Fri, 05 Mar 2010

10:00 - 13:00
DH 3rd floor SR

Compression of Synthetic Aperture Radar Images

Ralph Brownie and Andy Stove
(Thales UK)
Abstract

Synthetic Aperture Radars (SARs) produce high resolution images over large areas at high data rates. An aircraft flying at 100m/s can easily image an area at a rate of 1square kilometre per second at a resolution of 0.3x0.3m, i.e. 10Mpixels/sec with a dynamic range of 60-80dB (10-13bits). Unlike optical images, the SAR image is also coherent and this coherence can be used to detect changes in the terrain from one image to another, for example to detect the distortions in the ground surface which precede volcanic eruptions.

It is clearly very desirable to be able to compress these images before they are relayed from one place to another, most particularly down to the ground from the aircraft in which they are gathered.

Conventional image compression techniques superficially work well with SAR images, for example JPEG 2000 was created for the compression of traditional photographic images and optimised on that basis. However there is conventional wisdom that SAR data is generally much less correlated in nature and therefore unlikely to achieve the same compression ratios using the same coding schemes unless significant information is lost.

Features which typically need to be preserved in SAR images are:

o texture to identify different types of terrain

o boundaries between different types of terrain

o anomalies, such as military vehicles in the middle of a field, which may be of tactical importance and

o the fine details of the pixels on a military target so that it might be recognised.

The talk will describe how Synthetic Aperture Radar images are formed and the features of them which make the requirements for compression algorithms different from electro-optical images and the properties of wavelets which may make them appropriate for addressing this problem. It will also discuss what is currently known about the compression of radar images in general.

Fri, 04 Dec 2009 16:30 -
Sat, 05 Dec 2009 17:00
DH 3rd floor SR

Clustering recipes: new flavours of kernel and spectral methods

Ornella Cominetti
(University of Oxford)
Abstract
Soft (fuzzy) clustering techniques are often used in the study of high-dimensional datasets, such as microarray and other high-throughput bioinformatics data. The most widely used method is Fuzzy C-means algorithm (FCM), but it can present difficulties when dealing with nonlinear clusters. In this talk, we will overview and compare different clustering methods. We will introduce DifFUZZY, a novel spectral fuzzy clustering algorithm applicable to a larger class of clustering problems than FCM. This method is better at handling datasets that are curved, elongated or those which contain clusters of different dispersion. We will present examples of datasets (synthetic and real) for which this method outperforms other frequently used algorithms
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