Topouzelis ◽  
Suman Singha ◽  
Dimitra Kitsiou
Keyword(s):  
Synthetic Aperture Radar ◽  
Cross Section ◽  
Incidence Angle ◽  
Radar Cross Section ◽  
Sea Surface ◽  
Synthetic Aperture ◽  
Progressive Reduction ◽  
Sar Images ◽  
Wide Swath

AbstractA backscattering trend in the range direction of the signal received by Synthetic Aperture Radar (SAR) in Wide Swath (WS) mode results in a progressive reduction of brightness over images from near to far range, which affects the detection and classification of sea surface features on wide swath SAR images. The aim of the present paper is to investigate methods for limiting the issue of Normalized Radar Cross-Section (NRCS or

scholarly journals A Sensitivity Analysis on the Spectral Signatures of Low-Backscattering Sea Areas in Sentinel-1 SAR Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1183 ◽  
Author(s):  
Valeria Corcione ◽  
Andrea Buono ◽  
Ferdinando Nunziata ◽  
Maurizio Migliaccio
Keyword(s):  
Oil Pollution ◽  
Sea Surface ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Spectral Signatures ◽  
Objective Metrics ◽  
Wide Range ◽  
Imaging Mode ◽  
Physical Phenomena ◽  
Wide Swath

Satellite synthetic aperture radar (SAR) is a unique tool to collect measurements over sea surface but the physical interpretation of such data is not always straightforward. Among the different sea targets of interest, low-backscattering areas are often associated to marine oil pollution even if several physical phenomena may also result in low-backscattering patches at sea. In this study, the effects of low-backscattering areas of anthropogenic and natural origin on the azimuth autocorrelation function (AACF) are analyzed using VV-polarized SAR measurements. Two objective metrics are introduced to quantify the deviation of the AACF evaluated over low-backscattering areas with reference to slick-free sea surface. Experiments, undertaken on six Sentinel-1 SAR scenes, collected in Interferometric Wide Swath VV+VH imaging mode over large low-backscattering areas of different origin under low-to-moderate wind conditions (speed ≤ 7 m/s), spanning a wide range of incidence angles (from about 30° up to 46°), demonstrated that the AACF evaluated within low-backscattering sea areas remarkably deviates from the slick-free sea surface one and the largest deviation is observed over oil slicks.

scholarly journals Deep Cascade Generation on Point Sets

2019 ◽  
Author(s):  
Kaiqi Wang ◽  
Ke Chen ◽  
Kui Jia
Keyword(s):  
State Of The Art ◽  
Shape Reconstruction ◽  
The State ◽  
Graph Representation ◽  
Superior Performance ◽  
Point Sets ◽  
Single View ◽  
Proposed Model ◽  
3D Geometry ◽  
Low Dimensional

This paper proposes a deep cascade network to generate 3D geometry of an object on a point cloud, consisting of a set of permutation-insensitive points. Such a surface representation is easy to learn from, but inhibits exploiting rich low-dimensional topological manifolds of the object shape due to lack of geometric connectivity. For benefiting from its simple structure yet utilizing rich neighborhood information across points, this paper proposes a two-stage cascade model on point sets. Specifically, our method adopts the state-of-the-art point set autoencoder to generate a sparsely coarse shape first, and then locally refines it by encoding neighborhood connectivity on a graph representation. An ensemble of sparse refined surface is designed to alleviate the suffering from local minima caused by modeling complex geometric manifolds. Moreover, our model develops a dynamically-weighted loss function for jointly penalizing the generation output of cascade levels at different training stages in a coarse-to-fine manner. Comparative evaluation on the publicly benchmarking ShapeNet dataset demonstrates superior performance of the proposed model to the state-of-the-art methods on both single-view shape reconstruction and shape autoencoding applications.

scholarly journals SAR Target Configuration Recognition via Product Sparse Representation

Sensors ◽  
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.

scholarly journals R-CenterNet+: Anchor-Free Detector for Ship Detection in SAR Images

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5693
Author(s):  
Yuhang Jiang ◽  
Wanwu Li ◽  
Lin Liu
Keyword(s):  
Rapid Development ◽  
Poor Performance ◽  
Synthetic Aperture ◽  
Detection Methods ◽  
Sar Image ◽  
Average Precision ◽  
Sar Images ◽  
Ship Detection ◽  
Proposed Model ◽  
Length Width

