Abstract
Synthetic Aperture Radar (SAR) image compression is important in image transmission and archiving. We present a new algorithm for SAR image compression based on projection pursuit neural networks. At first, we segment an SAR image into regions of different sizes based on mean value in each region and then constructing a distinct code for each block by using the projection pursuit neural networks. The process is stopped when the desired error threshold is achieved. The experimental results show that excellent performance can be achieved for typical SAR images with no significant distortion introduced by image compression.
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© 2005 Springer-Verlag Berlin Heidelberg
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Ji, J., Tian, Z., Lin, W., Ju, Y. (2005). The SAR Image Compression with Projection Pursuit Neural Networks. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_117
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DOI: https://doi.org/10.1007/11427445_117
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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