SAR Image Compression Using Discretized Gaussian Adaptive Model and Generalized Subtractive Normalization

Synthetic aperture radar (SAR) image compression plays an important role in the manipulation of images. However, the existing optical compression methods cannot properly handle SAR compression due to the absence of feature learning and representation for SAR images. In this letter, we propose an end...

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Vydáno v:IEEE geoscience and remote sensing letters Ročník 19; s. 1 - 5
Hlavní autoři: Zhang, Lili, Pan, Tianpeng, Huang, Yufeng, Qu, Lele, Liu, Yuxuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation models
Algorithms
Compression
Discretization
Discretized Gaussian adaptive model
Distribution
Entropy
generalized subtractive normalization (GSN)
Image coding
Image compression
Image manipulation
Image reconstruction
Machine learning
Methods
Radar imaging
Radar polarimetry
Rate-distortion
SAR (radar)
Synthetic aperture radar
synthetic aperture radar (SAR)
ISSN:1545-598X, 1558-0571
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Shrnutí:Synthetic aperture radar (SAR) image compression plays an important role in the manipulation of images. However, the existing optical compression methods cannot properly handle SAR compression due to the absence of feature learning and representation for SAR images. In this letter, we propose an end-to-end trainable model to effectively fit the feature distribution and reduce information dependencies toward SAR image compression. To better parameterize the distribution of latent codes, a discretized Gaussian adaptive model is designed to achieve a flexible entropy process. To further remove the remaining redundancies, generalized subtractive normalization (GSN) is introduced to reduce the statistical dependencies in SAR images. Extensive experiments show that the proposed compression method outperforms the traditional compression methods and learning-based algorithms on both the ICEYE and Sandia datasets.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2022.3213375