scholarly journals Reduction of Spatially Correlated Speckle in Textured SAR Images

2021 ◽  
pp. 319-327
Author(s):  
Oleksii Rubel ◽  
Vladimir Lukin ◽  
Sergiy Krivenko ◽  
Vladimir Pavlikov ◽  
Simeon Zhyla ◽  
...  
Keyword(s):  
Complex Structure ◽  
Noise Removal ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Spatially Correlated ◽  
Sensing Systems ◽  
Remote Sensing Systems ◽  
Non Gaussian ◽  
Synthetic Aperture Radars ◽  
Sentinel 2

Synthetic aperture radars (SARs) provide a lot of images that can be used for numerous applications. A problem with acquired images is that they are corrupted by speckle which is a noise-like phenomenon with multiplicative nature. In addition, speckle is non-Gaussian and it is often spatially correlated. A typical task in SAR image processing is despeckling and many methods have been already proposed. However, most of them do not take noise spatial correlation into account during denoising. In this paper, we show how this can be done in despeckling based on discrete cosine transform. The use of frequency-dependent thresholds leads to sufficient improvement of denoising efficiency in terms of visual quality metrics. Moreover, we consider quite complex structure texture images for which noise removal is usually problematic and can lead to information loss. Comparison to the well-known local statistic Lee and Frost filters, extended DCT-based filter is carried out for different remote sensing systems including Sentinel-1 and Sentinel-2.

scholarly journals Quantitative Assessment for Detection and Monitoring of Coastline Dynamics with Temporal RADARSAT Images

2018 ◽  
Vol 10 (11) ◽  
pp. 1705 ◽  
Author(s):  
Biswajeet Pradhan ◽  
Hossein Rizeei ◽  
Abdinur Abdulle
Keyword(s):  
Noise Removal ◽  
Synthetic Aperture ◽  
Support Vector ◽  
Spectral Difference ◽  
Sar Images ◽  
Thematic Map ◽  
Tropical Regions ◽  
Passive Sensor ◽  
Multiresolution Segmentation ◽  
Salt And Pepper

This study aims to detect coastline changes using temporal synthetic aperture radar (SAR) images for the state of Kelantan, Malaysia. Two active images, namely, RADARSAT-1 captured in 2003 and RADARSAT-2 captured in 2014, were used to monitor such changes. We applied noise removal and edge detection filtering on RADARSAT images for preprocessing to remove salt and pepper distortion. Different segmentation analyses were also applied to the filtered images. Firstly, multiresolution segmentation, maximum spectral difference and chessboard segmentation were performed to separate land pixels from ocean ones. Next, the Taguchi method was used to optimise segmentation parameters. Subsequently, a support vector machine algorithm was applied on the optimised segments to classify shorelines with an accuracy of 98% for both temporal images. Results were validated using a thematic map from the Department of Survey and Mapping of Malaysia. The change detection showed an average difference in the shoreline of 12.5 m between 2003 and 2014. The methods developed in this study demonstrate the ability of active SAR sensors to map and detect shoreline changes, especially during low or high tides in tropical regions where passive sensor imagery is often masked by clouds.

Potential of multisensor assessment using digital image correlation for landslide detection and monitoring

2020 ◽  
Author(s):  
Doris Hermle ◽  
Markus Keuschnig ◽  
Michael Krautblatter
Keyword(s):  
Remote Sensing ◽  
Digital Image Correlation ◽  
Digital Image ◽  
Image Correlation ◽  
Positional Accuracy ◽  
Sensing Systems ◽  
Rapid Changes ◽  
Remote Sensing Systems ◽  
Uav Images ◽  
Sentinel 2

<p>With the combination of diverse remote sensing data, one can estimate the detection capabilities of gravitational mass movement dynamics and behaviour. Recent multispectral satellite sensors such as Sentinel-2, RapidEye and PlanetScope offer unprecedented spatiotemporal resolutions, hence reducing data gaps of alpine meteorological constraints. In addition to this data, very high resolution and accurate UAV images cover a broad range of spatial resolutions. The strengths of these remote sensing systems allow the data compilation of vast, difficult and dangerous to access mountain areas. However, the limitations of the spatiotemporal resolution for (i) pre-event landslide detection, (ii) monitoring of already known mass movements and (iii) the capability to measure rapid changes (e.g.  accelerations) for warnings have not been examined extensively. Thus, there is an important need to understand the potential of multispectral images to detect, monitor, and identify rapid changes prior to landslide events to increase the forecasting window.</p><p>Digital image correlation (DIC), as indispensable tool to measure surface displacements, aids in estimating the fitness of different remote sensing images. Here, we present first results of motion delineation by DIC of the Sattelkar, a high-alpine, deglaciated and debris-laden cirque in the Obersulzbach-valley, Austria. We used comprehensive knowledge of the study site to thoroughly understand DIC motion clusters for verification purposes. We then compared three different DIC software tools, COSI-Corr, DIC‑FFT and IMCORR. They revealed similar results for the three satellite systems in terms of hot spot areas as well as noise. Our findings show large motion inaccuracies for Sentinel-2, RapidEye and PlanetScope images due to spatial resolution, poor image co-registration and changing data quality. In contrast, displacement patterns from the three UAV images (7/2018, 7/2019, 9/2019) demonstrate good positional accuracy as well as data usability for this approach. The inherited noise results from decorrelation due to high velocities suggest using an increased temporal image acquisition for further evaluation.</p><p>Reliable, precise results for landslide detection, their ongoing monitoring and the measurement capability for significant changes are necessary for targeted investigations, precautionary measures and the start of the forecasting window. Multispectral UAV images of high positional accuracy and quality are able to provide dependable relative displacement velocities and have the capability to serve as a reliable tool. On the contrary, satellite images showed delusive results, and we recommend reconsidering their deployment in future applications. The knowledge of the most suitable data in terms of accuracy and processing speed is crucial for landslide identification, monitoring and acceleration threshold detection. At present, our prelimiary findings show the capability to detect and monitor relative and mainly slow changes. The detection of rapid changes lacks due to the accuracy, resolution and revisit time of the investigated remote sensing systems.</p>

scholarly journals SYNTHETIC APERTURE RADAR (SAR) BASED CLASSIFIERS FOR LAND APPLICATIONS IN GERMANY

2016 ◽  
Vol XLI-B1 ◽  
pp. 1187-1193 ◽  
Author(s):  
G. Suresh ◽  
R. Gehrke ◽  
T. Wiatr ◽  
M. Hovenbitzer
Keyword(s):  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Sar Images ◽  
Sentinel 2 ◽  
Aperture Radar