In recent years, the rapid development of Deep Learning (DL) has provided a new method for ship detection in Synthetic Aperture Radar (SAR) images. However, there are still four challenges in this task. (1) The ship targets in SAR images are very sparse. A large number of unnecessary anchor boxes may be generated on the feature map when using traditional anchor-based detection models, which could greatly increase the amount of computation and make it difficult to achieve real-time rapid detection. (2) The size of the ship targets in SAR images is relatively small. Most of the detection methods have poor performance on small ships in large scenes. (3) The terrestrial background in SAR images is very complicated. Ship targets are susceptible to interference from complex backgrounds, and there are serious false detections and missed detections. (4) The ship targets in SAR images are characterized by a large aspect ratio, arbitrary direction and dense arrangement. Traditional horizontal box detection can cause non-target areas to interfere with the extraction of ship features, and it is difficult to accurately express the length, width and axial information of ship targets. To solve these problems, we propose an effective lightweight anchor-free detector called R-Centernet+ in the paper. Its features are as follows: the Convolutional Block Attention Module (CBAM) is introduced to the backbone network to improve the focusing ability on small ships; the Foreground Enhance Module (FEM) is used to introduce foreground information to reduce the interference of the complex background; the detection head that can output the ship angle map is designed to realize the rotation detection of ship targets. To verify the validity of the proposed model in this paper, experiments are performed on two public SAR image datasets, i.e., SAR Ship Detection Dataset (SSDD) and AIR-SARShip. The results show that the proposed R-Centernet+ detector can detect both inshore and offshore ships with higher accuracy than traditional models with an average precision of 95.11% on SSDD and 84.89% on AIR-SARShip, and the detection speed is quite fast with 33 frames per second.

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

Sensors ◽  
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.

scholarly journals High-Resolution SAR Image Despeckling Based on Nonlocal Means Filter and Modified AA Model

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Qiao Ke ◽  
Sun Zeng-guo ◽  
Yang Liu ◽  
Wei Wei ◽  
Marcin Woźniak ◽  
...  
Keyword(s):  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Iterative Algorithm ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Sar Images ◽  
Edge Information ◽  
Nonlocal Means ◽  
Proposed Model ◽  
Effective Performance

A new speckle suppression algorithm is proposed for high-resolution synthetic aperture radar (SAR) images. It is based on the nonlocal means (NLM) filter and the modified Aubert and Aujol (AA) model. This method takes the nonlocal Dirichlet function as a linear regularization item, which constructs the weight by measuring the similarity of images. Then, a new despeckling model is introduced by combining the regularization item and the data item of the AA model, and an iterative algorithm is proposed to solve the new model. The experiments show that, compared with the AA model, the proposed model has more effective performance in suppressing speckle; namely, ENL and DCV measures are 21.75% and 4.5% higher, respectively, than for NLM. Moreover, it also has better performance in keeping the edge information.

scholarly journals Characterizing the Statistical Properties of SAR Clutter by Using an Empirical Distribution

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Gui Gao ◽  
Gongtao Shi ◽  
Huanxin Zou ◽  
Shilin Zhou
Keyword(s):  
Synthetic Aperture Radar ◽  
Empirical Model ◽  
Empirical Distribution ◽  
Statistical Properties ◽  
Synthetic Aperture ◽  
Statistical Characteristics ◽  
Sar Images ◽  
Proposed Model ◽  
Wide Range ◽  
Sar Data

The performances on the applications of synthetic aperture radar (SAR) data strongly depend on the statistical characteristics of the pixel amplitudes or intensities. In this paper, a new empirical model, called simplyℋ𝒢o, has been proposed to characterize the statistical properties of SAR clutter data over the wide range of homogeneous, heterogeneous, and extremely heterogeneous returns of terrain classes. A particular case of theℋ𝒢odistribution is the well-known𝒢odistributions. We also derived analytically the estimators of the presentedℋ𝒢omodel by applying the “method of log cumulants” (MoLCs). The performance of the proposed model is verified by using some measured SAR images.

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