Abstract
In previous papers we’ve shown how a well known data compression algorithm called Entropy-constrained Vector Quantization (ECVQ; [3]) can be modified to reduce the size and complexity of very large, satellite data sets. In this paper, we discuss how to visualize and understand the content of such reduced data sets. We developed a Java tool to facilitate this using simple multivariate visualization, and interactively performing further data reduction on user selected spatial subsets. This enables analysts to compare reduced representations of the data for different regions and varying spatial resolutions. The ultimate aim is to explain physically observed differences, trends, patterns and anomolies in the data.
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References
Braverman Amy, Fetzer Eric, Eidering Annmarie, Nittel Silvia, Leung Kelvin (2003). Semi-streaming quantization for remote sensing data. Journal of Computational and Graphical Statistics 12, 4, 759–780.
Braverman Amy (2002). Compressing massive geophysical datasets using vector quantization. Journal of Computational and Graphical Statistics 11, 1, 44–62.
Chou P.A., Lookabaugh T., Gray R.M. (1989). Entropy-constrained vector quantization. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37, 31–42.
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© 2004 Springer-Verlag Berlin Heidelberg
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Braverman, A., Kahn, B. (2004). Visual Data Mining for Quantized Spatial Data. In: Antoch, J. (eds) COMPSTAT 2004 — Proceedings in Computational Statistics. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2656-2_4
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DOI: https://doi.org/10.1007/978-3-7908-2656-2_4
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1554-2
Online ISBN: 978-3-7908-2656-2
eBook Packages: Springer Book Archive