Hyper-Spectral Satellite Image Compression
by
Alex Cho
Hyper-spectral images consist of huge amounts of data divided into
224 separate bands. As in any situation
that requires storage and transport of such data, efficient compression
techniques are needed. We have explored various techniques that
will save bandwidth with acceptable fidelity
trade-offs. I will present one of the
techniques that we have researched so far.
The major component of this technique involves predicting one band of
the image from
another which increases the efficiency of current compression algorithms
by a noticable factor.
Unlike most situations, these images are compressed in one location,
namely the satellite, and decompressed in another place, the Earth.
The prediction ordering must be synchronized between both places
while preserving the efficiency of compression. We used the minimum
spanning tree algorithm for directed graphs to find the optimal ordering
in which to compress the bands. I will present the algorithm in this talk.
Advised by Richard Ladner
MGH 228
Monday, February 3, 2003
3:30 - 4:20 pm