Land cover information is essential for urban planning and for land cover change monitoring. This paper presents an overview of the work conducted at the Federal Agency for Cartography and Geodesy (BKG) with respect to Synthetic Aperture Radar (SAR) based land cover classification. Two land cover classification approaches using SAR images are reported in this paper. The first method involves a rule-based classification using only SAR backscatter intensity while the other method involves supervised classification of a polarimetric composite of the same SAR image. The LBM-DE has been used for training and validation of the SAR classification results. Images acquired from the Sentinel-1a satellite are used for classification and the results have been reported and discussed. The availability of Sentinel-1a images that are weather and daylight independent allows for the creation of a land cover classification system that can be updated and validated periodically, and hence, be used to assist other land cover classification systems that use optical data. With the availability of Sentinel-2 data, land cover classification combining Sentinel-1a and Sentinel-2 images present a path for the future.

scholarly journals An Adaptive Hierarchical Detection Method for Ship Targets in High-Resolution SAR Images

2020 ◽  
Vol 12 (2) ◽  
pp. 303 ◽  
Author(s):  
Yi Liang ◽  
Kun Sun ◽  
Yugui Zeng ◽  
Guofei Li ◽  
Mengdao Xing
Keyword(s):  
Detection Method ◽  
Estimation Method ◽  
Kernel Density ◽  
Image Resolution ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Airborne Sar ◽  
Visual Attention Mechanism ◽  
Synthetic Aperture Radars ◽  
Coarse To Fine

With the improvement of image resolution in synthetic aperture radars (SARs), sea clutter characteristics become more complex, which poses new challenges to traditional ship target detection missions. In this paper, to detect ship targets quickly and efficiently in a complex background, we propose an adaptive hierarchical detection method based on a coarse-to-fine mechanism. This method constructs a new visual attention mechanism to strengthen ship targets and obtain the candidate targets adaptively by the means dichotomy method. On this basis, the precise detection results of the targets are obtained using the speed block kernel density estimation method, which maintains constant false alarm characteristics. Compared with existing methods, the adaptive hierarchical detection method has simple, fast, and accurate characteristics. Experiments based on GF-III satellite and airborne SAR datasets are presented to demonstrate the effectiveness of the proposed method.

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

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.

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.

scholarly journals Object Georeferencing in UAV-Based SAR Terrain Images

2016 ◽  
Vol 23 (1) ◽  
pp. 39-52 ◽  
Author(s):  
Michał Łabowski ◽  
Piotr Kaniewski ◽  
Piotr Serafin ◽  
Bronisław Wajszczyk
Keyword(s):  
Weather Conditions ◽  
Point Of View ◽  
Optical Methods ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Navigation Data ◽  
University Of Technology ◽  
Aerial Vehicle ◽  
Synthetic Aperture Radars ◽  
The Military

Abstract Synthetic aperture radars (SAR) allow to obtain high resolution terrain images comparable with the resolution of optical methods. Radar imaging is independent on the weather conditions and the daylight. The process of analysis of the SAR images consists primarily of identifying of interesting objects. The ability to determine their geographical coordinates can increase usability of the solution from a user point of view. The paper presents a georeferencing method of the radar terrain images. The presented images were obtained from the SAR system installed on board an Unmanned Aerial Vehicle (UAV). The system was developed within a project under acronym WATSAR realized by the Military University of Technology and WB Electronics S.A. The source of the navigation data was an INS/GNSS system integrated by the Kalman filter with a feed-backward correction loop. The paper presents the terrain images obtained during flight tests and results of selected objects georeferencing with an assessment of the accuracy of the method.

scholarly journals SYNTHETIC APERTURE RADAR (SAR) BASED CLASSIFIERS FOR LAND APPLICATIONS IN GERMANY

2016 ◽  
Vol XLI-B1 ◽  
pp. 1187-1193 ◽  
Author(s):  
G. Suresh ◽  
R. Gehrke ◽  
T. Wiatr ◽  
M. Hovenbitzer
Keyword(s):  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Sar Images ◽  
Sentinel 2 ◽  
Aperture Radar

Land cover information is essential for urban planning and for land cover change monitoring. This paper presents an overview of the work conducted at the Federal Agency for Cartography and Geodesy (BKG) with respect to Synthetic Aperture Radar (SAR) based land cover classification. Two land cover classification approaches using SAR images are reported in this paper. The first method involves a rule-based classification using only SAR backscatter intensity while the other method involves supervised classification of a polarimetric composite of the same SAR image. The LBM-DE has been used for training and validation of the SAR classification results. Images acquired from the Sentinel-1a satellite are used for classification and the results have been reported and discussed. The availability of Sentinel-1a images that are weather and daylight independent allows for the creation of a land cover classification system that can be updated and validated periodically, and hence, be used to assist other land cover classification systems that use optical data. With the availability of Sentinel-2 data, land cover classification combining Sentinel-1a and Sentinel-2 images present a path for the future.

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