Variational Approach for the Reconstruction of Damaged Optical Satellite Images Through Their Co-Registration with Synthetic Aperture Radar

AbstractSynthetic Aperture Radar (SAR) satellite images are used to monitor Arctic sea ice, with systematic data records dating back to 1991. We propose a semi-supervised classification method that separates open water from sea ice and can utilise ERS-1/2, Envisat ASAR, RADARSAT-2 and Sentinel-1 SAR images. The classification combines automatic segmentation with a manual segment selection stage. The segmentation algorithm requires only the backscatter intensities and incidence angle values as input, therefore can be used to establish a consistent decadal sea ice record. In this study we investigate the sea ice conditions in two Svalbard fjords, Kongsfjorden and Rijpfjorden. Both fjords have a seasonal ice cover, though Rijpfjorden has a longer sea ice season. The satellite image dataset has weekly to daily records from 2002 until now, and less frequent records between 1991 and 2002. Time overlap between different sensors is investigated to ensure consistency in the reported sea ice cover. The classification results have been compared to high-resolution SAR data as well as in-situ measurements and sea ice maps from Ny-Ålesund. For both fjords the length of the sea ice season has shortened since 2002 and for Kongsfjorden the maximum sea ice coverage is significantly lower after 2006.

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Satellite Radar Imagery for Detection and Monitoring of Geohazards

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120910746 ◽

2020 ◽

Vol 2674 (3) ◽

pp. 283-292

Author(s):

Sumanth V. Byrraju ◽

Dimitris C. Rizos ◽

Yu Qian

Keyword(s):

Moisture Content ◽

Synthetic Aperture Radar ◽

Satellite Images ◽

Satellite Image ◽

Synthetic Aperture ◽

Small Scale ◽

Interferometric Synthetic Aperture Radar ◽

Right Of Way ◽

The Right ◽

Aperture Radar

This paper presents three case studies that were part of a 1-year study that explores the feasibility of using commercially available satellite and other aerial imagery to monitor the right of way of railroads for effects and conditions that could potentially trigger landslides and other geohazards. Two satellite image processing techniques in the Interferometric Synthetic Aperture Radar (InSAR) family have been studied and employed, that is, the Differential Interferometric Synthetic Aperture Radar (DInSAR) and the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR). All satellite images used in this work are in the public domain and the software is open source. Showcase studies have demonstrated that the current satellite technology makes it feasible to monitor the railway right of way for large- and small-scale deformations and changes in the ground moisture content in adequate resolution. The frequency of acquisition of satellite images is adequate for the long-term monitoring of the infrastructure. The satellite analysis results can be superimposed to visual imagery for ease of visual inspection and evaluation. Future work for the development of a monitoring system of the railway right of way needs to focus on verifying the accuracy of the techniques with in situ measurements through conventional means and quantifying the changes of the moisture content.

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Despeckling Algorithms For Removing Noise In Medical Images

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i6.1271 ◽

2021 ◽

Vol 12 (6) ◽

pp. 87-94

Author(s):

D.Devasena Et.al

Keyword(s):

Synthetic Aperture Radar ◽

Satellite Images ◽

Medical Images ◽

Imaging Techniques ◽

Speckle Noise ◽

Synthetic Aperture ◽

Subjective Analysis ◽

Radar Images ◽

Different Types ◽

Aperture Radar

The medical and satellite images are mostly corrupted by a multiplicative granular noise called speckle noise which degrades the quality of the images captured by using medical imaging techniques and also Synthetic Aperture Radar images. It causes difficulties in image interpretation and this is mainly due to back scattered signals from the multiple targets. In medical field, the diagnosis of the tissues, bones and organs takes place by using imaging techniques. By using different imaging techniques, the medical images are captured and used for diagnosis. Different types of filtering techniques are proposed in the literature to remove the speckle noise in medical and satellite images. In this research paper different types of adaptive filters and its modifications are proposed and compared. The filters like modified lee filter, modified Edge Enhanced lee filter, modified fast bilateral filter and Modified Particle Swarm Optimization based despeckling algorithm. The results are verified for both simulated images and real medical images and also for Synthetic Aperture Radar images. The results are compared in terms of both objective and subjective analysis for simulated and real medical images. The simulation is done using MATLAB R2013 and the visual qualities of the images are analyzed for varying noise densities.

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Ship Detection Based on Information Theory and Segmentation from Synthetic Aperture Radar (SAR) Images

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f8465.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 2513-2517

Keyword(s):

Remote Sensing ◽

Synthetic Aperture Radar ◽

Satellite Images ◽

Synthetic Aperture ◽

Morphological Operations ◽

Sar Images ◽

Ship Detection ◽

Radar Images ◽

Vessel Detection ◽

Aperture Radar

Ship detection is a procedure which asserts in fields such as ocean and sea management, vessel detection, marine superintendence, and rein, and also can be applied to exclude extralegal actions. Remote sensing can be utilized as a potential tool for zonular and universal monitoring to attain the forenamed goals. Among the radar images, the precious datum from Synthetic Aperture Radar (SAR) is playing a serious duty in remote sensing. Howsoever, vessel detecting in heterogeneous and strong clutter is still a question in this regard. The letter points to a ship detection scheme for SAR images exploiting a segmentation-based morphological operation using entropy. In the presented scheme, the morphological operations are adopted to intercept the background and foreground in the satellite images. The method was implemented and tested on the homogenous, heterogeneous and strong clutter SAR images and the results are promising and showing that the proposed method can improve the vessel detection from homogenous and heterogeneous and strong clutter satellite images

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Iceberg Detection using Satellite Images

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36782 ◽

2021 ◽

Vol 9 (VII) ◽

pp. 1896-1901

Author(s):

Dr. Ashoka K

Keyword(s):

Neural Networks ◽

Synthetic Aperture Radar ◽

Convolutional Neural Networks ◽

Satellite Images ◽

Ice Sheet ◽

Synthetic Aperture ◽

The Arctic ◽

Ice Shelves ◽

Ice Conditions ◽

Aperture Radar

Chunks of ice present genuine risks for transport route and seaward establishments. Subsequently, there is a huge interest to limit them ideal and over tremendous regions. As a result of their autonomy of overcast cover and sunlight, satellite Synthetic Aperture Radar (SAR) pictures are among the favoured information hotspots for functional ice conditions and ice sheet events. The picture spatial goal for the most part utilized for chunk of ice observing changes between a couple and 100 m. Prepared SAR information are portrayed by dot clamour, which causes a grainy appearance of the pictures making the distinguishing proof of ice shelves amazingly troublesome. The techniques for satellite checking of hazardous ice developments, similar to ice shelves in the Arctic oceans address a danger to the security of route and monetary action on the Arctic rack. Along these lines, here we have thought of a thought of an application which distinguishes the Iceberg pictures utilizing satellite pictures and it is proposed by utilizing Convolutional Neural Networks (CNN) grouping.

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