scholarly journals A Deep Vector Quantization Clustering Method for Polarimetric SAR Images

2021 ◽  
Vol 13 (11) ◽  
pp. 2127
Author(s):  
Yixin Zuo ◽  
Jiayi Guo ◽  
Yueting Zhang ◽  
Bin Lei ◽  
Yuxin Hu ◽  
...  
Keyword(s):  
Vector Quantization ◽  
Visual Quality ◽  
Synthetic Aperture ◽  
Polarization Data ◽  
Sar Images ◽  
Polarimetric Synthetic Aperture Radar ◽  
Qualitative And Quantitative ◽  
Linear Dependency ◽  
Proposed Model ◽  
Convolutional Autoencoder

Convolutional Neural Network (CNN) models are widely used in supervised Polarimetric Synthetic Aperture Radar (PolSAR) image classification. They are powerful tools to capture the non-linear dependency between adjacent pixels and outperform traditional methods on various benchmarks. On the contrary, research works investigating unsupervised PolSAR classification are quite rare, because most CNN models need to be trained with labeled data. In this paper, we propose a completely unsupervised model by fusing the Convolutional Autoencoder (CAE) with Vector Quantization (VQ). An auxiliary Gaussian smoothing loss is adopted for better semantic consistency in the output classification map. Qualitative and quantitative experiments are carried out on satellite and airborne full polarization data (RadarSat2/E-SAR, AIRSAR). The proposed model achieves 91.87%, 83.58% and 96.93% overall accuracy (OA) on the three datasets, which are much higher than the traditional H/alpha-Wishart method, and it exhibits better visual quality as well.

scholarly journals ANALYSIS OF SCATTERING COMPONENTS FROM FULLY POLARIMETRIC SAR IMAGES FOR IMPROVING ACCURACIES OF URBAN DENSITY ESTIMATION

2016 ◽  
Vol III-7 ◽  
pp. 219-226
Author(s):  
J. Susaki
Keyword(s):  
Normal Distribution ◽  
Urban Areas ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Polarimetric Synthetic Aperture Radar ◽  
Urban Density ◽  
Concrete Blocks ◽  
Image Pixels

In this paper, we analyze probability density functions (PDFs) of scatterings derived from fully polarimetric synthetic aperture radar (SAR) images for improving the accuracies of estimated urban density. We have reported a method for estimating urban density that uses an index <i>T</i><sub><i>v</i>+<i>c</i></sub> obtained by normalizing the sum of volume and helix scatterings <i>P</i><sub><i>v</i>+<i>c</i></sub>. Validation results showed that estimated urban densities have a high correlation with building-to-land ratios (Kajimoto and Susaki, 2013b; Susaki et al., 2014). While the method is found to be effective for estimating urban density, it is not clear why <i>T</i><sub><i>v</i>+<i>c</i></sub> is more effective than indices derived from other scatterings, such as surface or double-bounce scatterings, observed in urban areas. In this research, we focus on PDFs of scatterings derived from fully polarimetric SAR images in terms of scattering normalization. First, we introduce a theoretical PDF that assumes that image pixels have scatterers showing random backscattering. We then generate PDFs of scatterings derived from observations of concrete blocks with different orientation angles, and from a satellite-based fully polarimetric SAR image. The analysis of the PDFs and the derived statistics reveals that the curves of the PDFs of <i>P</i><sub><i>v</i>+<i>c</i></sub> are the most similar to the normal distribution among all the scatterings derived from fully polarimetric SAR images. It was found that <i>T</i><sub><i>v</i>+<i>c</i></sub> works most effectively because of its similarity to the normal distribution.

scholarly journals Integration of Fine Model-Based Decomposition and Guard Filter for Ship Detection in PolSAR Images

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4295
Author(s):  
Dongsheng Liu ◽  
Ling Han
Keyword(s):  
Figure Of Merit ◽  
Spatial Information ◽  
Synthetic Aperture ◽  
Widespread Application ◽  
Polarimetric Synthetic Aperture Radar ◽  
Ship Detection ◽  
Qualitative And Quantitative ◽  
Decomposition Scheme ◽  
Model Based ◽  
Maritime Surveillance

Ship detection with polarimetric synthetic aperture radar (PolSAR) has gained extensive attention due to its widespread application in maritime surveillance. Nevertheless, designing identifiable features to realize accurate ship detection is still challenging. For this purpose, a fine eight-component model-based decomposition scheme is first presented by incorporating four advanced physical scattering models, thus accurately describing the dominant and local structure scattering of ships. Through analyzing the exclusive scattering mechanisms of ships, a discriminative ship detection feature is then constructed from the derived contributions of eight kinds of scattering components. Combined with a spatial information-based guard filter, the efficacy of the feature is further amplified and thus a ship detector is proposed which fulfills the final ship detection. Several qualitative and quantitative experiments are conducted on real PolSAR data and the results demonstrate that the proposed method reaches the highest figure-of-merit (FoM) factor of 0.96, which outperforms the comparative methods in ship detection.

scholarly journals Unsupervised Classification Method for Polarimetric Synthetic Aperture Radar Imagery Based on Yamaguchi Four-Component Decomposition Model

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Sheng Sun ◽  
Renfeng Liu ◽  
Wen Wen
Keyword(s):  
Unsupervised Classification ◽  
Component Model ◽  
Synthetic Aperture ◽  
Polarimetric Synthetic Aperture Radar ◽  
Qualitative And Quantitative ◽  
Four Component Model ◽  
Component Decomposition ◽  
Distance Model ◽  
Radar Imagery ◽  
Synthetic Aperture Radar Imagery

