Abstract
Classical image compression algorithms such as discrete cosine transform (DCT), Karhunen-Loeve transform (KLT), and subband decomposition using wavelet filters (SDWF) are well-understood for optical imaging. However, their applications to synthetic aperture radar (SAR) images have not been well-studied. This paper applies DCT, KLT and SDWF to raw SAR images after appropriate preprocessing, and compares the results based on three performance criteria, namely energy gain (E/sub C/), transform coding gain (G/sub T/), and peak-to-peak signal-to-noise ratio (PSNR).
Keywords
Discrete cosine transform
Karhunen–Loève theorem
Synthetic aperture radar
Transform coding
Computer science
Artificial intelligence
Wavelet transform
Image compression
Lapped transform
Preprocessor
Data compression
Computer vision
Radar imaging
Energy (signal processing)
Pattern recognition (psychology)
Mathematics
Wavelet
Image (mathematics)
Image processing
Radar
Telecommunications
Subject Areas
Image and Signal Denoising Methods
·Computer Vision and Pattern Recognition ·Physical Sciences
Mathematical Analysis and Transform Methods
·Applied Mathematics ·Physical Sciences
Advanced Data Compression Techniques
·Computer Vision and Pattern Recognition ·Physical Sciences
Citations by Year
2012: 1
2014: 1
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