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Impact of SAR data compression on SAR data applications


The satellite images used for a monitoring and analysing of the Earth have been processed over past few decades. The images contain a large amount of data. To reduce amount of the data and store the data or images, the compression methods are applied. In this research project the image compression and extraction of data from satellite images will be researched. The compression method for satellite images will be developed and incorporated with a method for extraction of data. First, the method for extraction of data from Synthetic Aperture Radar (SAR) images will be researched. The methods for estimation of area of interests will be tested and researched. The method based on Bayesian statistics will be implemented for estimation of the distribution of the particular area of interest. Once the object or objects will be isolated and defined the data compression will be applied. The compression method will be object oriented that means that the object of interest will be coded separately from the scene and the both; scene and object will be accessible separately. The SAR image will be transformed using a wavelet transformation and coded using a compression method. The methods for object extraction will be performed in the wavelet domain on the approximation sub band and on the other subbands. The object in the scene will be isolated from the rest of the scene using a method that connects neighbour the pixels with most likely distribution. The goal will not be only isolating an object from the scene, but the method should take in to account the ability of efficient coding of the isolated object. The fuzzy context based coding and the SFQ method will be used for quartering and coding of the survived coefficients. The quintile strategies will be researched to preserve or neglect the details of the selected object. The end user will define the amount of loss of details.

Call for proposal

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Funding Scheme

EIF - Marie Curie actions-Intra-European Fellowships
Leaflet | Map data © OpenStreetMap contributors, Credit: EC-GISCO, © EuroGeographics for the administrative boundaries


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