For improving the accuracy of unsupervised classification based on scattering models, the four-component Yamaguchi model is introduced, which is an improved version of the best-known three-component Freeman model. Therewith, the four-component model is combined with the Wishart distance model. The new proposed algorithm of clustering is rolled out thereafter and the procedure of this new method is listed. In experiments, seven areas of various homogeneities are singled out from the Flevoland sample image in AIRSAR dataset. Qualitative and quantitative experiments are performed for a comparative study. It can be easily seen that the resolution and details are remarkably upgraded by the new proposed method. The accuracy of classification in homogeneous areas has also increased significantly by adopting the new iterative algorithm.

scholarly journals ANALYSIS OF SCATTERING COMPONENTS FROM FULLY POLARIMETRIC SAR IMAGES FOR IMPROVING ACCURACIES OF URBAN DENSITY ESTIMATION

2016 ◽  
Vol III-7 ◽  
pp. 219-226
Author(s):  
J. Susaki
Keyword(s):  
Normal Distribution ◽  
Urban Areas ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Polarimetric Synthetic Aperture Radar ◽  
Urban Density ◽  
Concrete Blocks ◽  
Image Pixels

In this paper, we analyze probability density functions (PDFs) of scatterings derived from fully polarimetric synthetic aperture radar (SAR) images for improving the accuracies of estimated urban density. We have reported a method for estimating urban density that uses an index &lt;i&gt;T&lt;/i&gt;&lt;sub&gt;&lt;i&gt;v&lt;/i&gt;+&lt;i&gt;c&lt;/i&gt;&lt;/sub&gt; obtained by normalizing the sum of volume and helix scatterings &lt;i&gt;P&lt;/i&gt;&lt;sub&gt;&lt;i&gt;v&lt;/i&gt;+&lt;i&gt;c&lt;/i&gt;&lt;/sub&gt;. Validation results showed that estimated urban densities have a high correlation with building-to-land ratios (Kajimoto and Susaki, 2013b; Susaki et al., 2014). While the method is found to be effective for estimating urban density, it is not clear why &lt;i&gt;T&lt;/i&gt;&lt;sub&gt;&lt;i&gt;v&lt;/i&gt;+&lt;i&gt;c&lt;/i&gt;&lt;/sub&gt; is more effective than indices derived from other scatterings, such as surface or double-bounce scatterings, observed in urban areas. In this research, we focus on PDFs of scatterings derived from fully polarimetric SAR images in terms of scattering normalization. First, we introduce a theoretical PDF that assumes that image pixels have scatterers showing random backscattering. We then generate PDFs of scatterings derived from observations of concrete blocks with different orientation angles, and from a satellite-based fully polarimetric SAR image. The analysis of the PDFs and the derived statistics reveals that the curves of the PDFs of &lt;i&gt;P&lt;/i&gt;&lt;sub&gt;&lt;i&gt;v&lt;/i&gt;+&lt;i&gt;c&lt;/i&gt;&lt;/sub&gt; are the most similar to the normal distribution among all the scatterings derived from fully polarimetric SAR images. It was found that &lt;i&gt;T&lt;/i&gt;&lt;sub&gt;&lt;i&gt;v&lt;/i&gt;+&lt;i&gt;c&lt;/i&gt;&lt;/sub&gt; works most effectively because of its similarity to the normal distribution.

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 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 Novel Techniques for Built-Up Area Extraction from Polarimetric SAR Images

2020 ◽  
Author(s):  
D Ratha ◽  
P Gamba ◽  
A Bhattacharya ◽  
Alejandro Frery
Keyword(s):  
Remote Sensing ◽  
Unit Sphere ◽  
Geodesic Distance ◽  
Synthetic Aperture ◽  
Extraction Techniques ◽  
Remote Sensing Images ◽  
Sar Images ◽  
Polarimetric Synthetic Aperture Radar ◽  
Urban Scenes ◽  
L Band

© 2004-2012 IEEE. Built-up (BU) area extraction from remote sensing images is important to monitor and manage urbanization and industrialization. In this letter, we propose two BU area extraction techniques based on the analysis of fully polarimetric synthetic aperture radar (PolSAR) data. Both methods exploit the geodesic distance on the unit sphere in the space of Kennaugh matrices. The first method is based on the three dominant scattering types in the scene and compares them with scattering models; if any of them matches with BU type elementary scattering models, then the pixel is said to belong to a BU area. The second method is based on a novel PolSAR BU index (RBUI) composed by considering scattering mechanisms from BU structures. The two proposed techniques are validated on two different urban scenes, one acquired at C-band by RADARSAT-2 and other at L-band by ALOS-2 SAR sensors.

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.

Prediction of Lee filter performance for Sentinel-1 SAR images

2020 ◽  
Vol 2020 (9) ◽  
pp. 371-1-371-7
Author(s):  
Oleksii Rubel ◽  
Vladimir Lukin ◽  
Andrii Rubel ◽  
Karen Egiazarian
Keyword(s):  
Neural Network ◽  
Complex Structure ◽  
Remote Sensing Data ◽  
Visual Quality ◽  
Synthetic Aperture ◽  
Spectral Features ◽  
Sar Images ◽  
Filter Performance ◽  
Efficient Processing ◽  
Trained Neural Network

Synthetic aperture radar (SAR) images are corrupted by a specific noise-like phenomenon called speckle that prevents efficient processing of remote sensing data. There are many denoising methods already proposed including well known (local statistic) Lee filter. Its performance in terms of different criteria depends on several factors including image complexity where it sometimes occurs useless to process complex structure images (containing texture regions). We show that performance of the Lee filter can be predicted before starting image filtering and which can be done faster than the filtering itself. For this purpose, we propose to apply a trained neural network that employs analysis of image statistics and spectral features in a limited number of scanning windows. We show that many metrics including visual quality metrics can be predicted for SAR images acquired by Sentinel-1 sensor recently put into operation.

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